[1,2]Darius Deubert

1]Institute for Control Engineering of Machine Tools and Manufacturing Units
(ISW), University of Stuttgart, Seidenstraße 36, 70174 Stuttgart, Germany

2] Bosch Rexroth AG, Bgm.-Dr.-Nebel-Straße 2, 97816 Lohr am Main, Germany

# Online Simulation at Machine Level: A Systematic Review

<EMAIL_ADDRESS>Lars Klingel<EMAIL_ADDRESS>Andreas Selig<EMAIL_ADDRESS>[  [

###### Abstract

The importance of simulation at machine level in industrial environments is
steadily increasing especially in the design and commissioning phase. Using
models during the operation phase together with the real machine or plant is
referred to as online simulation. Online simulation is used for system
monitoring, predictive analyses, decision support or online optimization and
therefore has various advantages and a wide field of applications. This paper
has the aim to characterize online simulation at machine level in industrial
automation focusing on key technologies and common applications. Therefore, a
set of 60 relevant publications, which are focusing on this subject, is found
by database search, expert consultation, and snowballing. As key technological
aspects, the used model types, interfaces and platforms, and the aspects of
initialization and synchronization are further investigated. The results are
interpreted and limitations, knowledge gaps and future prospects are
discussed. The potential of online simulation at machine level especially
arises due to the increasing availability of component and machine models from
the design and commissioning phase, which can be reused for online simulation.
Remaining challenges are identified concerning implementation, simulation
platforms, model maintenance and especially in the field of synchronization.

###### keywords:

Online simulation, industrial automation, digital twin, real-time simulation

##  1 Introduction

Modern production plants are complex because of the increasing variety of
products with various characteristics, while the number of human operators is
decreasing. To maintain today’s production plants efficiently and to make
optimal decisions, technical experts and autonomous intelligent systems need
to have access to the exact plant state remotely. For this, it is necessary to
provide the internal state of the plant, its machines and its components as
precisely as possible and to be able to predict the plant behavior in near
future. This problem is inter alia solved by online simulation at machine
level. [48]

In the past, dynamic simulation based on first principle models (FPMs) were
used offline in the system design phase, for testing and validation, and for
process planning. Due to advances in industrial communication technology,
reduction of hardware costs and development towards flexible, automated and
highly connected production environments, online simulation became more and
more popular in the recent years. [60]

Online simulation means, that a process or system behavior is simulated during
the system operation and the simulation is directly coupled with the real
system to consider the same system state and real-time inputs that affect the
real system [14]. Therefore, it is somehow connected to the real system, which
could be a mechatronic component, a machine, a process or even a whole
production plant or facility.

Online simulation has multiple applications and advantages [51, 14, 54, 17]:

  * •

Determination of quantities that cannot be obtained by measurement directly
(virtual sensing)

  * •

Substitution of expensive sensors

  * •

Accessibility and easier visualization of system state and internal quantities

  * •

Prediction of system behavior

  * •

Validation of control laws

  * •

Optimization of system operation

  * •

Decision support

  * •

Performance monitoring

  * •

Diagnosis, fault detection and maintenance

In the past it was expensive and time consuming to build and maintain
simulation models [20]. Today, an increasing number of simulation models is
existing from sizing tools, model based engineering and most recently also
from virtual commissioning. This development paves the way for online
simulation, as the expenses for simulation models and platforms are decreasing
and the possibilities of application in a modern Industry 4.0 environment are
extensive.

Early examples of online usage of simulation models are observers, like the
Kalman Filter [32], or Model Predictive Control (MPC). However, these
applications mainly use simplified linear models, which are embedded deeply
into mechatronic control systems. Online simulation in industrial automation
today comprises significantly more. Applications reach from simple
calculations for control tasks to complex simulations of entire manufacturing
facilities. As an online simulation is a digital replica of a machine or plant
which is coupled to the real system in the operation phase, it is also
referred to as simulation-based digital twin [64].

However, there is no consistent understanding of online simulation in
literature. Different theoretical considerations and practical realization
studies of online simulation have been published increasingly in the recent
years, but there is no systematic overview of online simulation at machine
level to the knowledge of the authors.

This article presents the state of the art on online simulation at machine
level through a systematic literature review. It focuses on the technical
aspects, like initialization, synchronization, interfaces and platforms.
Furthermore, exemplary applications of online simulation are presented.
Through the reviewing process, knowledge gaps within the field of online
simulation at machine level are identified, setting directions for future
work.

The remainder of this article is structured as following: In the next section,
the used methods for the systematic review with a focus on literature research
are described. In section 3, the results of the literature research are
presented. This section is structured based on different technical aspects.
Section 4 reviews the publications and summarizes limitations and challenges,
as well as future potential. In the end, a short conclusion is given.

##  2 Methods

This paper follows a systematic approach. In particular, the methodology of
this paper is based on Webster and Whatson [73], Booth et al. [55] and the
PRISMA statement [38].

At first, a clear aim is defined. The central research question is:

> Which approaches, methods and techniques concerning online simulation at
> machine level are existing and how are they applied in industrial practice?

This is further refined by the following questions, which should be answered
by this article:

  * •

Which different methods, approaches and technologies are existing?

  * •

Which different types of models are used for online simulation?

  * •

How are the aspects of initialization and synchronization addressed in
literature?

  * •

Which platforms and interfaces are used for online simulation?

  * •

Which applications exist for online simulation?

Furthermore, in the discussion section, remaining challenges and knowledge
gaps are to be identified.

To find relevant publications, database search, snowballing (using the
references between papers to identify additional papers) and expert
consultation is used. For the database search, the most important terms in the
field of online simulation at machine level are identified. This comprises
different spellings of online simulation and tracking simulation, which is an
important variation of online simulation, and terms to express the industrial
context. By connecting the terms with boolean operators the following advanced
search string for database search results:

> ( TITLE-ABS-KEY ( {online simulation} ) OR TITLE-ABS-KEY ( {on-line
> simulation} ) OR TITLE-ABS-KEY ( {tracking simulation} ) OR TITLE-ABS-KEY (
> {tracking simulator} ) ) AND ( TITLE-ABS-KEY ( manufacturing ) OR TITLE-ABS-
> KEY ( plant ) OR TITLE-ABS-KEY ( "industrial automa*" ) )

Besides scopus, the databases IEEE Xplore and ACM Digital Library are chosen.
This search string yielded 191 results on scopus. With slight variations
concerning the syntax, the search string is applied to IEEE Xplore and ACM
Digital Library and led to 117 results at IEEE Xplore and six results at ACM
Digital Library. The publications were collected in April 2023.

As the scope of this review is online simulation at machine level, especially
in industrial automation, only publications, which focus on this subject
should be included. To narrow down the set of relevant publications to this
field and filter irrelevant papers systematically, the following exclusion
criteria are defined:

  * •

not referring to online simulation: publications, which do not refer to online
simulation at all and therefore are completely irrelevant (affecting 57
publications)

  * •

not in the context of industrial automation and manufacturing: publications
referring to areas other than the mentioned ones, for example power plants or
sewerage control (affecting 83 publications)

  * •

not focusing on online simulation: publications, which emphasize on a subject,
where online simulation is only handled as a side aspect and therefore not
covered in detail (affecting 38 publications)

  * •

Limitation to shop-floor level: publications, which are focusing too much on
shop-floor level online simulation and where the content can hardly be
transferred and applied at machine level (affecting 32 publications)

After removing duplicates, the articles were filtered by the application of
these predefined exclusion criteria, resulting in a corpus of 42 papers. The
huge number of results, which do not refer to online simulation at all, are
due to the ambiguous meaning and use of the terms online and tracking together
with simulation. A major part of the research on online simulation before the
1990s was done at shop floor level, especially to support real-time scheduling
[34].

A lot of these publications are irrelevant for online simulation at machine
level and therefore excluded. Most of the irrelevant papers were excluded
during an initial abstract screening and the remaining papers were excluded
after further analysis.

By including nine articles, which were found by snowballing, and nine
articles, which were found by expert consultation, a final set of 60 relevant
publications is defined. This step ensures that relevant papers, which are not
found by the database search, are included in this review. The overall process
of literature selection is depicted in Figure 1.

Figure 1: Accumulation of relevant papers (based on PRISMA flowchart [38])

The identified set of relevant publications was analyzed comprehensively and
the results concerning the defined research questions are presented in the
next section.

