# Sound Event Classification in an Industrial Environment: Pipe Leakage
Detection Use Case

Ibrahim Shaer  Department of Electrical and Computer Engineering
Western University
London, Canada
<EMAIL_ADDRESS>Abdallah Shami  Department of Electrical and Computer
Engineering
Western University
London, Canada
<EMAIL_ADDRESS>
###### Abstract

In this work, a multi-stage Machine Learning (ML) pipeline is proposed for
pipe leakage detection in an industrial environment. As opposed to other
industrial and urban environments, the environment under study includes many
interfering background noises, complicating the identification of leaks.
Furthermore, the harsh environmental conditions limit the amount of data
collected and impose the use of low-complexity algorithms. To address the
environment’s constraints, the developed ML pipeline applies multiple steps,
each addressing the environment’s challenges. The proposed ML pipeline first
reduces the data dimensionality by feature selection techniques and then
incorporates time correlations by extracting time-based features. The
resultant features are fed to a Support Vector Machine (SVM) of low-complexity
that generalizes well to a small amount of data. An extensive experimental
procedure was carried out on two datasets, one with background industrial
noise and one without, to evaluate the validity of the proposed pipeline. The
SVM hyper-parameters and parameters specific to the pipeline steps were tuned
as part of the experimental procedure. The best models obtained from the
dataset with industrial noise and leaks were applied to datasets without noise
and with and without leaks to test their generalizability. The results show
that the model produces excellent results with 99% accuracy and an F1-score of
0.93 and 0.9 for the respective datasets.

###### Index Terms:

Industrial Internet of Things, Sound Event Detection, Industrial Event
Detection, Machine Learning Pipeline, Support Vector Machine

##  I Introduction

The Internet of Things (IoT) technologies have been widely adopted in
economic, industrial, and health sectors given by their promise of sensing,
automating, and actuating processes [1]. As part of their perceived function,
IoT sensors can classify gathered data to rationalize the phenomena monitored
over an extended period. The task of classifying time-series data gives rise
to Time Series Classification (TSC) as a challenging aspect of the data mining
field. In that regard, the classification of the gathered data can detect the
existence or absence of the monitored phenomena.

Audio Event Classification (AEC) represents one application leveraging the
data collected by IoT devices. Sound classification applications span
automatic speech recognition [2], and Environment Sounds Classification (ESC)
[3]. In comparison to other disciplines, non-stationarity and aperiodicity
characterise ESC tasks [4]. The events in an urban or industrial environment,
belonging to the class of ESC tasks, display no specific structure and are
randomly generated as opposed to music and speech signals. Such factors
challenge identifying sound events of interest in an industrial environment.

In an industrial facility, stand-alone or fluid-carrying pipes integrated into
larger systems are essential for manufacturing processes [5]. The processes
result in unfavourable conditions for the facility’s workers such as the
exposure to high temperatures or the inhalation of harmful material, often
rendering the area around these pipes unreachable for humans. These conditions
hinder the workers’ ability to identify any leaks by eye test or manual
experimentation. As such, augmenting pipes with sensors to collect nearby
acoustic variations and linking them to the leakage phenomenon can be achieved
using Industrial IoT (IIoT) devices. To extract the concealed relationships
between the collected acoustic data and the presence of leaks, supervised
Machine Learning (ML) techniques can be utilized.

The leakage detection problem has been previously addressed in literature in
the works of Beghi et al. [6] and in real-world implementations of the Wessex
county water leakage detection challenge [7]. In comparison with these
environments, the industrial setup under study exhibits two distinctive
characteristics. First, the industrial framework encompasses many interfering
sounds that can potentially mask the identification of leaks. Such noises may
include the manufacturing process, which can be broken down into multiple
phases, each displaying its unique characteristics. Second, the conditions
created by the manufacturing process challenge extensively running experiments
that simulate leak conditions; thus, contributing to a limited amount of
collected data. Moreover, the developed module should be deployed on gateways
of limited computational capabilities close to the sensors collecting the
data. This aspect is prevalent in the edge computing paradigm [8] that is
adopted in the realm of IIoT applications to mitigate the communication issues
between sensors and gateways [9].

The data pertaining to our study were collected in an industrial setup in
collaboration with an industry partner, and the fluid leakages were manually
created. The acoustic data were represented in the time-frequency domain to
address the high-dimensionality of the raw acoustic signals. To address the
challenges imposed by the industrial setting under study and its requirements,
the contributions of this work are as follows:

  * •

Identify the challenges associated with fluid leakage detection in an
industrial setting and highlight its uniqueness in comparison to other
settings;

  * •

Propose an ML pipeline that addresses each of the challenges imposed by the
industrial environment;

  * •

Apply the proposed pipeline to three datasets, collected in different
industrial settings, and explain its capacity to identify fluid leakages while
minimizing false alarms.

