Cyclic Graph Dynamic Multilayer Perceptron for Periodic Signals
Authors: Mikio Furokawa
Erik Gest
Takayuki Hirano
Kamal Youcef-Toumi
Venue:  (2020)

Abstract: We propose a feature extraction for periodic signals. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. It is possible to collect data about periodicity by mea- suring a shaft’s rotation. However, it is difficult to perfectly control the collection timing of the measurements. Imprecise timing creates phase shifts in the resulting data. Although a phase shift does not materially affect the measurement of any given data point collected, it does alter the order in which all of the points are col- lected. It is difficult for classical methods, like multi-layer perceptron, to identify or quantify these alterations because they depend on the order of the input vectors’ components. This paper proposes a robust method for extracting features from phase shift data by adding a graph structure to each data point and constructing a suitable machine learning architecture for graph data with cyclic permutation. Simulation and experimental results illustrate its effectiveness.

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CYCLIC GRAPH DYNAMIC MULTILAYER PERCEPTRON FOR PERIODIC SIGNALS
Anonymous authors Paper under double-blind review
ABSTRACT
We propose a feature extraction for periodic signals. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. It is possible to collect data about periodicity by mea- suring shaft's rotation. However, it is difficult to perfectly control the collection timing of the measurements, Imprecise timing creates phase shifts in the resulting data. Although a phase shift does not materially affect the measurement of any given data point collected, it does alter the order in which all of the points are col- lected. It difficult for classical methods, like multi-layer perceptron, to identify quantify these alterations because they depend on the order of the input vectors' components. This paper proposes a robust method for extracting features from phase shift data by adding graph structure to each data point and constructing a suitable machine learning architecture for graph data with cyclic permutation. Simulation and experimental results illustrate its effectiveness.
INTRODUCTION
Understanding what phenomena, rotating shaft is experiencing is critical for machine health mon- itoring. From industrial manufacturing equipment, transportation systems, to consumer products, rotating shafts are in many mechanical devices. Many issues such long-term fatigue, wear related issues, and acute failures can cause symptoms that are detectable from the shaft. The effort and flow variables associated with the shaft are desirable state variables to measure in nearly all these cases mentioned. Although these physical variables may provide useful information for detecting anomalies and estimating symptoms, one should extract features hidden in these signals. Therefore, an efficient feature extraction method plays an important role in anomaly detection and symptoms recognition.
Deep learning networks achieved remarkable results compared to the traditional methods. The time- frequency analysis, such as the short-time Fourier transform Xie et al. (2012) and the wavelet trans- form YanPing et al. (2006); Al-Badour et al. (201 1), well known feature extraction methods. For example, one can detect a certain bending mode by paying attention to the resonance frequency. Namely, domain knowledge expertise is needed to extract features from time-frequency represen- tation associated with particular phenomena. Some deep convolutional neural network (CNN) ar- chitectures achieved good results by taking time-frequency images inputs Verstracte et al. (2017); Guo et al. (2018). Although many machine learning methods with preprocessing schemes were used extract signal features, many of them do not really consider the specific characteristics of signals. For example, the output from general CNN is compressed by pooling regardless of time or frequency direction.
A method that considers the relative order information of signals is necessary. Classical methods, such as multi-layer perceptron (MLP), regard signals as vectors and accordingly use vectors as puts. However, vector does not give relationship information that might exist between coordinates. Therefore, while classical methods can measure data points, is difficult to detect whether they are in proper order relative to each other. This could occur due to pooling even if the signal is converted into an image by some method and input to the CNN. The relative order is very important to classify them. For example, the data obtained from rotating machinery is periodic as in Figure 5. Figure 5a shows noise associated with the rotation period, and so it is related to a rotation anomaly such crack in gear. However, Figure 5b shows noise that is different from the rotation period, and

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thus there is possibility that it is not related to rotation, but perhaps abnormality of the sensor system. Even with a classical method, it is possible to classify them if such data are included in the training data. However industrial machines would require significant time and cost to run the nec- essary experiments for collecting data. If the abnormality to be detected was rare, then the required effort would be magnified.