##  3 Results

Using simulation integrated and in parallel to the operation of a real system
is called online simulation. In the past, the main application at machine
level was visualization and monitoring of process quantities, but as mentioned
before, there is a wide variety of use-cases. The main goal of online
simulation at machine level is to support the optimal and undisturbed
operation of the plant.

The following deals with different aspects of online simulation. It summarizes
used model types, interfaces and platforms. Furthermore the aspects of
initialization and synchronization are described and an overview of
applications is given.

###  3.1 Model types

At first, different model types, which are used for online simulation, are
compared. Online simulation models should represent the real system as
accurate as possible, but also be re-configurable [31]. Modeling in general
should need minimal effort, so there has to be found an optimum of accuracy
and complexity.

####  3.1.1 Discrete-event models

In production and logistics, discrete-event simulation (DES) is used for
analyzing, planning and optimizing processes. DES models are based on
instantaneous actions, which occur at a single moment and effect the system in
its state. [18]

DES is suitable for simulating flow of material or the capacity and
utilization of production environments. Therefore, DES models are often used
to determine optimal production flow or waiting times on logistics or shop
floor level. DES is an effective tool for simulating alternative scenarios
predictively [79]. Examples of DES models are Petri nets and final state
machines [28, 8].

On the other hand, continuous models are used for motions, speeds, cycle times
and other processes, which often are real-time dependent and prevalent at
machine level [71]. Nevertheless, to model the business and operation logic or
communication state machines of automation components, DES models are also
necessary at machine level.

####  3.1.2 Hardware-in-the-Loop and Software-in-the-Loop models

In the phase of plant engineering and commissioning, Hardware-in-the-Loop
(HiL) and Software-in-the-Loop (SiL) models are used, for example for tests of
control systems. These models can also be used in the operation phase, for
example as a reference for intended behavior. As the HiL and SiL models are
usually representing the operation logic of components or machines and only
rough physical models, they are used in operation phase mainly for comparing
the logical behavior of a system to its intended behavior [30]. In combination
with precise physical models, they can also be used for detailed monitoring
and visualization of production processes and many other applications.

Figure 2: Structure of HiL online simulation (adapted from [30])

Kain et al. [30] present an exemplary structure of a HiL model for online
plant simulation (see Figure 2). The simulation uses a model consisting of
mechanics, sensors and actuators, as well as process materials. Just like for
HiL testing, the simulation environment is directly connected to the control,
which could be a programmable logic controller (PLC) or a computer numerical
control (CNC) and therefore needs corresponding interfaces [31].

####  3.1.3 Physical and statistical models

Nakaya et al. [52] distinguish rigorous models, which are based on physical
laws or phenomena, and statistical models, which are data-driven and consider
the historical process data. The rigorous models are commonly referred to as
FPMs, which can be defined as models based on fundamental engineering, physics
and chemistry principles [54].

Statistical models are especially useful, if unknown phenomena, which could
not be reconstructed by physical laws or are expensive to identify manually,
have to be considered in the simulation model. Examples for statistical models
are artificial neural networks, look-up tables or generally different types of
regression [5]. While FPMs can be reused easier, statistical models can be set
up faster. The disadvantages of FPMs are that the modeling and initial
parameter tuning is complex and inexplicable phenomena cannot be considered.
Statistical models in contrast have to be maintained and provide less accurate
estimations as long as they are in their learning phase [52]. Additionally,
they are not able to predict unusual behavior [64]. When it comes to model
adaption, data-driven statistical models are often newly learned, while
physical models have to adjust their parameters [62].

Usual structures of statistical models are Multiple Linear Regression (MLR),
Principal Component Regression (PCR), Partial Least Squares (PLS) regression
or Just-In-Time (JIT) models [47]. The latter one is most suitable for
applications with different operation ranges, as other methods tend to over-
fitting. Neural networks also suffer from over-fitting and furthermore are
unintelligible in their meaning [53].

When models are adjusted to a certain operation range to strong, they usually
are worse applicable to other operation ranges. JIT models store historical
data in a database and only consider the relevant dataset for determining a
local model. The relevant dataset is chosen by a similarity factor, for
example correlation, the Euclidean distance or other distance measures [65].
By implementing a threshold, a new model is only built when the relevant
dataset changes significantly. JIT modelling is also called lazy learning or
locally weighted learning.

(a)

(b)

(c)

Figure 3: Possible combinations of physical and statistical models in hybrid
modeling (adapted from [48])

To combine the advantages of both, first principle and statistical models,
hybrid models are used. Generally, three types of combinations are possible
(see Figure 3):

  * •

statistical model as input for the physical model (Figure 3(a))

  * •

statistical model as compensation for the physical model (Figure 3(b))

  * •

statistical model processing the differences between physical model and real
system (Figure 3(c))

Nakabayashi et al. [47] applied a hybrid model consisting of a physical and a
JIT statistical model on an online simulator for a steam reforming plant and
proposed a new Mahalanobis distance-based JIT (MJIT). The Mahalanobis distance
considers the covariance so it is correlation based, but does not need a
hyper-parameter for weighting it relative to the distance measure. Hence, only
the remodeling threshold has to be set. This is an advantage, as these hyper-
parameters have to be set by trial-and-error, so they should be avoided or
work as clear as possible [65].

####  3.1.4 Fluid dynamics modeling

For fluid dynamics, which are necessary for industrial applications like
bottling drinks or painting and casting steel, diverse methods are applicable.
Generally, models are divided into meshed and meshless discretization. The
most popular method using meshed discretization is Reynolds-averaged Navir
Stokes, as it is strongly simplified. For a higher resolution and turbulent
flow, large-eddy-simulation or direct-numerical-simulations are common
alternatives, but require a higher computational power. Meshless methods can
model turbulent flow more easily because of variable discretization. As
meshless methods are scalable, suitable for parallelization and have a high
time step tolerance, they are more suitable for real-time simulation. Examples
are the smoothed particle hydrodynamics (SPH) method, the reproducing Kernel
particle method (RKPM), and the finite point method (FPM). [36]

For online simulation, Krotil et al. [36] define that before each iteration of
model execution, the model is adjusted. They distinguish fluid-specific model
evolution, where sources and sinks are defined, and geometry-specific model
evolution, which is derived from a CAD model.

####  3.1.5 Automatically generated models

Manual modeling in general is laborious and requires system experts. Hence,
efforts are made to generate models automatically. As statistical models are
generated by computation anyways, the main challenge of automatic online
simulation model generation is the generation of underlying first principle
models. For this, information mapping algorithms are utilizing various
information sources to map data into a simulation language. Possible data and
information sources are: [64]

  * •

Initial process design and equipment data sheets

  * •

Piping and instrumentation diagram (P&ID) in process industry

  * •

Automation I/O tables

  * •

3D plant models

  * •

Control application programs

As the real plant behavior may deviate from the automatically generated model,
manual correction, applying an online parameter estimation or using tracking
simulation, where the model parameters are optimized during operation, is
recommended [64]. Reasons for the deviation are that the system is built
different from the design or due to degradation. Santillán Martinez et al.
[64] present a method, where they firstly generate a steady state model from
preliminary process design data, which is then converted to a dynamic
simulation model. Alternatively, the dynamic simulation model can be built
from the P&ID or automation I/O tables and enhanced by further information.
Afterwards, the simulation models are connected to the manufacturing execution
system (MES) and enterprise resource planning (ERP) systems for the operation
phase. The method carries out the following steps:

  1. 1.

Automatic model generation

  2. 2.

Model integration within the plant

  3. 3.

Model optimization

  4. 4.

Online model parameter estimation

Other terms, which are used in this context, are model adaption and model
deployment. In this context, Model adaption is the optimization of models,
which were generated automatically or in another context, like in the design
phase, for the use in online simulation. The model integration within the
plant is also referred to as model deployment.

Automatic model generation is still in its infancy, but is particularly
important for economic efficiency and therefore for the widespread use in the
production industry.

####  3.1.6 Surrogate models

As computing power is limited in mechatronic systems and online simulation at
machine level requires real-time capability, high-fidelity simulation models
are not suitable. To address this, surrogate low-fidelity models are built.

