# Detecting anomalous images in astronomical datasets

Pedro Alonso  Department of Astronomy, Shanghai Jiao Tong University, Shanghai
200240, China  Shanghai Key Laboratory for Particle Physics and Cosmology,
Shanghai 200240, China  Jun Zhang  Department of Astronomy, Shanghai Jiao Tong
University, Shanghai 200240, China  Shanghai Key Laboratory for Particle
Physics and Cosmology, Shanghai 200240, China  Xiao-Dong Li  School of Physics
and Astronomy, Sun Yat-Sen University, Guangzhou 510297, China  CSST Science
Center for the Guangdong–Hong Kong–Macau Greater Bay Area, SYSU, Zhuhai
519082, China

###### Abstract

Environmental and instrumental conditions can cause anomalies in astronomical
images, which can potentially bias all kinds of measurements if not excluded.
Detection of the anomalous images is usually done by human eyes, which is slow
and sometimes not accurate. This is an important issue in weak lensing
studies, particularly in the era of large scale galaxy surveys, in which image
qualities are crucial for the success of galaxy shape measurements. In this
work we present two automatic methods for detecting anomalous images in
astronomical datasets. The anomalous features can be divided into two types:
one is associated with the source images, and the other appears on the
background. Our first method, called the Entropy Method, utilizes the
randomness of the orientation distribution of the source shapes and the
background gradients to quantify the likelihood of an exposure being
anomalous. Our second method involves training a neural network (autoencoder)
to detect anomalies. We evaluate the effectiveness of the Entropy Method on
the CFHTLenS and DECaLS DR3 data. In CFHTLenS, with 1171 exposures, the
Entropy Method outperforms human inspection by detecting 12 of the 13
anomalous exposures found during human inspection and uncovering 10 new ones.
In DECaLS DR3, with 17112 exposures, the Entropy method detects a significant
number of anomalous exposures while keeping a low false positive rate. We find
that although the neural network performs relatively well in detecting source
anomalies, its current performance is not as good as the Entropy Method.

methods: analytical – methods: data analysis – techniques: image processing –
surveys – telescopes – cosmology: gravitational lensing

##  1 Introduction

Anomalous images can be caused by atmospheric, environmental, or instrumental
conditions. They can be categorized into source and background anomalies.
Source anomalies are prominent in the vicinity of bright sources, distorting
their shapes, whereas background anomalies generally manifest systematic wave-
like patterns that distort the entire background of the CCD images. Fig. 1 and
fig. 2 show several examples of source and background anomalies, respectively.

Anomalous images can be a source of systematics in many astronomical
measurements. It could be a particularly important issue in weak lensing
studies if they are mistakenly included in the dataset. This is because the
cosmic shear signal is only of the order of a few percent, highly sensitive to
the quality of the images. In past surveys, the number of exposures was small
enough to check each one individually and discard the anomalous ones. More
recent surveys, such as SDSS (York et al., 2000), DES (The Dark Energy Survey
Collaboration, 2005), CFHTLenS (Heymans et al., 2013), HSC (Aihara et al.,
2018), or KiDS (de Jong et al., 2013), typically produce tens of thousands of
exposures, making human inspection impractical. As a result, the development
of algorithms that can automatically detect anomalous images is becoming
crucial.

Anomaly detection techniques are typically classified as supervised or
unsupervised. Supervised methods use labeled data to directly learn the
difference between normal and anomalous data. Common supervised anomaly
detection techniques include support vector machines (SVMs: Hearst et al.,
1998) and neural networks. In most real case scenarios, however, the amount of
anomalous data is generally much less than normal data, making it difficult to
collect enough anomalous data for training. Therefore, anomaly detection
algorithms have to rely solely on the information about normal data.
Unsupervised learning algorithms first learn the common features that describe
normal data and then use an anomaly detector to judge how similar new data is
to the normal one. If the features of the new data are very different from
those of normal data, it is classified as anomalous. Well-known unsupervised
techniques used in anomaly detection include principal component analysis
(PCA: Shlens, 2014), isolation forest (Liu et al., 2008), autoencoders
(Rumelhart et al., 1986; Michelucci, 2022), k-Nearest Neighbors (KNN:
Peterson, 2009), and K-means (MacQueen, 1967). In particular, visual anomaly
detection is an active field of machine learning (see Yang et al. (2021) for a
review) with applications in many fields, including astronomy (Han et al.,
2022).

In this project, we present two methods to detect anomalous images in
astronomical datasets. Our first method is analytical, and we call it the
Entropy Method. Our second method uses a neural network called autoencoder to
detect anomalies in an unsupervised way. We show the performances of the two
methods and compare them. We search for anomalies in the CFHTLenS and DECaLS
DR3 datasets.

The structure of this paper is as follows. §2 describes our sources of data
and introduces sources and background anomalies. §3 presents the Entropy
Method and its results. In §4 we introduce our deep learning method and its
results, and we conclude in §5.

##  2 Data and examples of anomalies

In this paper, our main focus is on identifying anomalous exposures within two
significant astronomical datasets. First, we use data from the The Canada-
France-Hawaii Telescope Lensing Survey (CFHTLenS: Heymans et al., 2012; Erben
et al., 2013), which was specifically design for weak lensing studies as part
of the CFHT Legacy Survey. Second, we use data from the third data release of
the Dark Energy Camera Legacy Survey (DECaLS DR3). DECaLS is one of the
imaging projects of the Dark Energy Spectroscopic Instrument (DESI: DESI
Collaboration et al., 2016) and it is extensively used in many astronomical
studies, including weak lensing.

CFHTLenS includes optical data in 4 fields (W1, W2, W3, W4), covering a total
of $154$ $\text{deg}^{2}$ in 5 optical bands (u, g, r, i, z), with the i-band
showing better seeing conditions than the rest. We search for anomalies in the
i-band of CFHTLenS, which consists of 1171 exposures. Each exposure is
composed of 36 CCD images.

DECaLS DR3 includes optical data in three bands (g, r, z), covering a total of
$4300$ $\text{deg}^{2}$ in g-band, $4600$ $\text{deg}^{2}$ in r-band, and
$8100$ $\text{deg}^{2}$ in z-band. We search for anomalies in the g, r, and z
bands, which are composed of 3919, 5712, and 7481 exposures, respectively.
Each exposure consists of 61 CCD images.

The i-band of CFHTLenS has undergone visual inspection, enabling us to
directly benchmark the performance of our methods against human inspection.
Furthermore, DECaLS DR3 provides us with a huge number of exposures to further
test the performance of our methods. Combining the datasets from CFHTLenS and
DECaLS DR3 forms an extensive and comprehensive collection of data, enhancing
the robustness of our analysis and enabling us to draw more meaningful
conclusions about anomalous exposures and weak lensing studies.

In the following we describe source and background anomalies and provide some
examples from CFHTLenS and DECaLS DR3.