The proposed method to solve this problem, considers a graph structure for each data point. This scheme provides a relative order information about the vector coordinates. It then applies graph neural network, such as Atwood & Towsley (2016); Kipf & Welling (2017), to the graph structured data. For example, the relative information can be obtained by calculating tween the points. Since some deep learning approaches achieved results by concatenating different numerical sequences such as different sensor signals and treating them as inputs, we can concate- nate the original sensor signal with the crosscooreeations. This defines the relative information, and treat it as an input. However it not natural to treat them in the same way by simply concatenating them because the sensor signal represents physical value and the cross-correlation represents their relationship. Hence, the essential meaning is different. We deal with these different values simul- taneously by using a graph structure that represents each point and their relationships. Then, the obtained graph data is fed to a graph neural network for feature extraction. This enables the system to learn by focusing
cors-coorssstions be-
the relative relationship of each coordinate of the data point.
The main reason for using a graph structure is to give data additional information. The time- frequency representation simply converts the original signals to other forms. Inspired by the success of CNN in computer vision, Wang & Oates (2015); Zhu et al. (2019) proposed encoding time series as different types of images using methods other than time-frequency analysis and inputting them into CNN. Umeda (2017) proposed method of converting the original signals to high dimensional data cloud. While they are all categorized as information conversions, conversions meaning that they do not add any other information, we add relationship information as edges to the original signals, thus the graph hold richer information than the original signal and its conversions.
A key feature of the method is phase shift invariance. The application of our current research is for industrial machines with rotating shafts. Virtually every mechanized transportation vehicle, power generation, industrial machine, and robotic system contains rotating shafts. The shafts provide an opportunity to collect periodic signals. In practice, most measuring instruments such as sensors, processors and loggers along with their data acquisition systems show time delay respectively in the availability of the data. There a limitation to correct the delays by hardware design or implemen- tation. Hence, phase shifts may occur in the obtained periodic signals. However, these phase shifted signals are essentially the same. We identify them using a shift invariance method.
Our proposed method performs a cyclic permutation to a graph neural network. This method assures that the results account for phase shift the periodic measurements. It is not necessary to consider the vertex order in the graph originally, but is necessary give the order for computability. Here, we identify the graphs whose vertex orders are different due to phase shifts. The conventional graph neural networks regard them different. Therefore, we propose method that intentionally focuses on shift invariance by acting a cyclic permutation to graph neural network. The use of this method in Section 3 shows that it offers predictions with sufficient accuracies for idealized data and the experimental data obtained from a test setup Gest et al. (2019).
2 MACHINE LEARNING METHODOLOGY
In this section, define our machine learning method. First introduce necessary terminology to review the graph theory. Then we describe the method of constructing a graph structure for each data point, and introduce the learning model corresponding to the graph data. Finally extend the learning model to periodic data.
2.1 NOTATION AND TERMINOLOGY OF GRAPHS
The notation and terminology of graphs is as follows. Let G be a graph. We denote the vertex set and the edge set of G by the symbol (G) and E(G). A simple graph is graph containing graph loops or multiple edges. A complete graph is a graph in which each pair of graph vertices is connected by an edge. An ordered graph is graph with total order over its vertices. If graph
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G is ordered with |VG)) N, then regard V(G) as ordered set {utTISENN The adjacency matrix A(G) of a simple ordered graph G is a matrix with rows and columns labeled by graph vertices, with I or 0 in position (i; v2y) according to whether the vertices 8; and v connected by an edge or not, respectively. For graph G, a vertex labeling is a function from (G) to a set of labels. A graph with such function defined is called vertex-labeled graph. For vertex-laheles graph G. we denote the vertex labeling by the symbol L(G). Unless otherwise stated we assume that graphs are simple ordered graphs labeled by real numbers.
2.2 CONSTRUCTION OF GRAPH STRUCTURE
Now we construct a graph structure on each data point. Let x be a data point, namely, æ be an N- dimensional real vector (I1, ................................ IN) for some integer N. For simplicity of notation, we write (I:) instated of (I1, (21,,2.... EN). Fix integers ws and ss with 1 < ws, S. N, we call these window size and slide size. Let |-] be the floor function, namely |a] := max  Z n I a). Then we obtain the following -engegt sequence of ws-dimensional real vectors {u;), where N' L(N - w8)/ 88] +1.