To combine multiple models with different fidelity, multi-model-data-fusion is
used [5]. By learning the fusion process with real measurements as ground
truth, a hybrid of an existing model and a statistical model, which is
represented by the fusion process, results. Bergs and Heizmann [5] present a
generic approach for generating virtual sensors from high-fidelity models in
that way. In particular, low-fidelity models together with learning fusion
operators are used to approximate the results of the high-fidelity simulation.
After a learning phase, the new combined model can be applied for online
simulation, providing more accurate results than the low-fidelity model itself
and being computationally cheaper than the high-fidelity model.

###  3.2 Initialization

One of the most important characteristics of online simulation is that it is
initialized with the actual state of the system [14].

To identify the actual state of a system, the following aspects have to be
fulfilled [19]:

  * •

Clear definition of the data forming the system state

  * •

Availability of that data

  * •

Sufficient quality of that data

  * •

Sufficient update frequency of that data

This section summarizes different initialization strategies and presents a way
of initialization data standardization.

####  3.2.1 Initialization strategies

Online simulation has to be initialized with a state that is as close to the
real system state as possible. Hanisch et al. [21] present two possibilities
for initialization.

The first option is to initialize an online simulation instance from a parent
simulation instance, which is synchronized to the real system. This requires
an enhanced simulation platform, which is on the one hand able to copy a child
simulation from a running simulation instance and on the other hand able to
synchronize the parent simulation with the real system. This option has the
advantage that it is possible to gather the entire system state at one point
of time.

The second option is to initialize simulation by using measurements of sensors
or state information from system components. This requires suitable
connectivity and higher quality of data, but provides less restrictions
regarding simulation platforms, as most of them are able to read and process
initialization data [21]. Especially if the measurement cycles are not
synchronized, a higher measurement frequency enables a more accurate
initialization. However, in some systems a frequent cyclic measurement might
not be possible, so this is not applicable in general. Another common problem
is that there might be relevant information or quantities, which are not
captured by any sensor. The determination of workpieces’ location is often
solved by retrofitting with radio-frequency identification (RFID) technology
[1].

Cardin et al. [10] list another approach, which is a synthesis of the
aforementioned options: Observers use a model of the system to process the
measurements and estimate the current state of the system, which is used for
initialization. Using observers is powerful, as it can be applied nearly
everywhere and also provides the full state at any time. Cardin et al. [12]
emphasize the importance of centralizing plant data using an observer, which:

  * •

gathers the events occurring in the distributed manufacturing environment

  * •

interpolates the behavior of the manufacturing units between the points of
measurement

  * •

makes the system state data available for the whole manufacturing environment

Another hybrid approach is presented by Santillán Martinez et al. [61]. They
perform a rough database-based initialization with initial conditions, which
is then tuned in a validation stage. In the validation sequence, firstly the
simulated state and then the model parameters are adjusted. The offline tuning
process can be repeated iteratively to get closer to the actual system state.
This process is called offline adaption and is followed by online adaption,
which basically is a tracking simulation (see section 3.3). In this phase, the
simulation is synchronized with the real plant. If the discrepancy between
simulation and real plant gets too strong during the online adaption, the
offline adaption can be repeated.

Especially if models do not allow to directly set a certain state, a further
possibility is to initialize the model with a standard state and use a
simulated control system to align the simulated state to the actual state in
an offline simulation [63].

The choice of initialization strategy is depending on the availability and
quality of initialization data [25].

####  3.2.2 Initialization data

As stated before, it is important to clearly define the system state. There
has to be a consistent understanding of which information and data is part of
the system state and how the data has to be interpreted.

To enable interoperability between simulation systems there are various
standards developed within the Simulation Interoperability Standards
Organization (SISO). One of these standards is the core manufacturing
simulation data information model (CMSD-IM), which focuses on interoperability
between simulation and manufacturing applications. The CMSD-IM provides
information models and data structures in form of the Unified Modeling
Language (UML) and the XML schema definition language (XSD). Bergmann et al.
[4] are mapping state information into CMSD-IM classes for the initialization
of simulation models. Specifically, the classes Resource, Part, Job,
JobEffortDescription, Schedule, ScheduleItem and ProcessPlan are used.
Bergmann et al. [4] propose to extend the CMSD-IM by a timing property that
indicates the process of a currently processed job.

Although the concepts refer to shop-floor level, they can be transferred to
the machine level very well. While jobs on shop-floor level are manufacturing
or maintenance operations, on machine level they may be operation steps in a
flexible production environment or steps/states within the execution of a
specific program. An exception are the interfaces, as Bergmann et al. assume
to take the data from an Enterprise Resource Planning (ERP) system or a
Manufacturing Execution System. At machine level, such centralized systems
often do not exist. Nevertheless, the availability of real-time data is
improved due to the industrial Internet-of-Things (IIoT), so that there are
coming up many other possibilities for gathering the machine state.

###  3.3 Synchronization and Tracking Simulation

Synchronization is necessary to ensure that the online simulation can
represent the current state of the real plant [10]. Due to wear,
reconfiguration and malicious manipulation, the plant behavior is changing in
its life cycle [78]. As manual synchronization would be costly and error
prone, there have to be automatic synchronization mechanisms or at least
monitoring systems, which detect deviations for maintenance personnel.

At machine level, this monitoring can be done utilizing the network.
Manufacturing machines typically consist of controllers, which are connected
to actuators and sensors via fieldbuses. Passive capturing only observes the
present data stream in the network, while active capturing sends messages to
gather information about the system state. [78]

(a)

(b)

Figure 4: Differences of standard online simulation (a) and tracking
simulation (b) (adapted from [61])

Besides monitoring, adapting the simulation to be synchronous with the plant
is a major challenge. One way to solve the synchronization problem is by
tracking simulation.

####  3.3.1 Tracking simulation

The concept of a tracking simulator was firstly presented by Nakaya et al.
[49]. They describe it as a dynamic process simulator, which works
simultaneously with a target plant. To ensure accuracy and minimize
discrepancy between the real plant and the simulation, the model parameters
are adjusted. This is done by comparing sensor data with simulation results
and updating the model parameters recursively, in a way that the simulation
output converges to the real system output. A visualization of this principle
is pictured in Figure 4. While basic online simulation does not use any
feedback mechanisms (see Figure 4(a)), tracking simulation processes the
difference between process output and simulated output to update the
simulation model (see Figure 4(b)).

Reasons for diverging behavior between plant and simulation could be different
operating conditions compared to the design base, changing characteristics due
to degradation, or simply inaccurate simulation models [46].

Figure 5: Stages of model adjustment for tracking simulation according to
Santillán Martinez (adapted from [62])

Santillán Martinez et al. [61, 62] distinguish different stages of model
adjustment (see Figure 5). In the model structure update, the model
configuration is changed according to the plant, as the model created in the
design phase could deviate. This step often has to be carried out by experts.
In the model tuning stage, the model parameters are re-estimated. At model
initialization, the obtained system state and suitable parameters are
transferred to the simulation. In the case of tracking simulation, the state
and the model parameters are continuously adjusted further on. The tracking
simulation instance may then be used as parent simulation for several
predictive simulations.

Pantelides et al. [54] also propose three stages of model adaption:

  1. 1.

Offline model validation and parameter estimation

  2. 2.

Offline model-to-plant calibration

  3. 3.

Online calibration

Nevertheless, initial conditions for tracking simulation must be chosen
carefully, as the recursive determination of suitable parameters could take a
long time, if the parameters are inaccurate in the beginning. Hence, the
initial values should be chosen as precise as possible. [50]

####  3.3.2 Model parameter adjustment

Besides common applications and advantages of online simulation, tracking
simulation is useful to determine model parameters [51].

The first approach was to adjust a parameter in a way inspired by a PI
control. For a parameter $\Lambda$, Nakaya et al. [49] determine the
correction $\Delta\Lambda$ with:

$\Delta\Lambda=K_{p}e+K_{i}\int e\;dt\;,$ (1)

where $e$ is the difference between measurement and simulated output and
$K_{p}$ and $K_{i}$ are feedback gains, which define how the feedback effects
the parameter adjustment.

Friman et al. [20] also describe their tracking simulator as PI controller,
whose error to minimize is the difference between simulated and real output
and whose control value is an unknown parameter.

However, PI controllers can only be used, if the sign of the process gain is
known. Otherwise, wrong control gains can increase the simulation error.
Hence, the choice of control gains is not trivial.

Pietilä et al. [57] are using a proportional and derivative part for an error
function. For the parameter update, they are using a predictor based
estimation together with the error function.