###  2.1 Source anomalies

Fig. 1 shows examples of source anomalies, which are defined as those
anomalies that distort the sources or their vicinity across entire exposures.
They can be caused by various optical aberrations, such as coma, which
produces sources with comet-like shapes, or astigmatism, which causes
elongations on the sources. Other examples of optical aberrations that can
distort the shape of sources include spherical aberration and field curvature.
Thankfully, these optical aberrations have been extensively studied and modern
telescopes can correct for them. Nevertheless, it is possible that most source
anomalies in our data may be attributed to small shifts on the focal plane of
the telescope during observations. Depending on the magnitude of this shift,
it can introduce elongations on the sources (panels a and c of fig. 1) or even
generate multiple source images (panel b of fig. 1). Other types of source
anomalies are likely related to problems during the readout process. Vertical
bright trails like those found in panels d and f of fig. 1 can be generated
when the readout process starts while the shutter is still open. There are
other source anomalies with very peculiar forms such as panel e of fig. 1.

Despite the diverse nature of source anomalies, we have observed that all of
them exhibit a certain coherent anisotropy across the entire exposure. This
anisotropy distorts the sources or their vicinity along a particular
direction. This observation prompts us to utilize the orientation of the
sources across the entire exposure as our indicator for detecting source
anomalies.

We perform a statistical study of the orientation of all the sources within
each exposure to determine the likelihood of that exposure presenting a source
anomaly. The detailed description of this methodology is given in §3.1.

Figure 1: Examples of source anomalies in CFHTLenS and DECaLS DR3.

###  2.2 Background anomalies

Background anomalies can introduce distortions in the CCD images, and there
are two main types. First, light reflections inside the telescope can cause
the background to present a series of circular shapes that cover a significant
part of the exposure. Sometimes this effect is visible on single CCD images
(panel b of fig. 2), and, in other cases, the anomaly only become noticeable
when looking at the entire exposure (panel d of fig. 2). The second type of
background anomaly manifests as frequent stripes in the CCDs, likely caused by
electronic noise. These stripes exhibit a preferred direction, visible either
on single CCDs (panel a of fig. 2) or when looking at the entire exposure
(panel c of fig. 2).

Similar to source anomaly identification, we utilize the level of statistical
anisotropy of background gradients to detect background anomalies. A more
detailed explanation of the method is given in §3.2.

Figure 2: Examples of background anomalies in DECaLS DR3.

##  3 Entropy Method for detecting anomalies

In §2 we have shown that both source and background anomalies distort the CCD
images. In fact, these distortions can be quantified in terms of entropy. In a
normal image, sources are randomly oriented, which corresponds to a state of
high entropy. If the image presents a source anomaly, the majority of sources
are oriented towards a particular direction. This corresponds to a state of
reduced entropy. In the same way, the background gradients of a normal image
should be random. Background anomalies induce spatially coherent distortions,
which can significantly reduce the entropy of the background distribution. In
this section we present a method that quantifies the entropy of the CCD images
and use it as an indicator of the presence of anomalies.

###  3.1 Source anomalies

To quantify the entropy of the sources in terms of their orientation. We
define the source orientation as the angle between its long axis and the
x-axis, and we call it $\theta$. For each exposure, we construct a
distribution of $\theta$, including all sources in that exposure. In a normal
image, the orientation of the sources should be random, resulting in a flat
$\theta$ distribution and a state of highest entropy. In the presence of a
source anomaly, however, the sources will show a preferred orientation and the
$\theta$ distribution will not be flat, corresponding to a state of reduced
entropy.

We quantify the anomaly likelihood as the deviation of each $\theta$
distribution from the mean $\theta$ distribution of all exposures, with a
higher deviation indicating a higher likelihood of that exposure presenting a
source anomaly. We calculate this deviation using the Kullback-Leibler (KL)
divergence (Kullback, 1959; Cover & Thomas, 1991), which is commonly used in
machine learning and information theory as a measure of the distance between
two probability distributions. For each exposure, we divide its $\theta$
distribution into N bins of equal size and define its KL divergence as,

$\mathrm{KL}(P_{i}|\overline{P})=\sum_{j=1}^{N}{P_{i}}(\theta_{j})\cdot\mathrm{ln}\frac{P_{i}(\theta_{j})}{\overline{P}(\theta_{j})},$
(1)

where $P_{i}(\theta_{j})$ represents the value of the $j^{th}$ bin in the
$\theta$ distribution of the $i^{th}$ exposure and $\overline{P}(\theta_{j})$
represents the $j^{th}$ bin in the mean $\theta$ distribution of all
exposures.

Since it is assumed that the number of anomalous exposures is very small
compared to the size of the entire dataset, the mean $\theta$ distribution of
all exposures can be safely used as a representation of the distribution of
normal exposures.

To build the $\theta$ distribution of each exposure, we follow these steps:

  1. 1.

We center each source on a 64x64 stamp and calculate its ellipticity
components as,

$e_{1}=\frac{Q_{20}-Q_{02}}{Q_{20}+Q_{02}}$ (2)

and

$e_{2}=\frac{2Q_{11}}{Q_{20}+Q_{02}}.$ (3)

$Q_{ij}$ are the quadrupole moments defined as,

$Q_{ij}=\sum_{I(r)>I_{0}}I(r)x^{i}y^{j},$ (4)

where $r=\sqrt{x^{2}+y^{2}}$. The threshold $I_{0}$ is set as
$I_{0}=0.02I(\vec{r}=0)$.

  2. 2.

We calculate the angle between the long axis of the ellipse and the x axis as:

$\theta=\frac{\mathrm{arctan}(e2/e1)}{2}$ (5)

  3. 3.

For each exposure, we create a distribution $\theta$ including all the sources
in that exposure.

To identify sources, we realize that there are many possible algorithms for
background removal and source identification. However, these algorithms are
usually computationally expensive. For the purpose of detecting anomalies we
do not need a very accurate source identification method, since the anomalies
are more noticeable around the brightest sources. Therefore, we do not need to
select really faint sources, which require high level source identification
models. We aim for a quick and relatively accurate background removal and
source identification methods. In addition, it is important to note that, in
contrast with common source identification algorithms, we do not require
sources to be isolated within their stamp, meaning that other sources or
bright pixels can contaminate the stamp. This is because many anomalies
present multiple source images (panel b of fig. 1) or other bright pixels
(panels d, e, and f of fig. 1), and this requirement would directly discard
those sources and complicate the anomaly detection.

To build our background model, we select 1000 random points on the CCD image,
and discard the brightest 10% and the darkest 10% to avoid source pixels and
defective areas. We fit the remaining points to a polynomial of second order.
This builds our background model that is subtracted from the CCD image.

After the background has been removed, we define each source center as a pixel
that meets the following criteria:

  * •

Its value is above 4$\sigma$111We have explicitly tested setting higher
thresholds for source selection, such as $10\sigma$ and $30\sigma$, which
include only the brightest sources. They lead to very similar results in the
detection of anomalies..

  * •

It is brighter than all the other pixels within a $64$x$64$ stamp centered in
it.

  * •

The number of pixels above 2$\sigma$ within that stamp is higher than 6.

###  3.2 Background anomalies

Ideally, the background of a CCD image should exhibit a uniform distribution,
with the background gradients being random, corresponding to a state of
highest entropy. Background anomalies, however, introduce coherent features in
the background, such as stripes or wave-like patterns, which distort the
natural pixel uniformity and reduce the entropy of the background
distribution.