U. = (I (((i-))=s++++ (i -ii- 1)sss+wss) with 1 S S N'
We fix real number and distance function d on the ws-dimensional real vector space, such as the Euclidean distance or the correlation distance. Then we can define a graph G as follows:
V(G) = { {u; {u:]sisN'
E(G) = {(0,, U3) v. 0g E VVG) with d(v,, uy) < <)).
C(G) proj
I((-1))ee+ws-
If the slide size and the window size small enough, then maximum, minimum and mean work the same as a projection for the labeling function. However, our case, vertices should be at least one period sub-waves because we construct edges by their similarity. Then max and min become constant values for every vertex. Also, periodically the window size becomes equal to a constant multiple of the period because our experimental data consists of many different frequencies. Then the mean becomes the same value for every vertex. Therefore, we use the projection as vertex labeling in this paper.
2.3 LEARNING MODEL FOR GRAPH DATA
Here we define our machine learning model suitable for graph data set. Our model draws inspiration from recent work on a graph neural network (Kipf & Welling, 2017). However, they consider the problem of classifying vertices of a graph by sharing filter parameters for each vertex and treat all vertices equal. On the other hand, since we consider graph-wise feature extraction and the vertices of our graph have time information as index, we think that it is not suitable to treat the vertices in the same way. Therefore, we avoid weight sharing by using the Hadamard product as shown below.
Set an integer N. Let GN be a set of graphs with N-vertices. Set an integer M, which is a number of hidden layers. We take a finite sequence of N-by-N matrices (Wm Jommommsss Ms called trainable weights, and finite sequence of N N-dimensional real vectors {bm JosmgM, called trainable bias. Then we define function & from GN to RV-V as follows:
(GG) LM+1,
where, Lo is the image of the labeling function (VG)))
Lmt1 = (Lm (A(G) o W..)) bm-
Here, A(G) = A(G) + IN with the rank N identity matrix IN and 7 is activation function, such as the ReLU, and means the matrix product and o means the Hadamard product. Graph neural networks usually use the Laplacian as A(G), but our experimental results were almost the same, so we used Á(G) = A(G) + IN instead. This turned out to be simpler than the Laplacian.
This function by admitting a graph structure on each data point. In fact, our function on the complete graph G, namely all the elements of A(G) are 1, is equal to a MLP. Hence we call this function a graph dynamic multi-layer perceptron (GDMLP).
be regarded
natural extension of multi-layer perceptron (MLP) obtained
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Feature Extract on Vertex Labels"
bare)
Regard vertex labels as sequence of real number.
Figure 1: Graph dynamic multilayer perceptron (GDMLP).
2.4 EXTENSION OF LEARNING MODEL TO PERIODIC DATA
In this section, extend the above learning model to another model which is suitable for periodic data. We consider its application to data obtained from industrial machineries containing rotating shafts. Accordingly each data point follows. Let 1" be the sampling interval, that is time between which data is recorded. Then æ (ra)ıçisN is periodic there exists a period T  = n with a positive integer n2 such that x; = zj if if mod nz. On the other hand, it is difficult to perfectly control the collection timing of these data points because there a limitation to correct the delays by hardware design or implementation. To account for this the data includes phuse shift x' (1() ISiNN of namely, for periodic data points x and x' with a period To Te nT,, there is a delay T [(a,,r') = (m, I with an integer ((xx"" such that z; 2nj "j=n(,,=') j ----i n1(x,=)) mod n. A period and a delay are multiples of the sampling interval T. This is a practical limitation and not a theoretical limitation. In general, sensor measurements are discrete in time. The sampling interval T is the minimum interval when the sensor actually measures. Although we use this notation to clarify this practical limitation, sampling interval T is not limitation in the theoretical claim (see the appendix for details).
(x.)
obtained from them is periodic defined as
Figure 2: (a): Two waves with phase shift. The above wave represented by sin 2nt 0.3 sin 16ml
100 with < t < 140 and the below x' is phase shifted wave of æ with 30 delay. (b): The graphs obtained from the left waves by the operation in Section 2.2 with ws 20, ss = 15, euclidean distance as d and e 4.5. (c): The subgraphs of the left graphs whose equivalence up cyclic order of vertices.