An alternative solution is to use a sliding mode controller for the parameter
adjustment. This has the advantage that sliding mode control has a better
disturbance rejection than PI-based controllers. Furthermore, the sliding mode
control needs less information about the system and is robust against
uncertainties. The disadvantages are that a sufficiently small step size and
minimal delay is required. As the sliding mode controller needs to know the
derivatives of system and simulation outputs, an additional sliding mode
observer is necessary in most cases. [60]

Another possibility is to record an output trajectory and solve an
optimization problem to estimate optimal parameter adaptions, but this
approach needs more computing power. The advantage of an optimization
algorithm is that it does not need that much tuning parameters [57].

For the model tuning, Santillán Martinez et al. [62] utilize optimization
algorithms that are applied on historical data. In particular they present two
variants, based on QNSTOP [2], the Levenberg-Marquardt algorithm, and
Broyden’s method [9]. The Levenberg-Marquardt algorithm was also approved by
Fagervik et al. [17]. A problem of optimization algorithms for data
reconciliation is that often analytic values for the Jacobian matrix of the
system are necessary, which is not always given [63]. Another problem is that
scalar optimization algorithms may only find a local optimum.

In general, optimization problems can be solved by enumerative (brute-force),
deterministic and stochastic approaches. Enumerative ones are least efficient
and only applicable to discrete or discretized problems, with a small solution
space. Deterministic approaches often need certain assumptions, so they are
only applicable if these assumptions can be taken. Härle et al. [22] therefore
propose to use an evolutionary approach. As optimization problems need a large
computational effort, they are not suitable for real-time adaption.
Nevertheless, they can be used for periodical adaption of real-time online
simulation. [22]

####  3.3.3 Enhancements of tracking simulation

As the dynamic online adjustment of parameters often is limited, further
mechanisms are necessary for the model adjustment. The tracking simulator by
Nakaya et al. [49, 50] was enhanced in [68], where the simulation is divided
into three parts:

  * •

The mirror model is tracking the quantities of the real system by using
measured values as explained before.

  * •

The identification model is adjusting certain model parameters periodically by
data reconciliation using a least-squares method on a state-space
representation. (also referred to as Dynamic Data Reconciliation technique
[53])

  * •

An additional analysis model is used for predictions, optimization,
performance analysis and controllability improvement.

All parts rely on the same plant model. The mirror model and the
identification model are exchanging information in the form of variables and
parameters [48].

Another enhanced architecture is proposed by Santillán Martinez et al. [63].
They also divide the simulation system into three simulators:

  * •

The online simulator is running in parallel to the real plant. An
initialization manager takes care of the model initialization. During the
tracking simulation, the dynamic estimator adjusts the model parameters to
align the simulation to the real process. The online simulator is also used
for the initialization of child simulations as predictive or optimization
simulators.

  * •

The optimization simulator runs offline model optimization on historical data.
The optimized parameters are fed back to the online simulator.

  * •

The predictive simulator is used to be executed faster than real-time for
forecasting of the process.

In addition to the process model, they use a control system model, as this
enables predictive simulation and a control model is easier to integrate than
a real control. Hence, a replicate of the control application is used in the
same simulation environment as the process model.

####  3.3.4 State synchronization without model adaption

Another approach, which also uses the difference between the simulation output
and real output as feedback, but does not adapt the simulation model directly,
is presented by Zipper [77, 75, 76]. The approach considers black-box
Functional Mockup Interface (FMI) models, so the simulation system does not
know about all model parameters and could not adapt them properly. Instead,
the system input is adapted for the simulation in a way that the difference
between simulation output and real system output is compensated. A
visualization of this principle can be seen in Figure 6. Hence, this approach
is closely related to tracking simulation.

Figure 6: Synchronization approach by Zipper [77]

For the minimization of the simulation discrepancy, an optimization algorithm
over a certain time span is performed. This time span is depending on the
simulation steps needed for a changing input to affect the output. This is
depending on the number and constellation of simulation units in the co-
simulation. As optimization algorithms, Zipper validates L-BFGS and the
derivative-free BOBYQA.

Alternatively to adapting the simulation input, the system’s estimated state
can be handled separately from the simulation result and therefore be updated
by measurements. This approach does also not require model adjustment but
addresses state synchronization only indirectly.

###  3.4 Interfaces and platforms

Predefined interfaces and existing software and hardware platforms are
necessary for online simulation at machine level to establish in industrial
practice.

####  3.4.1 Hardware platforms

Ebner et al. [15] implemented a HiL test and simulation platform. It comprises
flexible hardware, such as a floating point digital signal processor (DSP), a
field programmable gate array (FPGA), a pulse-width modulation (PWM) unit, an
incremental encoder interface and a V/f-sensor interface, and high-speed
communication interfaces. To enable flexibility, expandability, and
reusability, an appropriate operating system was developed. Even though the
main application of the authors was to test control mechanisms for electric
drives, the platform is perfectly suitable for online simulation, as models
can be simulated in real-time and high-speed communication interfaces provide
an appropriate connectivity towards the machine level and supervisory levels.
For modeling, they are using Dymola, which is a Modelica-based tool for
modeling and simulation of mechanical, electrical, thermodynamic, hydraulic,
pneumatic, thermal and control systems. Dymola provides a code export
functionality, which is allowing to convert the model into C-code for
execution on the real-time platform.

Other applications do not utilize real-time targets for simulation. Fagervik
et al. [17] for example are using a personal computer, which is able to handle
multiple input and output signals. However, due to the used operating systems,
simulations on a personal computer are not real-time capable in the most
cases.

####  3.4.2 Software platforms

A general term that is used for simulation platforms in industry is computer-
aided production engineering (CAPE). CAPE tools are able to simulate
manufacturing scenarios with various production resources and control
functions [71]. The advantage of using CAPE tools for online simulation is
that they provide numerous predefined models and, at best, a model of the
plant is already present from the design or commissioning phase. Furthermore,
they already provide common interfaces.

Nakaya et al. [49, 27] are using the dynamic simulation environment OmegaLand
by the Japanese company Omega Simulation Co. [68].

Figure 7: Chemical process simulation with continuous parameter adaption using
Matlab (adapted from [57])

Pietilä et al. [57] are using the Outotec HSC simulator, which is a simulation
tool for chemical processes. They are using it as part of the tracking
simulator and for evaluation. The tracking simulator consists of a parameter
estimator, which is implemented in Matlab, and the HSC simulation model. An
overview of the constellation is shown in Figure 7.

Figure 8: Online Simulation with OPC UA and process historian (adapted from
[62])

Martinez et al. [61, 62, 63] are using the process simulation Software Apros
combined with OPC UA as an interface to the plant. OPC UA is a client-server-
based communication standard, which provides a unified interface for
heterogeneous data sources in Industry 4.0 environments. The communication
structure for tracking simulation with OPC UA is depicted in Figure 8. The OPC
UA historical access is used for the model tuning before model initialization.
As in many cases the time-steps are irregular, the data series has to be
interpolated. This is a disadvantage for simulations with fixed step sizes and
optimization algorithms relying on evenly distributed time series.

Petschnigg et al. [56] realized an interface to robot operating system (ROS).
As a simulation platform, they use CoppeliaSim (formerly V-REP). With an
extending script, path planning for robotic applications is possible. It is
connected to CoppeliaSim with the CoppeliaSim client API. The API client can
control the simulation state, retrieve and edit simulation data, like object
positions, and enqueue planning targets, which are target poses for the tool
center point.

In industrial automation, Modelica models are popular. Modelica is an object-
oriented simulation language, which is used by multiple simulation platforms
and hence, provides many ready-to-use models [15]. Further popular simulation
platforms in industrial automation are ISG-virtuos, machineering iPhysics,
Matlab/Simulink and Siemens tools like Siemens plant simulation. Some of them
come with real-time simulation targets, which can be used for HiL simulation.

To sum up, domain specific simulation platforms were often used and adapted to
the requirements of online simulation. A software platform especially intended
for online simulation does not exist to the knowledge of the authors. However,
many existing simulation platforms already provide suitable interfaces for
online simulation. In future, standards like the FMI will play an important
role, as they enable the co-simulation of various existing component models
that cant set up a machine or plant model.

Some of the reviewed implementations also use programming languages like
C/C++, Java and Python directly. In practice, newly coding component or
machine models with general purpose programming languages is expensive and
therefore not suitable for the most applications, but a way to be more
flexible in research and testing.