Similar to source anomaly detection, we search for a parameter that can
quantify the change of entropy in the background. We choose this parameter to
be the angle between the gradient at a given background point and the x-axis,
and we call it $\theta$. In a normal image, if we sample over a large number
of background positions, the $\theta$ distribution should be flat, since the
gradients should be random. However, in an image presenting a background
anomaly, the gradients exhibit certain preferred orientations, implying a
reduced entropy in the $\theta$ distribution.

The $\theta$ distribution is built following these steps:

  1. 1.

We smooth the background by applying a 10x10 mean kernel on the CCD image, to
enhance the wave-like features that define the anomaly and facilitate their
detection.

  2. 2.

We select 10000 random points from the smoothed image. For each point at
position (i, j), we calculate its gradient components as

$D_{x}=\frac{1}{2}\sum_{m=i+1}^{i+5}\sum_{n=j-5}^{j+5}\frac{(I_{m,n}-I_{2i-m,2j-n})}{m-i}$
(6)
$D_{y}=\frac{1}{2}\sum_{m=i-5}^{i+5}\sum_{n=j+1}^{j+5}\frac{(I_{m,n}-I_{2i-m,2j-n})}{n-j},$
(7)

where $I_{m,n}$ is the value of the pixel at position (m, n). From the
gradient components we derive the angle $\theta$ between the total gradient
and the x-axis as,

$\theta=\mathrm{arctan}\left(\frac{D_{y}}{D_{x}}\right)$ (8)

  3. 3.

Similar to source anomalies detection, we form a distribution of $\theta$, but
this time on a CCD level rather than exposure level. This is because, unlike
in source anomalies, we do not expect the same preferred direction for
$\theta$ on every CCD of the exposure.

We calculate the KL divergence (eq. 1) between the $\theta$ distribution of
each CCD and the mean $\theta$ distribution of all CCDs in the dataset. For
each exposure, we average the KL divergence of all its CCDs and use this value
as our background anomaly likelihood indicator.

###  3.3 Results

####  3.3.1 CFHTLenS

Zhang et al. (2019) reported 13 anomalous exposures in the i-band of
CFHTLenS222”827410” of field w1m2m2, ”792617” of w3m1m0, ”792436” of w3m1m2,
”987104” of w3p2m3, ”859948” and ”859950” of w4m1p0, and all 7 exposures of
w2p2p2. Applying the Entropy Method to this dataset, we can successfully
detect 12 of the 13 exposures identified by Zhang et al. (2019), only failing
to detect the exposure ”792436” of w3m1m2 (fig. 3), which presents extremely
faint anomaly patterns that are only visible around very bright sources. From
the anomalies reported by Zhang et al. (2019), we detect 5 as source anomalies
and 7 as background anomalies (all exposures of field w2p2p2). In addition, we
detect 10 new source anomalies; ”705392”, ”705393”, and ”705394” of field
w3m0m1, ”732383”, ”732384”, and ”732385” of field w2p3p2, ”797642” and
”797643” of w3p1p2, ”775160” of w2p1p3, and ”853686” of field w3p3m3. Fig. 4
shows a few examples of the newly detected source anomalies. These anomalies
present small elongations on the sources, creating a systematic anisotropy
across the entire exposure, which is detected by our method but could easily
be missed by human inspection. It is also possible that these elongations
could be understood as being caused by an elongated PSF. However, as we
discuss in §3.3.2, we believe that these are actual anomalies.

Figure 3: CCD patch of the exposure ’792436’ of the w3m1m2 field of CFHTLenS.
Figure 4: Examples of anomalous exposures in CFHTLenS that were detected by
the Entropy Method and missed by human inspection.

####  3.3.2 DECaLS DR3

With more than 17000 exposures, DECaLS DR3 forms a significantly larger
dataset than CFHTLenS, thus we do not check all the exposures by eye to
evaluate the performance of our method. Instead, we only check the $10\%$ of
exposures with the highest likelihood of being anomalous according to our
results. We analyze each band (g, r, z) independently.

Fig. 5 shows the results for source anomalies. We show the results for the
$10\%$ of exposures with highest KL divergence, i.e., highest likelihood of
being anomalous. We divide them into 10 bins of equal size and illustrate the
true positive rate (TPR) for each bin, based on eye check. As shown in §3.3.1,
eye check can miss many anomalies that can be captured by our method, and
therefore here we only use the eye check as a performance indicator, not as
the absolute truth.

In fig. 5, dark colors represent the TPR including only prominent anomalies,
i.e., easily observed by eye and undoubtedly anomalous. Light colors include
cases in which the sources present a small but systematic elongation along a
particular direction, which is very hard to detect by eye. As mentioned in
§3.3.1, these small elongations could also be seen as caused by an elongated
PSF. A strong enough PSF reconstruction technique should be able to handle
these elongated sources, but weaker PSF reconstruction techniques might not be
able to properly model the PSF and this could introduce bias in shear
measurements. We therefore show the TPR for the most restricted case (dark
colors) and for the most conservative one (light colors). Fig. 6 presents four
anomalous exposures found in DECaLS DR3, where two of them were classified as
prominent anomalies and the other two as dubious.

It is important to note that most of these dubious exposures present very
small elongations along the vertical direction. If these anomalies were caused
by an elongated PSF, the orientation of the psf should be, although roughly
constant along the CCD image, random from exposure to exposure. The preferred
vertical direction in most anomalous exposures in the dataset seems to
indicate that these elongations are actually caused by anomalies (possibly due
to CCD electronics) and not by an elongated PSF.

Fig. 7 shows the results for background anomalies. We plot the top $10\%$ of
exposures with the highest likelihood of being anomalous, as in source
anomalies. However, in this case we do not need to distinguish between obvious
and dubious anomalous exposures, since background anomalies are less ambiguous
for the human eye.

Figure 5: True positive rate for the 10% of exposures with highest likelihood
of presenting source anomalies according to the Entropy Method. The true
positive rate is based on human inspection. We divide the exposures into 10
bins of equal size, for each band (g, r, z). Dark colors include only
prominent anomalies, whereas light colors also include dubious anomalies.
Figure 6: Examples of prominent and dubious anomalous exposures identified in
DECaLS by the Entropy Method. Figure 7: True positive rate for the 10% of
exposures with highest likelihood of presenting background anomalies according
to the Entropy Method. The true positive rate is based on human inspection. We
divide the exposures into 10 bins of equal size, for each band (g, r, z).

##  4 Deep learning method for detecting anomalies

In this method we use a neural network called autoencoder to detect source
anomalies. We train the autoencoder to reconstruct source stamps and we use
the quality of these reconstructions to quantify the likelihood of exposures
presenting source anomalies. In this section we introduce the basic ideas of
autoencoders, and then show our results.

###  4.1 Autoencoders

Autoencoder is a type of neural network that learns to reconstruct input data
in an unsupervised manner (Rumelhart et al., 1986; Michelucci, 2022). It
comprises two components: an encoder and a decoder. The encoder is a feed-
forward neural network that compresses the input data into a lower-dimensional
space, also known as latent space. The decoder performs the inverse process,
transforming the data from the latent space back to the input dimension. The
bottleneck layer, located at the last layer of the encoder, has some
dimensionality constrains that guarantee that it has fewer neurons than the
previous layers, to ensure that the autoencoder does not merely copy the input
data.