The rest of the paper is structured as follows: Section 2 outlines the related
work. Section 3 describes the methodology. Section 4 provides the experimental
setup. Section 5 explains the results and Section 6 concludes the paper.

##  II Related Work

The problem of identifying leaks is a synergy between the TSC and ESC tasks.
Therefore, this section will investigate the contributions of the research
community in both disciplines.

Research conducted to address TSC tasks have experimented with hand-crafted
and automatic feature extraction techniques. The works in [10, 11, 12, 13]
applied Deep Neural Networks (DNNs) to time-series datasets of the UCR
repository, used as a benchmark to compare different approaches. These
approaches either use the raw time-series data as inputs, as is the case for
the work in [10], or transform the time-series data into images, that is
adopted in the remaining works of this category. In the realm of hand-crafted
feature extraction, the works in [14, 15] are the most prominent. Salamon et
al. [14] created a codebook from the time-frequency domain data to encode
different frames of the data. The encoded frames based on this codebook are
fed to a decision tree algorithm to classify the TSC tasks in the UCR
repository. On the same dataset, the work in [15] showed that the
classification accuracy of Fully Convolutional Network (FCN), when fed with
statistical features, outperforms the approach that only uses the raw input
data.

The surveyed ESC tasks include works classifying polyphonic and monophonic
sounds, which resemble our collected data that include overlapping leak and
industrial noise instances. The approaches proposed were applied to the
Detection and Classification of Acoustic Scenes and Events (DCASE) challenge,
which represents the largest collection of monophonic sounds. Koutini et al.
[16] and Yuji el al. [17] are two works that applied DNNs to extract features
and classify monophonic sounds. On the other hand, the authors in [18, 19]
have also used DNNs to classify polyphonic sound events derived from the DCASE
datasets. They synthetically created polyphonic sounds by overlapping
different sounds and employing data augmentation techniques to address data
scarcity issues. All of the mentioned works, except for the case of polyphonic
sounds, have reported high accuracy results relative to each of their tasks,
in the range of 90%.

Evaluating the outlined approaches will target the datasets the researchers
used to test their methods as they reflect the environment under study, and
the algorithms used for ESC and TSC tasks. The methods that apply DNNs for
both TSC and ESC tasks on either image transformations of raw input data or
the input data itself essentially violate two of the main constraints of this
industrial setup. First, the limited capability of gateways hinders the
training and testing of DNN models due to their resource-intensive nature.
Second, to avoid over-fitting, such models require large amounts of data,
which are not available in the current environment. On the other hand, the
datasets used to evaluate different methods do not reflect the industrial
environment because there is no transition phase from an in-existent event to
the event itself. To address these limitations, this study offers a light-
weight ML approach that combines feature selection and feature engineering
techniques applied to time-frequency data. The solution addresses the concerns
related to any industrial environment.

##  III Methodology

This section explains the method undertaken to tackle the leakage detection
problem. The first subsection describes the datasets. The second subsection
details the different steps of the applied Machine Learning Pipeline.

###  III-A Data Description

Each of the collected datasets describes the Power Spectral Density values
(PSD) of the process under study. In each dataset, the columns represent the
time domain, and the rows represent the frequency domain. The proposed ML
pipeline was applied to three different datasets, each with its specific
length and ambient conditions. The details of each dataset are provided in
Table I. Separated by a dash (_), the naming convention identifies the
existence of leaks and industrial noise. The datasets were collected with a
one-second granularity, and the frequencies obtained are in the range of 0 to
65,536 Hz with a sampling frequency of 131,072 Hz. The resulting datasets are
of row size 5,000 and column size equivalent to the period of data collection.
The last column in the table represents the leak intervals in each of the
datasets. Based on Table I, the Leak_process dataset has 4 leaks, and the
Leak_noprocess dataset has 3 leaks.