####  3.4.3 Cloud services

Depending on the model complexity and the optimization mechanisms, online
simulation might be computationally costly. In this case, cloud computing
services are considered, as they allow scalability and parallelization.
Especially for small and medium-sized companies, third party cloud services
like Amazon Web Services (AWS) and Microsoft Azure are attractive, as they
provide the infrastructure and are paid per use. [23]

Figure 9: Exemplary deployment of DES in AWS cloud environment (adapted from
[23])

Hofmann et al. [23] present an exemplary model for the deployment of a DES
simulation on the AWS cloud (see Figure 9). The data producer could be a real
production system, which collects information with its sensors and triggers
corresponding events. These events are captured by AWS Kinesis, which handles
the data stream and acts as a communication interface. The events are
processed first-in-first-out by AWS Lambda. AWS Lambda updates a Dynamo DB
database, which stores the events. The DES model is also implemented as AWS
Lambda function. According to the occurring events, the state variables are
changed in the Dynamo DB database. The AWS Lambda functions are executed
depending on cell updates in the Dynamo DB. The simulation time is
synchronized with the real system.

####  3.4.4 Interfaces

The synchronization of online simulation to a real system, as well as the
cooperation with other mechatronic or virtual systems require consistent
interfaces. While on shop-floor level human-machine-interfaces to the
simulation are used not only for supervision and monitoring but also for
manually providing system inputs [29], the online simulation at machine level
has to focus especially on machine-to-machine communication.

Machine-to-machine communication is divided into different architectures, such
as service-oriented architectures (SOA), Representational State Transfer
(REST) and message-oriented architectures [45]. In practice, the communication
is realized by standardized Internet-of-Things (IoT) protocols on the one hand
and APIs to proprietary apps and platforms on the other hand [39].

Apart from this, it is also possible to connect the simulation to the
mechatronic system using fieldbuses and Industrial Ethernet protocols.
Industrial fieldbuses are necessary, if the application requires real-time
capability. Zipper et al. [78] for example use the Ethernet-based PROFINET and
map the process data to the corresponding simulation inputs. Test access
points, which are located between the controller and the considered
components, capture the data from the communication network directly. This has
the advantage that the high amount of data is processed close to the process
and does not have to be spread through the factory network or even further
[78].

Cardin et al. [11] implemented a simulation system containing a discrete event
simulation as an observer and initialization basis for predictive online
simulation instances. An OPC server is used to synchronize the event-based
observer simulation to the real system. The OPC server sends events, when
predefined variables are changing. For the initialization of the predictive
online simulation instances a database containing production data is used.

Martinez et al. [63] propose an architecture, where three different simulators
(an online simulator, an optimization simulator, and a predictive simulation)
are connected to a historical data repository, which stores process and
simulation data in a database. For the historical data repository, they use a
Prosys OPC UA historian, which also uses an SQL-based database.

OPC UA has a service-oriented architecture. With OPC UA Pub/Sub, a cyber
physical system can register to a topic as publisher or subscriber and a
broker coordinates the data distribution.

An alternative to OPC UA is Message Queuing Telemetry Transport (MQTT), which
is a message-oriented protocol that offers multiple advantages for
asynchronous data streams. An MQTT broker running on a server manages the
message distribution inside a network. MQTT is supported by several open-
source libraries. [45]

In the case of Müller et al. [45], different online simulation instances for
different manufacturing units exchange their state information via MQTT. They
used an update interval of 200 ms, which is acceptable for the communication
with MES systems or non-real-time components, but cannot compete with
protocols used in mechatronic systems internally, like industrial Ethernet.

###  3.5 Real-time behavior and temporal synchronization

Online simulation at machine level is subjected to real-time constraints, as
the simulation results are needed at a certain point of time at the plant and
may be irrelevant afterwards [14]. Real-time behavior of a system refers to
the ability to keep deadlines and, especially regarding simulation, to be able
to synchronize to the real clock. Both aspects play a vital role within online
simulation.

Nakaya et al. [48] see the application of their tracking simulator for a
petrochemical plant on minute-level to hour-level and even in the static
domain, which corresponds to day-level. That means, that their proposed
tracking simulator is not suitable for real-time use on machine level, where
control cycles are in the order of milliseconds.

HiL models, which usually are connected to a control system via a fieldbus,
underlie hard real-time requirements. They often operate on the control’s
cycle, which is in the order of milliseconds for PLCs.

Also for real-time decision making, it is important to provide information by
time. In this case, the time scale is depending on the application, but as
several possibilities have to be simulated until certain decision points, the
simulation has to be performed much faster than real time. There has to be
found an optimum of simulation time scale and simulation speed concerning
simulation accuracy, which is related to a longer simulation duration, and
expressiveness, which is shrinking with the simulation’s duration due to
drifting of the real system’s behavior [6]. The simulation time scale and
simulation speed can be influenced by computing power, simulation step size
and level of detail.

####  3.5.1 Parallel and predictive online simulation

Online simulation can be run in parallel to the real system or sped up to
predict the system behavior. Important applications of parallel simulation are
monitoring, diagnosis and robust operation. The diagnosis aspect of parallel
online simulation is important for the service and maintenance of a plant, as
deviations to the intended behavior can be detected and tracked more easily.
By automatically replying to these deviations, a robust operation is provided
[30].

Predictive online simulation has to run faster than real time and, in contrast
to parallel online simulation, also model and simulate the control, as future
inputs and reference values have to be considered. However, a control model is
often also used for parallel online simulation, as it is easier to integrate
the control into the simulation in that way [63]. Predictive online simulation
aims to forecast failures in the plant behavior and also determines
degradation, which enables early scheduling of service tasks. Another
application of predictive online simulation is process optimization. Different
control options can be evaluated by simulation and the best option is chosen
for the process. In that way, decision support is given and optimal process
control is enabled. [30]

Cardin et al. [10] use predictive online simulation for decision making in
holonic manufacturing systems (HMS). They presented a novel approach of
proactive production activity control systems using online simulation [11].
Instead of periodically launching predictive simulations, like for model
predictive control and other proactive production activity control systems
(see Pujo et al. [58]), the predictive simulation can be launched on demand.
By simulating different options of control, the predictive simulation is used
to support decision making. For accurate initialization of the predictive
online simulation, a real-time simulator is used as an observer. This approach
is used often, as it provides a straightforward and accurate way to obtain the
system state for the initialization of further simulation instances (see
section 3.2.1).

To reduce computing time for predictive simulation, Manivannan and Banks [43]
propose a combination with a knowledge base, which processes simulation
results and observations of the real plant to assist a discrete event
simulation.

####  3.5.2 Temporal Synchronization

Cardin et al. [11] present a simple mechanism to synchronize an event driven
simulation to the real system. When an event occurs in the simulation, it
waits for the real event to occur before the simulation is continued. When an
event occurs in the real system and the simulation is not waiting for it, the
simulation is continued, but the next occurrence of the event in the
simulation is suppressed.

Manivannan and Banks [43] synchronize their discrete event simulation by
synchronizing the event list.

Bessey [7] proposes a method, which adapts the length of successive predictive
simulations to cause similar simulation horizons. This is necessary for the
simulation results to be better comparable.

###  3.6 Application scenarios

In this section, some of the most important applications for online simulation
at machine level and relevant applications of the aforementioned techniques
are presented, including implementation details.

Pantelides et al. [54] distinguish between closed-loop and open-loop online
model-based applications (OMBAs). Closed-loop control OMBAs provide direct
feedback to the physical system for process control. These could be virtual
sensors or model predictive controllers. On the other hand, open-loop advisory
OMBAs have no direct feedback and are used mainly as support systems, such as
monitoring, error detection or forecasting [63].

Another distinction is made between OMBAs for plant monitoring, which extend
the information of the plant provided by the sensors, and OMBAs for plant
forecasting.

The simulation can run continuously (e.g. for process monitoring), on demand
(e.g. for decision making), or at scheduled times (e.g. for performance
monitoring). [54]

####  3.6.1 Virtual assembly supported system

Figure 10: Structure of the symbiotic simulation proposed by Meng et al. [44]

A virtual assembly supported system for gas turbine manufacturing is presented
by Meng et al. [44]. The system consists of an input layer, including
measurement data, 3D models, etc., a database support layer, and a layer
consisting of an online simulation module, a data processing module, an
offline simulation module, an analysis module and a model process module (see
Figure 10).