The power of autoencoders lies in their ability to capture the primary
features of the input data, commonly known as latent features. This is akin to
Principal Component Analysis (PCA), but while PCA is a linear method,
autoencoders are not restricted to linear relationships on the data and can
capture more complex features. Autoencoders have a wide range of applications,
including image denoising, feature extraction, and anomaly detection. They are
particularly effective in certain image anomaly detection problems, due to
their ability to work in an unsupervised way and therefore not requiring
previous knowledge about the anomalies. Based on the assumption that the
amount of anomalous data is very small compared to the size of the entire
dataset, autoencoders automatically learn the features that describe “normal”
data.

###  4.2 Training data and network

Our dataset consists of 10000 source stamps from DECaLS DR3, each of size
64x64. We include sources from the g, r, and z bands.

The input of our neural network are the flattened source stamps, with size
4096 ($64*64$). The encoder is composed by three dense layers with 128, 64,
and 32 nodes respectively. The decoder is also composed by three dense layers
with 32, 64, and 128 nodes, respectively. The output of the decoder has equal
size to the input data. Fig. 8 presents a visual representation of our
Autoencoder. Although this is a very simple model, it suffices for our purpose
since the source stamps do not exhibit complex features and we aim to avoid
overfitting.

We divide our dataset into training ($\sim 80\%$) and validation ($\sim 20\%$)
sets and train our network for 500 epochs, reaching a stable validation loss.
We select the model at the epoch with lowest validation loss as our final
model to make predictions.

Figure 8: Architecture of our autoencoder. The encoder has three layers, with
128, 64, and 32 nodes respectively. The decoder has three layers with 32, 64,
and 128 nodes, respectively.

###  4.3 Results

For each exposure in DECaLS DR3, we select all its sources and we feed them
one by one to our trained model. The output of the model is the reconstructed
version of the input source stamp. We calculate the mean squared error (MSE)
between the original stamp and its reconstructed counterpart as:

$\text{MSE}=\frac{1}{N^{2}}\sum_{i=1}^{N}\sum_{j=1}^{N}\left(\hat{x}_{ij}-x_{ij}\right)^{2},$
(9)

where $x_{ij}$ is the (i, j) pixel of the original source stamp and
$\hat{x}_{ij}$ is the (i, j) pixel of the reconstructed source stamp. $N=64$
is the stamp size.

We finally calculate the mean MSE of all the sources within each exposure and
use that value as our source anomaly indicator. A higher mean MSE indicates a
higher likelihood of that exposure being anomalous.

Fig. 9 shows our results. We plot our results as in fig. 5 and fig. 7. Dark
colors include only prominent anomalies whereas light colors also include
dubious ones. Our model shows a good performance in detecting source
anomalies, particularly the prominent ones. This is expected since prominent
anomalies strongly distort the sources, and these look clearly different from
all sources within normal images. However, dubious anomalies induce small
elongations on the sources. These small elongations only become problematic
when we look at the entire exposure, not to a single source. Single sources
have intrinsic shapes of the same order as the elongations, and therefore
single sources elongated by the anomaly simply look like normal sources, and
their reconstruction is very accurate.

To conclude, although the autoencoder shows a decent performance in detecting
source anomalies, it is not as effective as the Entropy Method. The
autoencoder fails to detect many anomalous exposures that were identified by
the Entropy Method and it shows a higher false positive rate.

Figure 9: True positive rate for the 10% of exposures with highest likelihood
of presenting source anomalies according to our deep learning method. The true
positive rate is based on human inspection. We divide the exposures into 10
bins of equal size, for each band (g, r, z). Dark colors include only
prominent anomalies, whereas light colors also include dubious anomalies.

##  5 Conclusions

The detection of anomalies is a crucial aspect of astronomical research, as it
enables scientists to identify and understand unusual phenomena that deviate
from expected patterns. These anomalies represent observational artifacts that
need to be carefully distinguished from genuine astronomical signals.

In this study, we have presented two methods for detecting anomalous images in
astronomical datasets. We tested our methods on the i-band of CFHTLenS and the
g, r, and z bands of DECaLS DR3. Our first method, named as Entropy Method, is
based on the fact that both source and background anomalies introduce
systematic anisotropies on the CCD images. Leveraging this insight, we study
the statistical orientations of sources to detect source anomalies and the
statistical orientation of the gradients at background points to identify
background anomalies. The Entropy Method has demonstrated excellent results in
detecting anomalies in both CFHTLenS and DECaLS DR3. In CFHTLenS, the Entropy
Method outperforms human inspection by detecting 12 of the 13 anomalous
exposures previously identified by human inspection and detecting ten new
ones. In DECaLS DR3, the Entropy Method has detected a substantial number of
source and background anomalies while maintaining a low false positive rate.
Our second method involved training an autoencoder to reconstruct source
stamps, and we used the quality of these reconstructions as an indicator of
the likelihood of exposures containing source anomalies. After evaluating this
method on DECaLS DR3, we found that although the autoencoder performs
relatively well, the performance of the Entropy Method is significantly
better. The autoencoder fails to detect several anomalous exposures identified
by the Entropy Method and exhibits a higher false positive rate.

Autoencoders are unsupervised, meaning that they do not use any information
about the anomalies for training. We chose an unsupervised method to ensure
fair comparisons with the Entropy Method. However, utilizing the anomalous
exposures found by the Entropy Method to train a neural network in a
supervised way could potentially increase the performance of anomaly
detection. This would lead to a more general method that could combine the
anomalous exposures from different datasets to perform a more generalized
anomaly detection method, applicable to new datasets. Once new anomalous
exposures are found, the neural network could be fine-tuned. This approach is
left for a future work.

Overall, we believe that our research serves as a stepping stone for future
advancements in anomaly detection techniques in astronomy. Our work lays the
foundation for more sophisticated and efficient methods, potentially combining
traditional statistical methods and machine learning methods to further
enhance anomaly detection capabilities. As the field of astronomy continues to
evolve with technological advancements and increasingly vast datasets, our
methods are poised to play valuable roles in uncovering the secrets of the
cosmos.

##  6 acknowledgements

CFHTLenS is based on observations obtained with MegaPrime/MegaCam, a joint
project of CFHT and CEA/IRFU, at the Canada-France-Hawaii Telescope (CFHT)
which is operated by the National Research Council (NRC) of Canada, the
Institut National des Science de l’Univers of the Centre National de la
Recherche Scientifique (CNRS) of France, and the University of Hawaii. This
work is based in part on data products produced at Terapix available at the
Canadian Astronomy Data Centre as part of the Canada-France-Hawaii Telescope
Legacy Survey, a collaborative project of NRC and CNRS.