Although phase shift data points are different as vectors, the graphs obtained from them by the operation Section 2.2 have "large" equivalent induced subgraphs up to cyclic order of vertices. To state more precisely we make the following definition; pick a N-vertices graph G. Let {vihiisn be vertex set (G). Let a be cyclic permutation on V(G) such that (1;) = #/+1- Then the cyclic permutated graph (G) of G is defined as follows:
V(o(G)) = (o(v:)
E(a(G)) ((a(o)), (or,0)) F(G)),
L(a(G)): - (G))
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Let and x' be phase shift data points. They are different vectors (see Figure 2a). Hence the graphs Gæ. Gz' obtained from them by the operation Section 2.2 are not equivalent as ordered graphs (see Figure 2b). However, most of them produce identical vertices to each other up to cyclic order (see Figure 2c). In fact we can prove the following claim.
Claim Let æ and be phase shifted data points with period T. =T, = = n.T, and delay T(m,a")
(m,,"") Assume that a and x' more than periods, namely, [x] = |2'] 2 kn for an integer k 2 3. Then there exists window size and slide size satisfying the following condition. For graphs G and Gw which obtained from æ and x' by the operation Section 2.2, there exists an integer K (xx,z") such that both of G2 and a ((_,e') (Gat) have induced ordered subgraph ((a.x)) satisfying (S 2 [VG.) [k/(k + 1) (V(G.) (k)(() 1).
We provide proof of the claim in the appendix. By the claim, it is expected that the GDMLP outputs of G and o (a.a))>))) (Gw:) will be approximately the same.
For the above reason, we improve a GDMLP with a cyclic order as shown in Figure 3. We fix a GDMLP model 9x. Then define function @ from GN to TR as follows:
$ (G) = p (o(G), ((a(G)))) (G) 6(0 ((o (G)))
(1)
where, p is a pooling function, such as the wverage-pooling We call this function cyclic graph dynamic multilayer perceptron (CGDMLP).
GDMLP
GDMLP
pooling
GDMLP
Copy the original graph with order vertices.
Figure 3: Cyclic graph dynamic multilayer perceptron (CGDMLP).
2.5 LEARNING ARCHITECTURE
Since GDMLP is a feature extraction of the graph vertex labels, necessary compose function with GDMLP according to the final processing to be performed, such as a classification or a regression. Our main purpose is extract similar features from phase shifted signals. In our experiments, we confirm the performance of the proposed fcature extraction by checking the difference in accuracy with others. And this independent of the classifier used. Therefore we compose a MLP with GDMLP, which is simple method. The MLP input layer consists of the same number of perceptrons as the dimension of the GDMLP output. output layer and the hidden layer is optimized according to the sophistication of the problem, such as the number of classifications desired. Similarly, in the case CGDMLP, we compose tailored a MLP according to the output desired (see Figure 4). For simplicity of notation, and CGDMLP for the learning architecture whose feature extraction part are the feature extraction GDMLP and CGDMLP respectively.
the same letter GDMLP
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Data point
Graph
Feature Extraction
MLP
output
CGDMLP
Convert to graph by Operation Section 3.2.
Figure 4: Learning Architecture.
3 EXPERIMENTS
We now give an example where we compare our method for analyzing periodic data.
3.1 IDEAL DATA
First, we apply our method to idealized data that abstracts the problem are considering, such as a crack shaft, sensor malfunction, or external force that would impede mechanical rotation. The first of these would typically cause periodic noise, and the last two non-periodic noise. Reports of noises and their recurrence from a sensor, then, can be used to diagnose each of those and other issues. Our proposed method, which adds graph structure each data point give relative in- formation, should effectively make such a diagnosis. Also, because our proposed CGDMLP is with focus cyclie order invariance, is expected that phase shift signals can be identified because they produce approximately the same outputs in either unshifted or differently shifted signals.