The online simulation is responsible for real-time guidance and supervision of
the assembly process. This means, it finds deviations and errors, generates
warnings and provides a feedback mechanism for the offline simulation. It is
implemented as a real-time HiL simulation.

The offline simulation deals with process planning and analysis and is
implemented in the virtual space. Both modules are cooperating, as the offline
simulation is a preview and reference for the online simulation.

The simulations use actual data from the assembly system and enhance the
assembly by feeding back process information. There is diverse cooperation
between the modules, where information is exchanged and resources are shared.
Such compositions are known as symbiotic simulations [66].

####  3.6.2 Decision making

Online simulation has been used for decision making at MES level for a long
time [26]. The simulation is used to evaluate different scenarios and
therefore simplify the decision making.

Cardin et al. [11] apply their proactive activity control, which basically is
an online simulation system for decision making, to a holonic manufacturing
system consisting of different working stations and a transportation system.
Different orders are created and handled by the manufacturing system. When a
new order is created, a specific number of transporters have to be assigned.
By predictive simulation of the manufacturing system, the choice of
transporters was optimized in a way that the productivity was increased by 30%
in average.

Yoshitani et al. [74] apply this principle to a reheating furnace for slab
heating. They are using a high-fidelity model for predictions within a long
range and a low-fidelity model fort short term predictions. The approach is
comparable to model predictive control, where an optimal future control
trajectory is simulated and the determined control input is applied to the
plant.

Other examples of online simulation for decision support are Manivannan and
Banks [43], Bessey [8] and Krishnamurthi and Vasudevan [34].

Besides operational decision making, where online simulation mainly is used to
evaluate short-term decisions by predictive simulations, decision support is
also needed for engineering services. This comprises condition monitoring and
asset life-cycle management. Tjahjono et al. [72] present an online simulation
framework, which consists of a reliability estimator, an engineering service
simulation model and a condition monitoring system. The reliability estimator
uses the condition monitoring data to determine a likelihood of failure. This
indicator is used to plan maintenance actions. The engineering service model
enables predictive simulation to achieve an optimal maintenance activity. A
data capturing module collects data from the real system to synchronize to the
actual state. This state is used to initialize predictive online simulation.
Their experiments showed that online simulation compared to traditional
methods allows an improved monitoring, especially concerning systems with
dynamic behavior and perturbation. This is a valuable advantage for flexible
manufacturing systems in the context of Industry 4.0.

However, online simulation for decision making is less prevalent at machine
level, except model-based control methods. The major reason might be that
machines had a fixed repetitive operation in the past, where usually no
adaptions were made. This will change within Industry 4.0 due to
individualization and lot size one approaches. Additionally, adaptions need
experts at machine level, while at shop floor level simple heuristics and
adequate information are usually sufficient. Hence, decisions and
optimizations at machine level might be more complicated than shop floor level
decision support and optimized production scheduling.

####  3.6.3 Simulation-based early warning system

Hotz et al. [25] use online simulation for an early warning system. The idea
of simulation-based early warning systems is, to collect data from production
control centers, according databases or even from CNCs or PLCs and use them
together with simulation models to provide information about the future
behavior of a production system. An early warning system is designed
independently from the simulation tool and the simulation has to be embedded
invisibly, so that the user of the early warning system does not need to think
about the simulation. Hence, parameterization, initialization and the
execution of the simulation have to be automated.

####  3.6.4 Tracking simulator for steam reforming plant

In the process industry, the system behavior is changing over long time due to
degradation of catalyst activity and process drift [48]. The idea of a
tracking simulator came up thinking about transferring offline simulators from
the process industry (petrochemical, steel or paper plants etc.) into an
adapting online simulation [49]. That is why Nakaya et al. [49, 46, 47, 50,
51, 52, 53, 48] are using a steam reforming plant for a fuel cell as a
demonstrator, where it was commercially implemented later on. For this, they
use statistical models and FPMs for a hybrid model simulation [52]. The
statistical model is fed with measured values and data from the simulation.

Ishimaru et al. [27] also applied the tracking simulator to a depropanizer
process with a distillation column.

The main purpose of the tracking simulator in this case is to serve as a
virtual sensor for different chemico-physical quantities. Besides, it provides
possibilities for trend forecasts and optimized operation conditions. Nakaya
et al. [48] propose the use of tracking simulation together with model
predictive control (MPC) and real-time optimization (RTO) as MPC and RTO need
current estimations for unstable parameters.

####  3.6.5 Vibration Reduction

Due to energetic efficiency and material costs, lightweight machines are built
more often. However, lightweight machines are prone to vibration, which has to
be avoided. Measuring resonant frequencies of a machine before the
commissioning depending on machine positions is costly. Sekler et al. [69, 70]
propose a method to determine resonant frequencies from a simulation model,
whose behavior is calculated continuously on the control device during the
operation. In particular, predefined models are coupled based on the machine
position to determine the resonant frequencies for the current machine
position.

####  3.6.6 Wood-frame panel prefabrication

Altaf et al. [1] implemented an online simulation for a wood-frame panel
prefabrication facility, which consists of an RFID data acquisition module, a
central database and a discrete event simulation. Here, the simulation is
applied to a production line, consisting of multiple machines, but the
principle can also be applied at machine level, for example for a machine
tool.

The database contains the building panel information from 3D models, the
production schedule, and the panel locations, which are fed from the data
acquisition module.

The online simulation is implemented in Simphony.NET, where the production
steps are modeled as tasks, which are done by machines with workers as
resources. The simulation initializes with data from the database and runs
based on task-time formulas, which are developed empirically and determine the
processing time based on the panels geometry. Data, which could not be
obtained automatically, like the number of human workers, are put in via a
user interface.

The simulation is used to predict the performance of the production line and
to evaluate the real performance. This is necessary, as the panel size is an
insufficient indicator for the production time [1]. Transferring this to
machine level, online simulation provides new measures, which can be used to
determine the performance of a machine. These new measures are perfectly
suitable for pay-per-use approaches.

####  3.6.7 Digital Twin for CNC machine tool

CNC machine tools are prevalent in manufacturing. Luo et al. [42] present a
digital twin for a CNC machine tool, which provides precise simulation, self-
sensing, self-adjustment, self-prediction, and self-assessment. The mentioned
aspects play a key role in Industry 4.0 concerning the concepts of cyber
physical production systems and decentralized intelligence. One important
pillar therefor is modeling and simulation. In particular, Luo et al. [42] use
a system level simulation consisting of a descriptive model and an algorithm
model, which accesses real-time data for fault prediction using a fuzzy neural
network.

####  3.6.8 Flexible robot manufacturing

Traditionally, robot movements are typically hard-coded and sensors detecting
the workpieces and obstacles are not available. However, a lot of tools exist,
which enable the simulation of robots, for example to test applications and
code. Some tools even support automatic path generation. Virtual robots are
used to reduce the companies’ time-to-market and to improve the production
processes. [56]

Petschnigg et al. [56] mention several difficulties of using simulation for
robot operation:

  * •

Dynamically changing environment impedes collision detection, when paths are
planned by the simulation. Hence, either some kind of feedback information of
the environment via sensors or a dedicated cell, where the environment is
fixed, is necessary.

  * •

Dynamic change of the workpiece during the simulated process is hard to
include into 3D simulation with CAD models.

  * •

The necessary degree of detail is crucial, but hard to obtain. Therefore,
often it is tried to get an as detailed and as accurate model as possible.

  * •

The dynamic behavior of the end effector is hard to pre-calculate.

In their work, they connect a production environment with a parametrizable
simulation, which can calculate robot trajectories adapted to the current
state of the environment. The size and pose of obstacles and workpieces and
the current joint configuration of the robot are fed into the simulation
system, where they are used for path planning.

####  3.6.9 Online collision avoidance

Flexible production systems demand new safeguarding solutions, especially when
online adaptions and reconfiguration to the production system increase the
risk of a collision. In [67] and [24] a concept for an online collision
avoidance system is presented. In such a system an online simulation is used,
to calculate the motion of the real production systems in parallel. In the
virtual world the parts are surrounded by bounding volumes. Typically those
bounding volumes are larger than their real world counterpart to detect
collisions virtually before real collisions occur. Alternatively, the online
simulation is run predictively. If a collision is detected in the online
simulation, this information can be used to stop the production system or
initiate another reaction to prevent the collision. In [33] such a solution is
realized for a robot cell. This makes the close and flexible cooperation of
three industrial robots possible.