The Legacy Imaging Surveys of the DESI footprint is supported by the Director,
Office of Science, Office of High Energy Physics of the U.S. Department of
Energy under Contract No. DE-AC02-05CH1123, by the National Energy Research
Scientific Computing Center, a DOE Office of Science User Facility under the
same contract; and by the U.S. National Science Foundation, Division of
Astronomical Sciences under Contract No. AST-0950945 to NOAO. The Photometric
Redshifts for the Legacy Surveys (PRLS) catalog used in this paper was
produced thanks to funding from the U.S. Department of Energy Office of
Science, Office of High Energy Physics via grant DE-SC0007914.

JZ is supported by the National Key Basic Research and Development Program of
China (No.2018YFA0404504), and the NSFC grants (11621303, 11890691, 12073017),
the science research grants from China Manned Space Project (No. CMS-
CSST-2021-A01). XDL is supported by the China Manned Space Project with No.
CMS-CSST-2021-A03 and No. CMS-CSST-2021-B01.

The computations in this paper were run on the $\pi$ 2.0 cluster supported by
the Center of High Performance Computing at Shanghai Jiaotong University, and
the Gravity supercomputer of the Astronomy Department, Shanghai Jiaotong
University.

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# Towards a Semantic Administrative Shell for Industry 4.0 Components

Irlán Grangel-González1, Lavdim Halilaj1, Gökhan Coskun1, Sören Auer1, Diego
Collarana1, Michael Hoffmeister2  1Enterprise Information Systems, University
of Bonn, Germany
<EMAIL_ADDRESS>2Festo AG & Co. KG,
Germany
<EMAIL_ADDRESS>
###### Abstract

In the engineering and manufacturing domain, there is currently an atmosphere
of departure to a new era of digitized production. In different regions,
initiatives in these directions are known under different names, such as
_industrie du futur_ in France, _industrial internet_ in the US or _Industrie
4.0_ in Germany. While the vision of digitizing production and manufacturing
gained much traction lately, it is still relatively unclear how this vision
can actually be implemented with concrete standards and technologies. Within
the German Industry 4.0 initiative, the concept of an Administrative Shell was
devised to respond to these requirements. The Administrative Shell is planned
to provide a digital representation of all information being available about
and from an object which can be a hardware system or a software platform. In
this paper, we present an approach to develop such a digital representation
based on semantic knowledge representation formalisms such as RDF, RDF Schema
and OWL. We present our concept of a _Semantic I4.0 Component_ which addresses
the communication and comprehension challenges in Industry 4.0 scenarios using
semantic technologies. Our approach is illustrated with a concrete example
showing its benefits in a real-world use case.

##  I Introduction

The dynamic of today’s world imposes new challenges to the enterprises. The
globalization, the ubiquitous presence of the internet and the development of
hardware systems are some of the technological improvements that provoke
changes everywhere. In the engineering and manufacturing domain, there is
currently an atmosphere of departure to a new era of digitized production. In
different regions, initiatives in these directions are known under different
names, such as _industrie du futur_ in France, _industrial internet_ in the US
or _Industrie 4.0_ in Germany.

Industry 4.0 (I4.0) is a term coined in Germany to refer to the fourth
industrial revolution. This is understood as the application of concepts such
as Internet of Things (IoS), Cyber-physical Systems (CPS), the Internet of
Services (IoS) and data-driven architectures in the real industry. With
approximately the similar meaning, in North America, the term Industrial
Internet has been created. This term is very similar to I4.0, but the
application is broader than industrial production. Other areas are included,
for instance, smart electrical grids [1].

With the goal to develop the Industry 4.0 vision, CPS are of paramount
importance. CPS integrate physical and software processes [2]. In order to do
so, they use various types of available data, digital communication
facilities, and services [3].

While the vision of digitizing production and manufacturing gained much
traction lately, it is still relatively unclear how this vision can be
actually be implemented with concrete standards and technologies. The physical
network connection problem is meanwhile largely solved using technologies such
as _Profibus/Profinet_ [4] and _OPC-UA_ [5]. However, the much more
challenging problem is to make smart industrial devices able to communicate
and _understand_ each other as a prerequisite for cooperation scenarios. To
address this problem, we need techniques and standards for representing and
exchanging information, data and knowledge between devices participating in
manufacturing and production processes. Such standards must be flexible to
accommodate new features, usage scenarios, cover multiple domains, device
categories, and bridge organizational boundaries. Most importantly, they must
be able to evolve seamlessly over time to facilitate the swift realization of
new features and scenarios as they become apparent.

Within the Industry 4.0 initiative, the concept of an Administrative Shell was
devised to respond to these requirements. The Administrative Shell is planned
to provide a digital representation of all information (and services) being
available about and from a physical manufacturing component. In this article,
we present an approach to develop such a digital representation based on
semantic knowledge representation formalisms such as RDF, RDF-Schema and OWL.
The advantages of such an RDF-based approach are:

  * •

_Identification._ The use of URI/IRIs provides an decentralized, holistic,
extensible global identification scheme for all relevant entities either
physical or abstract

  * •

_Integration_ The simple, but at the same time expressive statement-centric
RDF data model enables the representation of facts, raw data, schema,
provenance and meta-data information in a unified manner.

  * •

_Coherence_ Existing and new taxonomies, vocabularies, and ontologies can be
mixed and meshed to represent information about various domains, application
scenarios, organizations etc.

The remainder of this paper is structured as follows. An overview of
background information and descriptions of the relevant terminology for our
approach is provided in section II. A comprehensive list of challenges
aggregated from the current state of the art and our ongoing work on is
presented in section III. In section IV, we present _Semantic I4.0 Component_
which is our approach to addressing the challenges using semantic web
technologies. In addition, a concrete example is given in section V which
shows the benefits of our approach in real world use case. We provide an
overview about related work in section VI. The conclusion and an outlook to
future work are presented in section VII.

##  II Background

This section describes several concepts and terminology that are relevant for
our approach.

###  II-A RAMI Model

Several German institutions and associations worked in close cooperation to
define a reference model for Industry 4.0. The result is the _Reference
Architecture Model for Industry 4.0_ (RAMI 4.0), that describes fundamental
aspects of the Industry 4.0 [6]. It illustrates the connection between IT,
manufacturers/plants and product life cycle through a three-dimensional space.
Each dimension shows a particular part of these worlds divided into different
layers as depicted in Figure 1. Left vertical axis represents IT perspective
which is comprised of various layers such as business, functional,
information, etc. These layers corresponds to the IT way of thinking where
complex projects are decomposed into smaller manageable parts. In the left
hand horizontal axis is displayed the product life cycle where _Type_ and
_Instance_ are distinguished as two main concepts. The model allows the
representation of the data gathered during the entire life cycle. Along with
the right hand horizontal axis the location of the functionalities and
responsibilities are given in the hierarchical organization. The model
broadens the hierarchical levels of IEC 62264
111http://www.iso.org/iso/catalogue_detail.htm?csnumber=57308 by adding the
_Product_ or a _workpiece level_ at the bottom, and the _Connected World_ goes
beyond the boundaries of the individual factory at the top. In addition, the
reference architecture model allows the description and implementation of
highly flexible concepts. This leverages the transition process of current
manufacturing systems to Industry 4.0 by providing an easy step by step
migration environment.