Name | Duration (sec) |  Leaks
---|---|---
Leak_process | 3096 |  $\left[1191:1276\right],\left[1370:1450\right],$
$\left[1796:1886\right],\left[1990:2081\right]$
Leak_noprocess | 3069 |  $\left[2300:2348\right],\left[2623:2670\right],$
$\left[2783:2833\right]$
NoLeak_noprocess | 2634 |  N/A
TABLE I: Datasets’ Details

###  III-B Machine Learning Pipeline

The proposed ML Pipeline is depicted in Figure 1. Five stages of pre-
processing, training, validation, and testing are applied to the dataset with
leaks and manufacturing noise denoted by Leak_process. This dataset is elected
for this procedure because it encompasses a more profound environment than the
other two available datasets. The other two datasets go through the same
procedure, except that the model selection phase is replaced by the best model
trained on the Leak_process dataset. The description of each stage of the ML
pipeline is as follows:

Figure 1: Machine Learning Pipeline

####  III-B1 Granular Dataset Creation

Each of the datasets in table I is an input to this stage. The first stage
creates three datasets of 1000 Hz, 2000 Hz, and 5000 Hz granularities to
reduce the dimensionality of the datasets by grouping sub-bands. The value of
each sub-band is calculated using three metrics: mean, median, and inter-
quartile range (iqr). The metrics are chosen to study the potential effect of
outliers in each sub-band on leakage detection.

####  III-B2 Frequency Band Selection

This stage takes as an input the granular datasets created in the previous
stage. The data pre-processing necessitates the extraction of sub-bands that
best represent the leakages, regardless of other interfering factors. While
these factors can affect a host of sub-bands, selecting specific bands can
minimize their potential effect on masking the identification of leaks.
Pearson correlation is calculated to quantify the assumed linear relationship
between the sub-bands and the probability of leaks. This step aligns with the
goal of reducing the dimensionality of the data to accelerate the ML modelling
[20]. The resulting datasets following this stage include the features with
the top-most correlation values with the output variable.

####  III-B3 Feature Engineering

This stage takes the resulting datasets from the previous step and applies a
time-based feature engineering technique to each of the sub-bands. The
temporal aspects are captured by overlapping adjacent time windows and
calculating statistical and temporal features.

The time-based features are listed in Table II. In this table, $x_{i}$ denotes
the value of the sub-band at time step $i$ after implementing the sliding
window process and $N$ represents the ten-second time frame. For $A_{t}$, five
different values of $t$ were considered to calculate the auto-correlations:
$t=\\{1,2,3,4,5\\}$. For $pct_{t}$, three different values of $t$ were
considered: $t=\\{1,2,3\\}$. Determining the bins that different values of
$x_{i}$ belong is a prerequisite for calculating the Shannon Entropy. To
mirror the leakage uncertainty, the $5^{th}$, $10^{th}$, $95^{th}$, $99^{th}$
quantiles are calculated for the leak data. Accordingly, this data displays a
distinctively higher entropy values compared to the data with no leaks.

In addition to the listed features, Approximate Entropy $APEn$ and Sample
Entropy $SampEn$ were included in the feature engineering procedure. Due to
space constraints, the keen readers can refer to the work in [21] to check
their calculation procedure. $APEn$ [21] quantifies the amount of
unpredictability of fluctuations in time series data to highlight the
transition phase between the two stages of absence and presence of leaks and
vice versa. $SampEn$ [21] addresses the limitations pertaining to the
calculation of $APEn$ by eliminating the re-calculation of a template vector,
which adds a bias to the $APEn$ values. It serves a similar purpose to $APEn$
in terms of quantifying the underlying data periodicities. Due to space
limitations, the full explanation of some features will be omitted. The work
of Sohaib et al. [22] provides a detailed explanation of some of the adopted
features.

Since the time-based features require a specific time window to be calculated,
a ten-second time window is considered. A leak is identified when a period of
ten seconds has passed with leak instances, reflecting that no anomalous non-
leak sound events are being recorded in that time window. The resulting
datasets include 52 predictors for two sub-bands, each representing a time-
based feature, and one output variable representing the existence of leaks.
The number of rows varies depending on the overlapping time window and the
original size of the dataset.

Feature Name Equation Feature Name Equation Feature Name Equation Peak
$P=max(|x_{i}|)$ Impulse Factor $\frac{P}{\frac{1}{N}\sum_{i=1}^{N}|x_{i}|}$
Square Root Mean $SRM=(\frac{1}{N}\sum_{i=1}^{N}\sqrt{|x_{i}|})^{2}$ Clearance
Factor $\frac{P}{SRM}$ Root Mean Square $\sqrt{(\frac{1}{N}E)}$ Margin Factor
$\frac{P}{SRM}$ Energy $E=\sum_{i=1}^{N}x^{2}$ Crest Factor $\frac{P}{RMS}$
Peak-to-peak $max(x_{i})-min(x_{i})$ Kurtosis
$\frac{1}{N}\sum_{i=1}^{N}(\frac{x_{i}-\overline{x}}{\sigma})^{4}$ Skewness
$\frac{1}{N}\sum_{i=1}^{N}(\frac{x_{i}-\overline{x}}{\sigma})^{3}$ Shape
Factor $\frac{RMS}{\frac{1}{N}\sum_{i=1}^{N}|x_{i}|}$ Index Maximum
$\frac{P}{N}$ Index Minimum $\frac{min(|x_{i}|)}{N}$ Auto-correlation
$A_{t}=\frac{\sum_{i=1}^{N-t}(X_{i}-\bar{X})(X_{i+t}-\bar{X})}{\sum_{i=1}^{N}(Y_{i}-\bar{Y})^{2}}$
Percentage Change $pct_{t}=\frac{(X_{i}-X_{i-t})\times 100}{|X_{i-t}|}$
Shannon Entropy $-\sum_{i=1}^{N}P(x_{i})\log P(x_{i})$ Rate Entropy
$H(X_{n}|X_{n-1},X_{n-2})$