####  3.6.10 Operation monitoring

In flexible manufacturing systems, a plant often consists of different
production units, which dynamically execute jobs on work pieces. For dynamic
collaboration, the controllers need detailed online state information of the
machines and robots. For this, Jahn [28] uses synchronized online simulation
to reflect the state and behavior of production cells. By this, simple sensors
can be used to update and synchronize the online simulation with respect to
the mechatronic system and detailed state information is provided at any time
by the online simulation. In particular, Jahn [28] applies DES, but generally,
any type of simulation may be useful to determine system information. The
model type has to be chosen depending on the kind of information that is
necessary for operation monitoring.

##  4 Discussion

In this section, the investigated publications on online simulation at machine
level are interpreted and the results are discussed. This includes knowledge
gaps and limitations as well as future potential.

###  4.1 Current limitations and remaining challenges

Fowler et al. [19] identified two major challenges for online simulation in
2005. Firstly, synchronized factory models with real-time data access should
be run permanently. This challenge is weakened in Industry 4.0 due to high
availability of accurate real-time data and increasing computational power on
edge and cloud devices, but still existent. Furthermore, there still is a lack
of synchronization techniques.

Secondly, the factory models have to be built automatically. The excessive
costs of model development is one major reason, why online simulation is not
applied in practice [54]. Santillán Martinez et al. [63] also state that a
focus for future work should be set in reducing the efforts for modeling and
integration. This challenge is still present, but there already are multiple
activities in solving this problem [3, 13].

Besides automatic model generation, automatic validation of simulation models
is a current challenge that is necessary for online simulation on the one hand
and could be solved by online simulation on the other hand, also concerning
models that are not only used during the operation phase.

In the following, further limitations and challenges are derived.

####  4.1.1 Implementation

A lot of theoretical concepts have been presented, which can be used or
transferred to machine level. However, there is still a need for practical
concepts, to implement online simulation in industrial practice [63].
Especially at machine level, the implementation is difficult, as real-time
process data has to be handled. If the simulates takes place in the cloud,
suitable interfaces have to be established in the mechatronic system. If the
simulation is run on field level, a device has to be found that:

  * •

has enough computing power for the simulation

  * •

can access the real system’s inputs

  * •

has interfaces to provide the generated information to the control system and
to analysis and maintenance tools

  * •

provides an environment, where the simulation is conducted, administered and
maintained

Online simulation needs to access the real system’s inputs in any case. These
inputs comprise endogenous inputs, which are inputs coming from the control
system, and exogenous inputs, which are mostly unwanted impacts, which the
system has no control of [14]. The exogenous inputs are hard to consider, as
the mechatronic systems often do not provide sensors for these quantities.

Implementation challenges also include real-time capability. Especially cloud
solutions and high-fidelity models have to be improved regarding this to meet
the future requirements.

In total, there are no general and clear approaches on how to proceed to
realize an appropriate online simulation for a specific application or
problem, but only separate theoretical approaches with individual
implementation examples.

####  4.1.2 Platforms

Simulation platforms are helping to overcome implementation challenges, as
they provide appropriate simulation and modeling tools, various interfaces to
the mechatronic system and sometimes also real-time capable simulation devices
(see section 3.4).

ISG-virtuos for example is real-time capable and can be connected to common
fieldbus systems, like CANopen, Ethernet IP, EtherCAT, Profinet, Profibus, or
Powerlink. Furthermore, it supports the use of FMI models and has a C++ SDK.
To be integrated directly into the mechatronic system and to separate
simulation from modeling, ISG provides the Realtime Target, which is a
compact, cabinet-based industrial PC. Hence, ISG-virtuos as an example already
provides the most important features for online simulation.

However, to the knowledge of the authors there is no simulation platform that
is explicitly dedicated for online simulation. As described, current
simulation platforms already provide important features for online simulation,
but especially methods for synchronization and instantiation of child models,
with the ability to run in different simulation speeds, have to be developed.

####  4.1.3 Real-time capability

Real-time capability is a vital aspect for online simulation at machine level.
Using existing platforms saves implementation efforts, but may restrict the
simulation system concerning real-time capability depending on the real-time
behavior of the simulation platform. Besides interfaces, where industrial
Ethernet is recommended for real-time communication, the operating system and
the computing power of the simulation device are the main bottlenecks. Many
operating systems cannot provide the necessary real-time capability at all. In
other cases the models are to complex to be executed with real-time
conditions. Depending on the simulation platform, there even is an overhead
additionally to the model complexity, for example due to a Simulation Master
for co-simulation or for administrative functionality. To provide cheap
computing power to handle complex models, cloud services are used. This solves
the problem of computing power, but enhances the problem of real-time capable
communication.

For these reasons, many of today’s implementations of online simulation at
machine level are not real-time capable. In the considered set of 60 relevant
publications, only 16 publications address real-time capability and nine
publications consider HiL models, which usually are real-time capable (see
Figure 11).

An example, where real-time capable simulation of mechatronic systems is
already realized, is virtual commissioning with HiL models. This example could
serve as a role model for online simulation at machine level and simulation
platforms can be reused for both applications.

Furthermore, approaches are necessary to reduce the model complexity and to
save computing power, maintaining the informative value of the online
simulation.

####  4.1.4 Model maintenance

As the modeled plants and machines are changed and reconfigured over time,
also the models for online simulation have to be adapted. This exceeds the
synchronization techniques mentioned in section 3.3, as models not only are
optimized towards gradual changes in behavior but may have to be completely
reconfigured. As online simulation aims to reduce maintenance efforts of
production plants, the maintenance effort for the simulation itself has to be
minimized. Today, maintenance of models is complicated and entails a lot of
effort, as large parts are done manually. This problem not only exists in the
context of online simulation but generally in the field of digital twins,
where the concept of an evolving digital twin [37, 40, 16] came up recently.
Companies are having high expenses for keeping their digital twins up to date.
This is a huge challenge to be solved in future.

Model sustainability is highly related to this and entails solvability,
maintainability and tractability [54]. Hence, models should not be too complex
but also not too simple. Furthermore, they should be adaptable and modular by
design [41]. The distinction between model maintenance, model adaption and
model synchronization is vague in literature.

Another aspect affecting model maintenance is model reuse. For this, models
should be flexible in their application, so that they can not only be used for
online simulation, but also for virtual commissioning or in the design phase.
As the models might have to be adapted for the different applications, version
management is necessary.

####  4.1.5 Synchronization

real-time capabilityHiL modelsmodel reusestate synchronizationmodel
synchronization$0$$10$$20$$30$$40$$16$$9$$17$$42$$26$ Figure 11: Number of the
relevant publications found (see methods, section 2) addressing an issue
(total number of considered publications: 60)

The aspect of synchronizing the online simulation to the real system is
essential. State synchronization, where only the state variables are adjusted
to fit the real system state, is distinguished from model synchronization,
where the simulation model is adapted corresponding to the actual system
behavior. Figure 11 shows that 42 of the 60 investigated publications address
the aspect of state synchronization and another 26 publications also address
model synchronization. The shown statistics are not quantitatively
representative but show a clear qualitative direction.

Model synchronization is especially necessary, if the behavior of a system is
changing over time. As mentioned before, this could be due to degradation,
changing environment or changing operation conditions. Additionally,
inaccurate models are improved by model synchronization. This is why the
concept of tracking simulation is a fundamental step towards the usage of
online simulation in industrial automation.

The state synchronization method by Zipper has several disadvantages compared
to regular tracking simulation, as the models themselves are not tuned and the
simulated state only is corrected indirectly. If model parameters are
incorrect due to bad modeling or changing behavior, the synchronization method
by Zipper compensates this by changing the simulation input in a way that may
be unrealistic, which could cause the internal state to be unrealistic.
However, the implementations of tracking simulation presented in the
literature are not suitable for co-simulation with black-box models. As the
modeling effort is reduced if the models of machines are made up from
component models and composed in a co-simulation environment, and component
models provided by the component manufacturers are supposed to be more
accurate, black-box model co-simulation could supposably be the future
standard for machine modeling in machinery manufacturing. The approach by
Zipper is intended especially for this scenario.