Figure 1: Reference architecture model for Industrie 4.0 (RAMI 4.0) comprising
the three dimensions layers, life-cycle and hierarchy levels (from [6]).

###  II-B Industry 4.0 Component

A component is a basic concept in Industry 4.0. As defined in [6] an I4.0
component constitutes a specific case of a CPS. It is used as a model for
representing the properties of CPS, for instance, real objects in a production
environment connected with virtual objects and processes. An I4.0 component
can be a production system, an individual machine, or an assembly inside a
machine. It is comprised of two foundational elements: object and
Administrative Shell. Every object or entity that is surrounded by an
Administrative Shell is described as I4.0 component. Figure 2 shows an example
of such a component. Additionally, these objects have at least the capability
of passive communication. As a result, a flexible framework for data
description and provisioning is established. In the following, these elements
are presented in detail.

###  II-C Object

In [6], the term _object_ is used to refer each individual physically or non-
physically part. An object can be an entire machine, an automation component
or a software platform. From a time perspective, it could be a legacy system
or a new system developed by modern techniques and technologies. The industry
should be able to integrate and benefit from these objects, independent of the
type and time that they belong.

Figure 2: Concept of an Industry 4.0 Component consisting of the object and
its Administrative Shell.

###  II-D Administrative Shell

The Administrative Shell is used to store all important data of an object
which can be hardware or software. It creates benefits for all participants in
networked manufacturing. Consequently, a consistent way of managing data along
with various functions and services for data manipulation and publication is
provided. Some of the benefits are presented in detail as below [6]:

##### Management of Data

The Administrative Shell provides mechanisms to manage a large amount of data
and information generated by manufacturers or participants. For instance, it
stores and manages the information related to configuration, maintenance or
connectivity with other devices.

##### Functions

Different functions such as configuration, operation, maintenance, complex
algorithms for business logic are provided by the Administrative Shell. These
functions facilitate the interactivity between the I4.0 component and other
actors including human users.

##### Services

Although the information of a component is stored only once, they can be used
beyond the boundaries of the component, within enterprise networks and in the
cloud as well. The advantage is that the information can be provided via IT
services to any user or in any use case.

##### Integration

The Administrative Shell, in combination with communication protocols, offers
the possibility of easy integration of each I4.0 component.

##### Modularity

Each specific part of an object should be able to store information in
Administrative Shell. This ensures that all information are saved and is ready
to be used in further analysis. Consequently, the Industry 4.0 is applied
successfully in a networked manufacturing.

##  III Challenges

Developing the vision of Industry 4.0 raises new challenges. These challenges
become even harder since participants in a networked manufacturing apply
different policies. This section describes some of the challenges which are
more critical for I4.0 in general and I4.0 component in particular. In the
following, these requirements are presented in detail.

####  III-1 Interoperability (Ch1)

On the one hand, I4.0 vision includes new ways of managing data, machines and
components. On the other hand, the enterprises need to maintain a huge amount
of legacy systems with their corresponding existing data. Commonly, this data
is in different formats (e.g. plain text, DBMS, XML, etc.). The new data and
new formats have to coexist with the old ones.

####  III-2 Global unique identification (Ch2)

Enabling intercommunication among I4.0 components and the environment over the
Internet is a big challenge. In addition to this, there should be a linking
mechanism between the I4.0 components and the generated information [7].
Therefore, addressing this challenge is of paramount importance in order to
realize the vision of I4.0.

####  III-3 Data availability (Ch3)

Another challenge is the availability of the data beyond the boundaries of the
manufacturers and across different hierarchy levels. This challenge becomes
even harder when various policy rules from manufacturers are applied. I4.0
components will communicate with each other and interact with the environment
through exchanging the data generated from different sensors and react to the
events by triggering actions with the aim of controlling the physical world
[8]. Therefore, sharing the generated data between participants [9] is a key
factor in the Industry 4.0.

####  III-4 Standardization compliance (Ch4)

The Standardization process is an important step toward the realization of
I4.0. Several standards to deal with different layers in the enterprises exist
nowadays. For instance, _AutomationML_ [10], _Profibus_ [11] and _OPC-UA_ [5,
12] are just some of the examples of the mentioned standards. The core idea of
all this effort is to provide a detailed description of the components in the
manufacturing process. The production process constantly generates different
components and the standards need to reflect this dynamically. As a result,
the standards grow in size and number, making this interoperability between
them a problem to solve.

####  III-5 Integration (Ch5)

Highly dynamic environment is one of the key obstacles to the establishment of
the vision of I4.0. The complexity of horizontal and vertical integration of
the I4.0 components is drastically increased with the fluctuating number of
the participants. Self-sense, self-configuration and self-integration are some
concepts used to describe autonomous interaction of a component with the
environment in a networked manufacturing. Following the principles from
Reconfigurable Manufacturing Systems (RMS), adding, removing, replacing or
rearranging the components must not affect the production process [13]. Thus,
developing a consistent data model is a crucial factor that facilitates the
integration of I4.0 components in the changing environments.

####  III-6 Multilinguality (Ch6)

In order to achieve a wide range of applicability to different cultures and
communities [14], the I4.0 should be able to support localization (and
internationalization) of the generated information. This will decrease the
learning curve and allow easier and faster adoption of the Industry 4.0 in
real production environment.

##  IV Semantic I4.0 Component

###  IV-A Addressing the challenges with the semantic approach

It is widely accepted that semantics technologies play a crucial role
regarding the management of things, devices and services [15, 16]. Moreover,
[6] recognizes as a requirement that I4.0 components and their contents should
follow a common semantic model. Therefore, we propose a semantic approach to
address the challenges presented in section III. This approach is developed
using fine-grained principles from Semantic Web and Linked Data. Figure 3
depicts our proposal to add a semantic layer to the Administrative Shell.

Figure 3: The Semantic I4.0 Component architecture comprising vocabularies and
RDF representations of relevant standards for representing information about a
wide range of relevant components.

####  IV-A1 Interoperability

To meet the interoperability demand, RDF and Linked Data have proven to be a
successful way to integrate different types of data [17, 18, 19]. Embedded in
the semantic for Administrative Shell we propose RDF as a middle layer to
support the interoperability between the data of the legacy systems and the
data generated by the I4.0 component. We aim to establish RDF as a _lingua
franca_ for data interoperability in the I4.0 landscape.

####  IV-A2 Global unique identification

Identification of each I4.0 component by using global unique identifier
ensures entity disambiguation and retrievable [20]. According to linked data
principles [21], HTTP URIs should be used for naming things. Following the
above-mentioned principle, we propose that each I4.0 component should be
identified by an HTTP URI. By doing so, a decentralized, holistic and
extensible global unique identification scheme for I4.0 components is
established. As a consequence, we will have derefenceable I4.0 components
which are able to self-locate and communicate with each other. 1 presents our
proposal for identifying the I4.0 components. In addition, it shows that
identification capabilities can be extended by various existing vocabularies
that provides adequate means. This example uses the term _identifier_ from
_Dublin Core Vocabulary_ 222http://dublincore.org/documents/dcmi-terms/#H1 to
achieve a reference to the resource which is unambiguous within a given
context.