Based on the above, we compare the classification result of each method for noisy sine waves (ft)icesT with some integer T defined as follows:
4n)) + 85,
where f is the frequency, tu is phase shift, e is a random noise from a uniform distribution over the half open interval 00005, 0.05) and 8y is periodic or non-periodic noise (see Figure 5).
MMM
(a)
(b)
Figure 5: (a): Periodic noise, (b): Non-periodic noise.
Set the frequency f to three types, 3, and We then execute (#t) over the range of c six times, once per each combination of frequency and periodic vs. non-periodic noise. Verification is performed with and without phase shift to. The validation set size for each result is fixed at 100. We train with several training set sizes, as shown Table
The architecture used for comparison is the architecture GDMLP and CGDMLP defined in Section 2.5. In addition, we use the architecture MLP and CMLP which are fully-connected networks ob- tained from GDMLP and CGDMLP by replacing all the elements of A(G) with respectively. We set the learning rate based on the LR range test (see Section 3.3 in Smith (2017)).
The results are summarized Table 1. Our main purpose is to extract similar features from phase shifted signals. We focused on the difference in accuracy with other methods. In the middle and the bottom Table including phase shifted signals, when the training set size per class 10, the difference in accuracy is higher than 30 %. It indicates that the proposed method can be performed with a small experimental dataset with phase shifts. On the other hand, the bottom third of Table shows that the training data does not include phase shifted signals, however the proposed method performs acceptable with the validation set which includes unknown delays in signals. This could be considered generalization performance when applied to phase shifted signals.

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Table 1: Validation set accuracy for simple periodic data.
Training Size
Training Phase Shift: None, Validation Phase Shift: None
Per Class
MLP
CMLP
GDMLP
CGDMLP
10
51.28
74.32
77.80
80.70
50
55.92
79.17
83.57
91.48
100
57.78
84.97
86.62
92.73
Training Set Size
Training Phase Shift: Exist, Validation Phase Shift: Exist
Per Class
MLP
CMLP
GDMLP
CGDMLP
I0
48.47
47.37
50.22
81.33
50
54.16 16
88.00
68.39
92.00
100
58.89
84.89
70.50
94.72
Training Set Size
Training Phase Shift: None, Validation Phase Shift: Exist
Per Class
MLP
CMLP
GDMLP
CGDMLP
10
27.06
52.83
32.89
89.89
50
26.11
86.39
33.89
91.00
100
26.33
88.28
33.89
94.39
3.2
VALIDATION WITH REAL DATA
3.2.1 TEST SETUP
In order validate model real-world dataset, Gest et al. (2019) as shown in Figure 6. The shaft is attached at both ends to brushed DC motors by compliant couplers. One motor, the driving motor, is connected to power supply and electronic speed control and is controlled by computer. The controls permit electrical current to the motor at any of five discrete voltage levels. The second motor, the damping motor, is attached array to create a variable rotary damper. In the resistor array, relays are used connect and bypass individual resisters. The possible combinations allow for sixteen discrete levels of resistance. Five different weights of different masses and sizes are attached to the shaft to simulate the shaft bending, a possible real-world anomaly. Tests are also run and data collected with no weights attached. The sensors mounted on the shaft collect triaxial acceleration and strain, and audio data is collected with an external microphone. We collect a total 400 cases of data from the above discrete variables. Each case consists of 50 data points. Each data point consists of 1.5 seconds signals. This time width is set to collect at least three rotations at the minimum rotation speed of 120 rpm. Due to the computational cost, each data point is downsampled consist of 150 points.
data obtained from the test setup
resistor
Driving
Damping
Meter
Mii O MaL
Electronics
Anmy
Figure 6: Experimental test sctup.
3.2.2 CONSTRUCT MODELS FOR THE TEST SETUP DATA
To confirm the performance of the proposed feature extraction, construct classification models to estimate the shaft bending. We use the measurements tangental acceleration, radial acceleration and strain, as input by permutation importance (see Altmann al. (2010), Breiman (2001)).

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The basic architecture is the one defined in Section 2.5, but this time there are three types of sensor signals used for input, so each feature is extracted in parallel and the outputs concatenation is imput to the multi-layer perceptron, which is classifier (sce Figure 7).