TABLE II: Time-based Features

####  III-B4 Model Selection

The time-based features extracted in the previous step are the input to this
Model Selection stage. This stage conducts extensive experimentation to decide
the best parameters, which encompass finding the best hyper-parameters for the
chosen algorithm, the size of the overlapping time window, the granularity and
sub-band coupling, and the best metric to detect leaks.

The Support Vector Classifier (SVC) algorithm is chosen to classify the data
to leak and no leak instances. This algorithm is selected for multiple
reasons. It is a low-complexity ML algorithm that uses linear, polynomial, and
radial kernel functions. These functions map the initial feature space into a
higher-dimensional space, which enables evaluating feature interactions that
may contribute to improving the distinction between leak and no leak
instances. Accordingly, this “kernel trick” refines the construction of a
hyper-plane with maximal margins to provide better generalization for linearly
or non-linearly separable kernel functions [23].

The light-weight nature of the SVC algorithm favours its deployment on IIoT
gateways of limited computational resources; thus, facilitating the model’s
re-training and testing on new data. Moreover, the scarcity of data in such a
harsh environment requires a model that can generalise well with a small
amount of data, which is a feature of the SVC algorithm. In the field of
light-weight algorithms, Federated Learning approaches are promising in IIoT
applications [24], which will be explored in future work. The SVC algorithm
determines the hyper-plane separating the involved classes using a limited
amount of data. In particular, the instances that are close to the hyper-plane
drive the decision boundary, which separates the two classes of the leak and
no leak instances. As such, if the data points fed to the SVC model are
representative of both classes, the SVC would be able to generalise to
different scenarios. In this industrial setup, it is critical to provide the
reasons for a particular classification result. To that end, the developed SVC
model provides better interpretability for any resulting classification when
compared to deep learning models.

##  IV Experimental Setup

This section explains the experimental setup. It starts with outlining the
evaluation criteria and explaining the parameters used for different types of
datasets. Next, it discusses the experimental procedure in terms of the used
parameters and selected models in light of the proposed ML pipeline.

###  IV-A Evaluation Criteria

The models built using different parameters are evaluated using the standard
performance metrics specific to a classification task. These metrics include
accuracy, specificity, recall, and precision. Different metrics are
prioritized depending on the testing dataset .For the Leak_noprocess and
Leak_process datasets, precision, recall, and specificity will be used as an
indicator of the models’ performance. These indicators reflect the model’s
ability to correctly identify leak instances while minimizing false alarms.
For the Leak_noprocess dataset, a higher priority will be given to precision
and recall as indicators of the quality of the model and the chosen
parameters. Such considerations will be highlighted while explaining the
obtained results.

###  IV-B Experimental Procedure

The different parameters involved in the experimental procedure are summarized
in Table III. As is the case in any ML modelling, the dataset is split into
training, validation, and testing sets. The training set is used to train the
model, which is evaluated on the validation set to select the best parameters
outlined in table III. Since 864 parameters are being tuned, the hyper-
parameters of the SVC model and the overlapping time window parameter are
selected based on their performance on the validation dataset. This
intermediate step reduces the total search space of the parameters and allows
for better interpretation and analysis of the effect of the other parameters
on the performance of the developed models. The testing set is used to select
the granularity-metric and sub-bands combinations.

All the parameters were selected on the Leak_process dataset based on their
performance on its respective validation and testing sets. The best performing
model, encompassing the {granularity, sliding window, metric} parameters and
SVC hyper-parameters (regularization parameter, kernel, and gamma), is
evaluated on the datasets with different ambient conditions, denoted by
Leak_noprocess and NoLeak_noprocess datasets.