Listing 1: Global ID with RDF

⬇

@prefix i40c: <http://purl.org/eis/i40c/>.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

@prefix dcterms: <http://purl.org/dc/terms/>.

#Class Definition

i40c:Actuator rdfs:subClassOf i40c:Component ;

rdfs:comment "Actuator is ...";

rdfs:label "Actuator".

#Instance Definition

i40c:ActuatorAAA001 a i40c:Actuator;

dcterms:identifier "AAA001" ;

rdfs:label "Actuator ID AAA001".

####  IV-A3 Data availability

The benefits of employing RDF as the standard for representation of the data
are twofold. Firstly, various data serialization formats are easy to be
generated and transmitted over the network. Secondly, using
SPARQL333http://www.w3.org/TR/rdf-sparql-query/, as a W3C Recommendation for
an RDF query language, it is possible to make data available through a
standard interface. RDF representation of the data can be created on the fly,
even if they are stored in relational databases or other data formats [22]. By
doing so, our approach enables data sharing between legacy systems and other
participants in a networked manufacturing as well.

####  IV-A4 Standardization compliance

Following the idea of employing RDF as a lingua franca for data integration,
we propose to translate existing standards into RDF vocabularies and SKOS
thesauri. The interoperability between standards can thus be managed through
the integration of the respective vocabularies. In addition, these
vocabularies are also connected with the Administrative Shell data (cf Figure
3). As an example, we created an RDF vocabulary for the _IEC 61360 - Common
Data Dictionary_ (IEC CDD)444http://std.iec.ch/cdd/iec61360/iec61360.nsf/. IEC
CDD is a common repository of concepts for all electrotechnical domains based
on the methodology and the information model of IEC 61360. It provides a
widely accepted terminology and definitions based on accepted sources such as
IEC standards as well as international and industry standards. It contains
four major concepts: Component, Material, Feature and Geometry. Component
describes an industrial product which serves a specific function and which in
a given context is considered not to be decomposable or physically divisible
and is intended for use in a higher-order assembled product. Component is
represented by an Object in RAMI Model, and when it is surrounded by the
Administrative Shell forms and I4.0 component. Figure 4 depicts part of the
hierarchy of the iec:Component class.

Figure 4: Overview of the class Component Hierarchy in the IEC CDD Vocabulary.

####  IV-A5 Integration

Running on completely unified and consistent data model facilitates the
integration of I4.0 components. Newly added components need a shorter time for
the integration process. Other peers will be aware of new peer and the way of
communication with it by simply synchronizing with the latest version of
vocabulary. The Vocabulary contains all necessary information for interaction
and data exchanging between peers in a networked manufacturing.

####  IV-A6 Multilinguality

Since various communities across the world will interact with I4.0 components,
it is very important that they will receive terms in their own language. The
semantic web technologies enable implementation of multilinguality in a very
straightforward manner. This will remain valid even for the newly introduced
languages or concepts. 2 depicts an example of our approach for
multilinguality. In this example is modeled an actuator with the adequate
translation for _rdfs:label_ and _rdfs:comment_ into respective languages,
_English_ and _German_.

Listing 2: Multilinguality

⬇

@prefix i40c: <http://purl.org/eis/i40c/>.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>.

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

i40c:ActuatorAAA001 a i40c:Actuator;

dcterms:identifier "AAA001" ;

rdfs:label "Actuator with ID AAA001"@en;

rdfs:comment "Actuator is ..."@en;

rdfs:label "Aktor mit ID AAA001"@de;

rdfs:comment "Aktor ist ..."@de.

###  IV-B The Administrative Shell Vocabulary

In order to provide a semantic layer for the Administrative Shell we have
developed an RDFS vocabulary. This is depicted in Figure 5 (for brevity
reasons, we have omitted most of the properties and some of the classes). Our
approach defines the Administrative Shell vocabulary by providing an
ontological formalization of the terms and concepts presented in the I4.0
component. The central classes that cover the main concepts are
i40c:AdministrativeShell and i40c:Object.

The first class encapsulates the knowledge regarding the Administrative Shell.
This class wraps the object concept and connects it with most of the important
features in the I4.0 domain. The i40c:TechnicalFunctionality class allows to
encode information regarding the version, url of the software that manage the
I4.0 object. Additionally, this class model information about the life stage,
representation as well as the deployment of the mentioned object. Other
important classes connected to the i40c:AdministrativeShell are the
i40c:Manifest and i40c:Data. Firstly, the information for commercial,
mechanical and location features are modeled with the class i40c:Manifest.
Secondly, the class Data represents different types of data such as
statistical, engineering, etc.

The i40c:Object encapsulate the I4.0 object and important data related to it
(e.g. identification, image, technical data). In addition, information
regarding the phases of the object and the entire life cycle are managed.
Given that the I4.0 object can be part of other components, we used the
isPartOf Content Ontology Design
pattern555http://ontologydesignpatterns.org/wiki/Submissions:PartOf to capture
this characteristic.

We argue that using a lightweight RDFS vocabulary, an important step towards
realizing the I4.0 vision in real world applications is established.

Figure 5: Overview of the core classes and relationships of the Administrative
Shell Vocabulary.

##  V Use case

The vision of Industry 4.0 is centered around the concept of decentralized
production and smart objects that participate in the production in terms of
autonomy and decision-making. To accomplish this goal, object metadata, data,
and relations with other objects, need to be semantically described within the
Administrative Shell. By doing so, the information provided by one object can
be understood and exploited by other smart objects in the production chain. To
illustrate the applicability of our approach we detail, in this section, a use
case where the semantic Administrative Shell is used to describe an I4.0
component and some of its basic relations.

3 shows the semantic representation of the Administrative Shell for the _Motor
controller CMMP-AS-C2-3A-M3_ object (a product of Festo
AG666https://www.festo.com/cat/en-gb_gb/products_CMMP_AS). For brevity, we
describe here the most relevant data of the resources.

This example contains four instances of respective types. An AdminShell1
surround Object1 and related it with the majority of the concepts in the
domain, as the Platform1 in this case. Also, Object1 has its technical data
defined on the resource TechnicalData1 (cf. Figure 6).

Figure 6: Example of Industry 4.0 objects graph Listing 3: Semantic
Administrative Shell for the servo motor controller CMMP-AS-C2-3A-M3

⬇

@prefix i40c: <http://purl.org/eis/i40c/>.

@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .

i40c:AdminShell1 a i40c:AdministrativeShell ;

rdfs:label "AdminShell1"^^xsd:string ;

i40c:surround i40c:Object1 ;

i40c:hasTechFuncionality i40c:Platform1 .

i40c:Object1 a i40c:Object ;

rdfs:label "Motor control..."@en ;

i40c:hasId "1501325"^^xsd:string ;

i40c:hasPhase "Single-phase"@en ;

i40c:hasTechnicalData i40c:TechnicalData1 ;

i40c:image "<http://...b4dc.jpg>" .

i40c:TechnicalData1 a i40c:TechnicalData ;

rdfs:label "TechnicalData1"@en ;

i40c:BrakingResistance "60 Ohm";

i40c:Outputfrequency "0...1,000 Hz" ;

i40c:display "Seven-segment display"@en .

i40c:Platform1 a i40c:Platform ;

rdfs:label "Function blocks ..."@en ;

i40c:functionBlockUrl "https://.../Siemens_V3_0.zip";

i40c:hasDate "2015-11-02"^^xsd:date ;

i40c:hasVersion "3.0"^^xsd:string .