A limitation of the current synchronization techniques is that they do not
consider the uncertainty of measurement, which is used for the model and state
correction. Also, if there is high noise, the state and model are adjusted too
often and too strong. Using the Kalman filter as a role model, the uncertainty
of measurement and the uncertainty of simulation should be considered to
achieve statistically optimal state and model adjustments. Furthermore,
determining the uncertainty of the state estimation would help for further
processing. Apart from this, Bessey [7] proposes a method, which weights
simulation results of predictive simulations, which were conducted
successively, as the different simulations used different real-time data for
initialization.

An explicit gap exists in the combination of model synchronization with HiL
models, which is not addressed by the reviewed literature at all. Model
synchronization and real-time capable simulation is addressed only by
Santillán Martinez et al. [63] and Bessey [6], which only deals with the need
for model synchronization and real-time capability independently, but not in
combination. At machine level, real-time capability is especially important,
since the simulation results should be used in the control system directly.
HiL models play a key role here, as they already exist from testing and
virtual commissioning and are real-time capable innately. Feedback from the
real system and proper reaction in form of synchronization is necessary to
ensure the accuracy and reliability of online simulation.

Besides state and model synchronization, temporal synchronization is a
challenge. The synchronization of different clocks itself has many solutions,
but the synchronization of multiple interacting simulation environments for
example, is an upcoming problem that is to be solved [23]. Applying
distributed simulation speeds up the simulation, but orchestrating multiple
local simulations is complex [6].

####  4.1.6 Processing of simulation results

As online simulation at machine level is expanding, it enables new
possibilities due to the supply with new types of data and information at
field and control level. Hence, questions on the use of this data and
information are coming up, like how to interpret the simulation data and how
to use these results. The results could be of quantitative manner, for example
giving estimations for physical quantities, or qualitative manner, for example
indicating if certain system states are avoided. Especially the latter is not
obvious, but important for retrospective analysis and safety critical systems.
The future online simulation systems have to provide appropriate
interoperability, to enable such analyses [6].

In general, retrospective analysis, which focuses on monitoring of the
mechatronic system, is distinguished from prospective analysis, which focuses
on the future operation, for example by validating or comparing control laws,
estimating future performance or determining optimal maintenance activities.
Online simulation supports the use of data analysis technologies, since it
provides more accurate and diverse data in the role of a digital twin.

###  4.2 Trends and future potential

$1988$$1989$$1990$$1991$$1992$$1993$$1994$$1995$$1996$$1997$$1998$$1999$$2000$$2001$$2002$$2003$$2004$$2005$$2006$$2007$$2008$$2009$$2010$$2011$$2012$$2013$$2014$$2015$$2016$$2017$$2018$$2019$$2020$$2021$$2022$$0$$1$$2$$3$$4$$5$$6$
Figure 12: Number of publications per year, which are considered as relevant

As seen in Figure 12, the number of relevant publications on online simulation
at machine level is increasing, although there is high variation from year to
year. The potential of online simulation at machine level especially arises,
as an increasing number of existing models reduces the obstacle of modeling
expenses to use online simulation. Also the number and extent of suitable
simulation platforms is increasing.

Additionally, online simulation is a form of a digital twin. The use of
digital twins is proclaimed as a major pillar for digitalization and is seen
as one of the most important technology trends of the recent years. Hence,
also the importance of online simulation is rising.

Common applications of online simulation at machine level are presented in
section 3.6. Important applications of the past were decision making support
and virtual sensing, while decision making support takes place rather at shop-
floor level. Nevertheless, machine level simulation is also necessary for
shop-floor level decision making. Virtual sensing is part of decision making
support, but mainly creates new possibilities concerning system control and
enables to substitute physical sensors, where they are expensive or hard to
integrate.

Another application of online simulation is model validation [41]. This is
done by applying the model for online simulation and comparing the simulation
results with the actual system behavior. By this, manual effort for model
validation is reduced.

####  4.2.1 Model reuse

Due to the increasing presence of various models from the design, engineering
and commissioning phase, further utilization of these existing models reduces
the expenses for the application of first principle models. Many models are
not used after the design phase. Online simulation supports the concept of
efficient re-utilization and consistent use of digital twin components across
the whole life cycle, as models from the design phase are reused. As seen in
Figure 11, 17 out of the 60 considered publications address model reuse.

Model reuse exceeds the limits of machines or plants, as component models or
submodels in general can also be used across different machines or plants.
This further improves economic efficiency and creates new value chains and
business models. [59]

Model reuse requires fixed interfaces and encapsulation of models. Current
efforts are also aimed on easy integration into the plant. All applications
have to be considered right from the beginning of the development and the
mentioned aspects have to be implemented by design.

Summed up, models should be reused consistently and efficiently across
machines or plants and life-cycle phases. Reusing models for online simulation
is a crucial step towards this goal. On machine level, especially HiL and SiL
models from virtual commissioning could be reused.

####  4.2.2 Online simulation as form of a digital twin

As mentioned before, online simulation of a system is a form of the system’s
digital twin. Generally, a digital twin is a virtual representation of an
asset. This digital representation ranges from ERP relevant data
representations to 3D CAD models. At machine level, online simulation is an
important pillar of corresponding digital twins. Online simulation includes
models of the machine, aggregates data and information concerning the machine,
and even generates further data and information by simulation.

According to Kritzinger [35], a digital twin must have bidirectional automatic
data flow between the real asset and the virtual counterpart. If there is no
automatic data flow, the virtual representation is just a digital model.
Transferring this to simulation, offline simulation only belongs to digital
models, but using simulation of a plant online and feeding data back to the
real plant can be referred to as a digital twin. This integration is covered
in section 3.4.

As a lot of data from mechatronic systems is collected in the context of
digital twins, online simulation plays a vital role as an easily accessible
data source. For accessibility, often cloud environments, SQL databases, and
IoT protocols are used (see [79, 39, 23]). Alternatively an OPC UA server
could be integrated (see [62, 63, 61, 71, 42, 60]), which seems to be the
future de facto standard in industrial communication.

Additionally to simply collect data, online simulators with model
synchronization can also detect changes in the behavior. Also the diversity of
collected data is enhanced. Together with the ability of predictive
simulation, these aspects of online simulation are supporting service and
maintenance.

By enabling deeper insights into the machine’s or plant’s state and behavior
remotely, teleoperation is supported. As the number of human operators in
production should be reduced for more efficient manufacturing and experts are
necessary to operate the complex plants, teleoperation is becoming an
increasingly vital factor.

####  4.2.3 Comparison to alternatives

One major difference of online simulation based on FPMs compared to learning
statistical models, like neural networks, is that online simulators based on
FPMs have plausible internal behavior. This is a big advantage, as diverse
phenomena could be interpreted more clearly and conclusions on the causes can
be drawn. The results of model updates can be interpreted physically and
therefore be used for service and maintenance. Furthermore, behavior, which is
not known from the past, can be predicted by the first principle models, as
they are not based on historical data. However, it is more complicated to
guarantee suitable model adaption for FPMs.

Another alternative for monitoring purposes are observers or Kalman filters.
Kalman filters should be treated as role models for online simulation
regarding the synchronization, as Kalman filters are processing the real
system output in a statistically optimal way to adapt the estimated system
state. Also model synchronization can be realized by Kalman filters, by
including model parameters into the state space. However, they are limited to
linearized state space models and machine models in industrial automation
could not always be easily transferred to state space models.

##  5 Conclusion

This work is intended as an overview of the state-of-the-art of online
simulation at machine level but also as an indication for remaining problems
and future work.

Online simulation at machine level provides multiple possibilities. As shown,
there are many applications for online simulation and its relevance is
increasing, due to Industry 4.0 and the prevalence of digital twins.
Furthermore, the obstacle of expensive modeling is reduced by automatic model
generation and reuse of existing models from previous life-cycle phases. Due
to these reasons, online simulation has an immense potential for future
applications at machine level.

As described in section 4, there are still many challenges that have to be
overcome to use online simulation efficiently. One major aspect is
synchronization of the simulation to the actual plant or machine. Another
challenge is the implementation. Multiple theoretical considerations
concerning initialization, synchronization, suitable platforms and real-time
behavior have been summarized in this article, but there are many practical
problems left, which have to be solved by future work.

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## Statements and Declarations

Funding The work presented in this paper has been partly funded by the German
Federal Ministry for Economic Affairs and Climate Action (BMWK) under the
project 13IK001ZF “Software-Defined Manufacturing for the automotive and
supplying industry https://www.sdm4fzi.de/”.