Parameter | Values
---|---
granularity | {1000, 2000, 5000}
sliding window | {3, 5, 7}
SVC_kernel | {linear, rbf}
SVC_Cost | {1, 10, 100, 1000}
SVC_gamma | {1, 0.1, 0.001, 0.0001}
metrics | {mean, median, iqr}
TABLE III: Experimental Parameters

##  V Results

(a) Precision on the testing set

(b) Recall on the testing set

Figure 2: Effect of metrics on the performance of sub-bands on the testing set
of the Leak_process dataset

This section first outlines the parameters that best performed on the dataset
with industrial noise. After that, the results of applying the obtained models
to datasets with no industrial noise are evaluated.

###  V-A Parameter Selection and Hyper-parameter Optimization

The best overlapping time window and SVC hyper-parameters are obtained based
on their performance on the validation set. The selected hyper-parameters are
the linear kernel and the 1 penalizing cost and 7 as the overlapping time
window. The validation step has disqualified some granularity-metric and band
combinations, but it still did not provide a clear-cut favorite set of
combinations. As such, several combinations qualified for the next step, which
were evaluated on the testing set of the Leak_process dataset.

Figure 2 depicts the results of applying these combinations to the testing
set. Due to space restrictions, the precision and recall were only taken as
metrics for evaluating the models. The discrepancy in performance for
different metrics of the same band shows the effect of outliers on the
performance of different band combinations. In most cases, the mean metric
displayed the better results compared with outlier-resistant metrics such as
median and iqr. With regard to band combinations, the combinations of low and
high bands, such as band_0_5k_40k_45k, have displayed poor recall results when
compared to combinations involving lower bands. Given that these band
combinations are selected based on their correlation with leaks, the obtained
results show that high bands have no contribution to identifying leaks.
Instead, these high correlation values are attributed to the effect of
industrial noise. These results show the importance of the inclusion of
multiple bands for identifying leaks and the effect of industrial noise in
emulating them.

The only two combinations that have displayed high precision and recall are
the combinations of iqr and band_2k_3k_1k_2k and mean and band_2k_4k_0_2k.
These combinations qualify for the subsequent analysis stage that takes the
models built on them and applies them to the Leak_noprocess and
NoLeak_noprocess datasets.

###  V-B Best Performing Models Implementation

The two best performing combinations are trained on the whole Leak_process
dataset and evaluated on the Leak_noprocess and NoLeak_noprocess datasets.
This approach assesses the generalizability of the model developed to be
applied to conditions with no industrial noise as it has proven its merit in
its presence. The parametric results of the given model are provided in Table
IV and V. The results show that both models managed to identify most of the
leaks, reflected by high recall, while avoiding false alarms, reflected by
high precision. Applied to the Leak_noprocess dataset, the two models
successfully identified all the normal cases. For the Leak_process dataset,
slight differences in results between the models can be highlighted. The
results show that the combination of mean and band_0_2k_2k_4k outperforms the
other combination in identifying leaks, which demonstrate that the wider bands
are more informative in terms of leak detection. Additionally, this result
shows that leaks can be better detected when incorporating outlier
calculation. This conclusion is emphasized by the better results displayed by
the combination that includes the mean metric.

Metrics | Leak_noprocess | NoLeak_noprocess
---|---|---
accuracy | 0.99 | 1
recall | 0.82 | N/A
precision | 1 | N/A
specificity | 1 | 1
TABLE IV: Results for iqr and band_1k_2k_2k_3k Metrics | Leak_noprocess | NoLeak_noprocess
---|---|---
accuracy | 0.99 | 1
recall | 0.88 | N/A
precision | 1 | N/A
specificity | 1 | 1
TABLE V: Results for mean and band_0_2k_2k_4k

##  VI Conclusion

This paper addressed detecting leakages in fluid-carrying pipes in a harsh
industrial environment. This environment is distinct compared to other urban
and isolated environments in terms of the inclusion of interfering noise and
the deployment requirements of any detection module. The former condition
challenges the identification of leaks. The latter results in data scarcity
imposing the use of low-complexity ML techniques. To address these limiting
conditions, this work proposes an ML pipeline encompassing feature selection
techniques and time-based feature engineering. The resulting data is fed to an
SVC algorithm that satisfies the environment’s deployment requirements. The
models obtained following the proposed ML pipeline successfully identified
leaks in environments with and without industrial noise. While the results
obtained are promising, there is still considerable room for improvement. A
suggested method would evaluate the proposed approach using various light-
weight ML classification models such as Decision Trees and ensemble learners
to compare them to the currently developed SVC model. Additionally, it is
critical to simulate leaks and industrial noise to increase the amount of
available data, allowing for a more extensive and profound evaluation.

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