As in Section 3.1, for comparison we use GDMLP and CGDMLP, and MLP and CMLP which are fully-connected networks obtained from GDML and CGDMLP respectively.
In the preprocessing part each of the above three sensors data are converted to the graph data by the operation introduced in Section 2.2 with the following parameters. The window size ws is set to 50, which is set to include at least one period at minimum rotation speed of 120 rpm. The slide size ss be smaller than period the maximum rotation speed of 1200 rpm.
set which is We set the distance function d the correlation distance and = 0.3, namely, a pair of vertices are connected if they have strong positive linear relationship.
We set the learning rates in this instance based on the LR range test as in Section 3.1 and we use grid search optimization method for other parameters.
Prepracessing
Feature Extraction
Classification
Daca point
Graph
CGDMLP
MLP
accoloration
output
CGDMLP
concatenating
CGDMLP
strain
Convert to graph by Operation in Section
Figure 7: Learning Model for the Test Sctup Data.
3.2.3 RESULTS
Table 2 shows the resulting accuracy of each method. There are only 60 samples in each case of the experimental data. For this limited data, our proposed method achieves sufficiently higher accuracy than either of the based methods MIP and CMLP. Of the proposed methods CGDMLP in particular achieves the highest accuracy. We suggest that our methods would be superior even with limited data since relative order information on the time axis was assigned here. We further suggest that CDGMLP, especially, would be superior even when evaluating data sets that include phase shift because our proposed CGDMLP is with focus on cyclic order invariance.
Table 2: Result of each classification method.
Model MLP
Accuracy (%)
73.48
CMLP
71.62
GDMLP 80.96
CGDMLP
87.73
CONCLUSION
In this paper, we proposed a machine learning method for analyzing periodic data by admitting a graph structure to each data point and constructing a machine learning model according to the characters of the graph structure and the original data. Another point of importance is that adding certain structure to data shown to be very effective for feature extraction. The paper demonstrates experimentally the effectiveness of adding graph structure the data.
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A APPENDIX
We give a proof of the claim in Section 2.4. First we recall the claim.
Claim Let æ and x' be phase shifted data points with period Ta = Ta - - nT', and delay T(a,)))
"( P. Assume that x and æ' is more than 3 periods, namely, |æ:] = |a'| 2 kn for an integer |aa|
k 2 3. Then there exists window size ws and slide size satisfying the following condition. For graphs G and G which obtained from æ and x' by the operation in Section 2.2, there exists an integer K ((n,a") such that both of Go and ø (m,,')) (Gar) have a induced ordered subgraph ((a,))) satisfying (Sa 2 [(G=)|[//( + 1) = (V [k/((k + 1).
Proof. If = it implies that æ and x' are constant sequences. Thus we assume n 2 2.
Let n ((,,z') be the remainder when ((x,,=)) is divided by n. By the definition of periodic and delay,
have xp " ((c.x'))
follows:
20 = an))
(By the definition of delay.)
(Here, q is the quotient when (()))r) is divided by n.)
(By the definition of periodic.)
Similarly, we have x/1 Fn 2n-n/ (m,)')) Note that ((m,ar) n by the definition of the remainder. If "(a,a') 2 n2/2, then n - ( (a,x') < n/2. Thus, we can assume that there is an integer ((x,"") < n/2 such that ao.' x by replacing æ and x' if needed. Then x and x' have continuous subsequence such that [s] > (k- 1)m.
Set ws < s| (m, and let ss be a divisor of (axm')- Note that we have |s| - (m, as follows:
|s| km > (K: 1) 1)m
k n
By |s| > (k: - 1)m and (x,x0))) < n//2.
2
k
1
2
3 2
1
By k and n 2 2.
Hence we can define ws satisfying wss < |s] -)
Since ss is a divisor of (=,,=))) for each m(xx,') the vertex v, of G. is equivalent some vertex vy of Gw Let S(m.ar) be the equivalent induced subgraphs consists of the above vertices. Since the graph G. is obtained by the operation in Section 2.2 and 88 is a divisor of (=,x"") we have [V( =
/ss and (((m,x²)] = L([s} (illsl-ss//eae
ass)/ss. Since we set ws < [ -km
and is a divisor of ox,)))) we have S( 2 ( ss] km) Then we have
(S I/V k/(k + 1) as follows:
|S (mm.