One of the main advantages of the Semantic Administrative Shell is the uniform
data representation according to the RDFS model, which enables efficient
integration and querying the data comprised in the shell. In order to
illustrate the data retrieval, we have designed simple SPARQL queries. For
example, it is relevant to know the technical characteristics of a component
in its various phases (e.g. _Single, Three_). In the following query (cf. 4),
we construct an RDF graph that contains a description of the technical feature
of an object with _Single-phase_.

Listing 4: SPARQL query to retrieve the electrical data with the state
_Initiated_

⬇

@prefix i40c: <http://purl.org/eis/i40c/>.

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

CONSTRUCT {

?object rdfs:label ?name.

?technicalData i40c:brakingResistance ?resistance .

?technicalData i40c:outputFrequency ?frequency .

} WHERE {

?object i40c:hasPhase "Single-phase"@en .

?adminShell i40c:surround ?object .

?object rdfs:label ?name.

?object i40c:hasTechnicalData ?technicalData .

?technicalData i40c:brakingResistance ?resistance .

?technicalData i40c:outputFrequency ?frequency .

}

Another example is retrieving the information of the I4.0 component platform,
during the maintenance cycle. The platform entities are referring to
functional library elements, which are specific to a certain automation
system. The query modeled in 5, obtains the details of the platform like the
name, version and the software URL that supports the I4.0 object.

Listing 5: SPARQL query to retrieve the version, date and URL of the platform
software

⬇

@prefix i40c: <http://purl.org/eis/i40c/>.

@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .

CONSTRUCT {

?object i40c:hasId ?objectId.

?platform rdfs:label ?name .

?platform i40c:hasVersion ?version .

?platform i40c:hasDate ?date .

?platform i40c:functionBlockUrl ?url .

} WHERE {

?adminShell i40c:surround ?object .

?adminShell i40c:hasTechnicalFuncionality ?platform .

?object i40c:hasId ?objectId.

?platform i40c:hasVersion ?version .

?platform i40c:hasDate ?date .

?platform i40c:functionBlockUrl ?url .

?platform rdfs:label ?name .

}

The above use case shows how the Semantic Administrative Shell provides a more
flexible data model. This semantic representation helps to overcome the
challenges that I4.0 is facing.

##  VI Related Work

_Approach_ | _Basic Concept_ | _Identification_ | _Data model_ | _Organization Type_ | _Serialization_
---|---|---|---|---|---
EDDL [23] | Device | Device and Unit ID | Object | Hierarchical | Text
PML [24] | Physical object | XML tag ID | Object | Hierarchical | XML
OMM [25] | Physical artifact | Primary ID and IDs Block | Element | Hierarchical | XML
SPDO [26] | Product | URI/IRI | Resource | Hierarchical | OWL-DL
Sem Admin Shell | Administrative Shell | URI/IRI | Resource | Hierarchical | RDF, RDF Schema, OWL
TABLE I: Comparison with related I4.0 component description approaches.

Currently, there are some efforts discussing the need of bringing more
semantics and data-driven approaches to I4.0. [27] presents guidelines, aiming
to help on choosing the level of semantic formalization for the representation
of the different types of I4.0 projects. The crucial role of semantic
technologies for mass customization is discussed in [16]. This work recognizes
semantic technologies as a glue to connect smart products, data and services.
Obitko [28] describes the application of semantics to I4.0 from the Big Data
perspective. The features of Big Data, as well as, an ontology for sensor data
are presented. Table I provides a comparison of our approach to the related
I4.0 component description approaches.

_Electronic Device Description Language_ (EDDL) is a language to describe
information related to digital components [23, 29]. Up to date, EDDL is
available for a huge amount of devices that are currently utilized in the
process industry. EDDL is a text-based description file of the field device
and its properties, which describes the data and how they should be displayed.

The  _Object Memory Model_ (OMM) is an XML-based format which allows for
modeling of the information about individual physical elements [25]. In this
work, the memory of the elements is partitioned to include different types of
data regarding the identification, name, etc. The idea of this approach was to
bring a semantic layer to the physical components but still suffers the
intrinsic limitations of XML. However, it is envisioned, that elements in the
OMM (so called blocks) contain RDF and OWL payload data. Extending the concept
of OMM _Domeman_ [30] is a framework for representation, management, and
utilization of digital object memories The idea of bringing semantic
descriptions of physical elements by combining OMM and a server realization
has been conducted by [31]. Nevertheless, this work is focused on the
identification of the elements and still rely on the above mentioned
limitations of OMM format.

The _Physical Markup Language_(PML) is a common language for describing
physical objects, processes and environments [24]. The goal of PML is to use
this descriptions in remote monitoring and control of the physical
environment.

Janzen [26] defines the Smart products as the connection of physical products
and information goods which allow the embedding of digital product information
into physical products. In this approach the _Smart Product Description
Object_ (SPDO) is presented. SPDO is a data model built on top of the _DOLCE_
ontology for describing smart products.

[32] defines an approach for distinguishing the local and global data
structures stored in Active Digital Object Memories (ADOMe), smart labels with
memory and processing capabilities. According to the author, this can be
realized by storing the data in a unified structured format.

The idea of bringing semantic descriptions of physical elements by combining
OMM and a server realization has been conducted by [31]. Nevertheless, this
work is focused on the identification of the elements and still rely on the
above mentioned limitations of OMM format.

##  VII Conclusion and Future Work

In this paper, we have described an approach for semantically representing
information about smart I4.0 devices with an Administrative Shell. The
approach is based on structuring the information using an extensible and
light-weight vocabulary aiming to capture all relevant information. Compared
to prior approaches, the RDF-based Semantic Administrative Shell has a number
of advantages. The URI/IRI based identification scheme provides a unified way
to identify all types of relevant entities, from physical objects, abstract
concepts, properties, concrete raw and derived data, etc. Existing standards
(such as eClass, IEC device characteristics or AutomationML) can be more
easily integrated and referenced. Information about and from different objects
can be easily integrated (since a basic integration can be achieved by merging
sets of triples). Accessing the information in a unified way is established by
using SPARQL as query language.

We see this work as a first step in a larger research and development agenda
aiming at equipping manufacturing equipment with semantics-based means for
communication and data exchange. In the medium to long term, we aim to bring
more intelligence to the edge of production facilities thus promoting self-
organization and resilience.

In future work, we envision to refine and expand the Administrative Shell
vocabulary in order to provide support for a wide range of device types. We
intend to study the interaction between various devices equipped with
Administrative Shells and research application scenarios, such as predictive
maintenance. Another interesting avenue of research is how Semantic
Administrative Shells can be generated and populated from existing information
systems and data sources available at the manufacturers.

## Acknowledgments

This work is supported by the German Ministry for Education and Research
funded project LUCID and European Commission under H2020 for the project
BigDataEurope (GA 644564).

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