(m |/VV(()
(By [VG)] = |S(m,,z') + 17 (aaxa))))
(((ee,))) + m (2xaa)
m( 188
SSee,ee)) + (((x))))/
m(xxx') / 188
(By [(()a))) 2 km a
mm + m (a)))
k
k:
Then m( /s8 is the desired integer K ((m,,)))
10


Reviews:
[{"review_id": "SkeTY92ocH", "reviewer": "AnonReviewer1", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "The goal of this work is to explore multi-sensor data modelling, with a focus on anomaly detection in machinery containing rotating shafts. Multi-sensor recordings from machinery can be phase-shifted, due to errors in the relative timing of sensors. The authors develop a method for modelling such phase-shift data. They augment phase-shift data with a graph structure which represents the relationship between sensors and they use a graph neural network with a cyclic permutation structure to enforce phase-shift invariance. Model performance is evaluated with real-world data from machinery containing a rotating shaft. Their model may be useful for other domains with phase-shifted data, such as multi-sensor medical data.\n\nAlthough this is an interesting application domain and model, I have selected weak reject. \n\nThe primary reason for this decision is that the authors do not provide sufficient comparisons to related work and models, either in the form of a literature review, or in the form of model benchmarking. This is especially problematic for a domain which will be unfamiliar to much of the machine learning community.\n\nNone of the six references in the paper address anomaly detection for temporal data (e.g. Ahrens et al. “A machine-learning phase classification scheme for anomaly detection in signals with periodic characteristics” 2019) or the extensive related literature of time series models (e.g Pope et al. Learning phase-invariant dictionaries 2013, Edwards and Lee, Using Convolutional Neural Networks to Extract Shift-Invariant Features from Unlabeled Data”, 2019), or more closely related work on shift invariant graph neural networks (e.g. Gama et al, Convolutional neural network architectures for signals supported on graphs, 2018). \n\nTo address this, I feel that the authors need to provide a related work discussion. I would also like to see some experiments comparing their model to benchmark models that have a greater chance of being competitive such as a variant of models introduced by Pope et al. 2013 or Edwards and Lee 2019 for example.\n\nMinor note: there is a typo in the definition of v_i\n\nThank you for the submission.\n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I carefully checked the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I carefully checked the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "S1g1uWIO9r", "reviewer": "AnonReviewer2", "summary": "", "questions": "", "limitations": "", "rating": 6, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper proposes a machine learning framework for periodic data. The authors note that representing input data in vector form does not encode input coordinates relationship to one. Capturing this structure can be especially important for periodic signals. The authors address this by adding graph structure to each data point to encode relative structure about each coordinate. They then apply a graph neural network to the resulting structured data. They evaluate the method on an anomaly detection in a low data setting.  \n\nThis paper would be greatly improved by an addition of a related work section. It is unclear where the novelty comes in precisely in this work because it is not very well situated within previous work on (i) anomaly detection w/ periodic signals, (ii) temporal and periodic signal processing and (iii) graph neural network approaches. Contextualized this work within these related areas would improve the clarity and readability of the work and also help frame the results. \n\nThe method is evaluated on synthetic and real datasets, comparing a couple variants of the model (one that can deal with phase shifts). Results support the main claims of the paper.\n\nIt is hard for me to assess the significance of this work since this is a very specific application of known techniques. I think the application is an important one, and also one that requires some domain knowledge, so there does appear to be a useful contribution in terms of adapting graph-based methodologies here. However, the methodology and application is outside my area of expertise.\n \nDetailed suggestions / questions / comments:\n- the ws-dimensional  vector v_i is defined as v_i = (x_{(i−1)∗ss+1}, x_{i+1}, . . . , x_{(i−1)∗ss+ws}). Is there a typo here? It's not obvious to me how the second index relates to the sequence or how this sequence is specified? Perhaps it should say  v_i = (x_{(i−1)∗ss}, x_{(i-1)*ss+1}, . . . , x_{(i−1)∗ss+ws})?\n - The authors mention alternative methods of capturing structured temporal information in the input features. For example, they suggest concatenating the original signal with the cross correlated signal. They also suggest time-frequency analysis methods (such as a Fourier transform and the wavelet transform) and applying a CNN to the time-frequency signal. They authors mention that these methods would require much more data than their graph convolution approach. I agree this is probably the case, but this would still be a useful empirical result to show the degree of data required for these alternative. \n- What are previous approaches to detecting the properties explored in this work? In addition to discussing previous approaches in a related work section, some empirical analysis comparison would help contextualize this work as well.  \n\nOverall, I think this paper is a useful application of graph-based methods. The claims are verified empirically on real and synthetic data. I think it could be significantly improved with a discussion of related work and better situating of the methods / more comparisons in the results. As a result of these significant weaknesses I'm really on the fence with my recommendation -- the work is sensible but the paper has a lot of room for improvement and I'm not quite sure its ready for publication. However, it is possible underestimated the significance/impact of this work because I am not very familiar with the topic.", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "HklFYLqOqr", "reviewer": "AnonReviewer5", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "In the paper, the authors proposed a novel method adding graph architecture for collected data points to utilize not only features but also relative information. This helps reduce time and costs to collect a huge amount of data from industrial machines and improve accuracy. Although the paper idea is very interesting when presenting a new learning model for graph data, explanations and experiments are not convincing. \n\nFor explanations, the authors did not provide sufficient related works or references to prove that the problem the paper wants to solve is important. Also, for some approaches using deep learning mentioned there are no references.\n\nFor experiments, the results presented in Table 1 are good but there are no official baselines (e.g. from some prior works) to make the comparison more reliable.\n\nBase on the arguments mentioned above, the paper is not convincing and reliable.\n\nSmall suggestion revision: \nMore analysis of prior works to show that the problem is important and need-to-solve\nThe introduction section should more references.\nThe experiments should be rigorous cause it lacks reliable baselines for comparison.  \n", "extra_scores": {"experience_assessment": "I do not know much about this area.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper."}, "extra_text": {}}, {"review_id": "BkliLUPDqS", "reviewer": "AnonReviewer4", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "This paper presents a novel architecture for extracting features for periodic signals that is sample efficient and has superior performance than previous approaches. The proposed method is based on a graph architecture that takes into account the ordering of the vertices, contrary to standard GNNs. In order to extract periodic signals, they prove that if two data points are phased shifted, then there exists a subgraph for each data point such that one is a cyclic permutation of the other. To that end, the authors present an architecture that is cyclic permutation invariant through a pooling operation on all the cycles. The proposed method is evaluated on simulated and real data.\n\nThe paper is well written and well motivated. The authors however ignore most of previous related work, there is a huge bulk of work on graph neural networks and on modelling time-series. It is important and interesting that the authors compare how the presented architecture differs from previous work.\n\nRegarding the method, the entire approach is based on two assumptions: 1) the data points are phase shifted with a period that is a multiple of T, and 2) that you know that windows size and slide size such that makes one graph a cyclic permutation of the other. How often does (1) happen in practice? How sensitive it is to the failure of such assumptions? \n\nThe results section is the weakest part of this paper. The comparison between other approaches not presented by this method is essentially just the MLP, which is the most naive baseline. The author should compare to 1Dconvs, RNNs, MLPs with fourier features, and state-of-the-art approaches tackling time-series and/or periodic signals. As I mentioned previously, it would also be important to analyze the sensitivity of the method with respect to the assumptions build upon.\n\nAt this stage, I do not think that the paper is ready for acceptance.", "extra_scores": {"experience_assessment": "I have read many papers in this area.", "review_assessment:_thoroughness_in_paper_reading": "I read the paper at least twice and used my best judgement in assessing the paper.", "review_assessment:_checking_correctness_of_experiments": "I assessed the sensibility of the experiments.", "review_assessment:_checking_correctness_of_derivations_and_theory": "I assessed the sensibility of the derivations and theory."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The reviewers all appreciated the area explored by this work but there was a consensus that it lacked a thorough presentation of existing works, as well as relevant baselines.

I encourage the authors to better position their work with respect to the existing literature for what should be a stronger submission for a future conference.
Decision: Reject