Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks
<p>Three generations of neural networks.</p> "> Figure 2
<p>Reservoir computing architecture.</p> "> Figure 3
<p>Biological neuron and its model used to build artificial neural networks.</p> "> Figure 4
<p>Supervised learning.</p> "> Figure 5
<p>(<b>a</b>) Unipolar sigmoid, (<b>b</b>) bipolar sigmoid, and (<b>c</b>) hyperbolic tangent.</p> "> Figure 6
<p>Ball bearing fault detection by phase currents of the asynchronous motor. An accelerometer signal is presented for comparison.</p> "> Figure 7
<p>The photograph of the experimental test bench (<b>a</b>) and measurement equipment (<b>b</b>): (1) 0.75 kW electric motor; (2) electromagnetic brake providing the motor load; (3) cabinet with control and measurement equipment; (4) piezoelectric accelerometer for vibration recording; (5) driver and amplifier for acceleration measurement; and (6) NI PXI digital processing unit with PC-like user interface.</p> "> Figure 8
<p>Spectrograms of an electric motor with normal and faulty bearings with different defects of 0.014″ size. For the illustration, drive end accelerometer data recorded in no load condition (rotation speed 1797 RPM) were used. With no load, the defects were revealed most clearly.</p> "> Figure 9
<p>Examples of data samples in gearbox fault diagnosis dataset. Recordings from accelerometer A2.</p> "> Figure 10
<p>Healthy (<b>a</b>) and broken (<b>b</b>) gearbox vibration spectrograms for load value 90. Visualization is performed in the assumption of the 10 kS/sec rate.</p> "> Figure 11
<p>Order of operations in forming a dataset.</p> "> Figure 12
<p>ETU bearing dataset. The presented image was obtained using phase current waveforms fragments from 1498 RPM records of 5-sec length each to build each sample (the line of an image). On the right, there are the designations of the classes.</p> "> Figure 13
<p>Bearing data center dataset. The presented image was obtained using acceleration waveform fragments from 1797 RPM records of 0.05-s length to build each sample (line of an image). On the right, there are the designations of the classes; for details, see <a href="#BDCC-07-00110-t002" class="html-table">Table 2</a>.</p> "> Figure 14
<p>Comparative training time.</p> "> Figure 15
<p>Comparative testing time.</p> "> Figure 16
<p>Comparative accuracy. Dashed black lines are trend lines.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Neuron Models
2.2. Learning Methods for Neural Networks
2.2.1. Echo State Network
2.2.2. Liquid State Machine
2.3. Libraries, Programming Languages, and Hardware
- Model: ASUS ROG STRIX G15 G513IH-HN002;
- CPU: AMD Ryzen 7 4800H 8 cores 2.9-4.2 GHz;
- GPU: GeForce GTX 1650;
- RAM: DDR4 8 Gb;
- Storage device: SSD M.2 PCIe 512 Gb.
2.4. Datasets
2.4.1. ETU Bearing Dataset
Classes | Number of Points in Waveforms | If the Class Is Used in This Work | Number of Segments of 50,000 Points Length for Training | Number of Segments of 50,000 Points Length for Testing |
---|---|---|---|---|
Healthy | 6,000,000 | yes | 30 | 20 |
Broken | 6,000,000 | yes | 30 | 20 |
2.4.2. Bearing Data Center Dataset
2.4.3. Gearbox Fault Diagnosis Dataset
Classes | Number of Points in Waveform | If the Class is Used in This Work | Number of Segments of Length 10,000 Point for Training | Number of Segments of Length 10,000 Point for Testing |
---|---|---|---|---|
Healthy A1 | 106,752 | no | - | - |
Broken A1 | 105,728 | no | - | - |
Healthy A2 | 106,752 | yes | 50 | 50 |
Broken A2 | 105,728 | yes | 50 | 50 |
Healthy A3 | 106,752 | no | - | - |
Broken A3 | 105,728 | no | - | - |
Healthy A4 | 106,752 | no | - | - |
Broken A4 | 105,728 | no | - | - |
2.5. Datasets Pre-Processing
2.6. Comparative Evaluation
3. Results and Discussion
3.1. Training Time
3.2. Testing Time
3.3. Accuracy
3.4. Discussion
4. Conclusions
Dataset 1: ETU Bearing | Dataset 2: Bearing Data Center | Dataset 3: Gearbox Fault Diagnosis | |
---|---|---|---|
3rd gen overcame 2nd gen in peak accuracy | 20% | 64.47% | 11.11% |
2nd gen overcame 3rd gen in peak training time | 1047.53% | 225.45% | 723.96% |
2nd gen overcame 3rd gen in peak testing time | 1359.04% | 2497.97% | 1253.68% |
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ANN | Artificial neural network |
SNN | Spiking neural network |
LIF | Leaky integrate-and-fire |
ESN | Echo state network |
RC | Reservoir computing |
LSM | Liquid state machine |
PC | Personal computer |
CPU | Central processing unit |
GRU | Graphics processing unit |
RAM | Random access memory |
RMSE | Root-mean-square error |
SSD | Solid-state drive |
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Classes | Number of Points in Waveform | If the Class Is Used in This Work | Number of Segments of Length 2400 Points for Training | Number of Segments of Length 2400 points for Testing |
---|---|---|---|---|
Normal | 243,938 | yes | 50 | 50 |
B007 | 244,739 | yes | 50 | 50 |
B014 | 249,146 | yes | 50 | 50 |
B021 | 243,938 | yes | 50 | 50 |
IR007 | 243,938 | yes | 50 | 50 |
IR014 | 63,788 | yes | 50 | 50 |
IR021 | 244,339 | yes | 50 | 50 |
OR007 | 244,739 | yes | 50 | 50 |
OR014 | 245,140 | yes | 50 | 50 |
OR021 | 246,342 | yes | 50 | 50 |
Number of Neurons | ETU Bearing–ESN, sec | ETU Bearing– LSM, sec | Bearing Data Center– ESN, sec | Bearing Data Center–LSM, sec | Gearbox Fault Diagnosis–ESN, sec | Gearbox Fault Diagnosis–LSM, sec |
---|---|---|---|---|---|---|
50 | 47.353 | 543.39 | 114.633 | 373.073 | 21.896 | 180.414 |
100 | 48.093 | 592.891 | 149.854 | 628.645 | 24.776 | 210.002 |
150 | 61.192 | 680.536 | 198.986 | 934.213 | 27.655 | 258.775 |
200 | 76.618 | 819.313 | 243.586 | 1402.423 | 32.759 | 306.776 |
250 | 86.761 | 1023.68 | 306.056 | 1985.818 | 27.649 | 371.039 |
Number of neurons | ETU Bearing–ESN, sec | ETU Bearing– LSM, sec | Bearing Data Center–ESN, sec | Bearing Data Center–LSM, sec | Gearbox Fault Diagnosis–ESN, sec | Gearbox Fault Diagnosis–LSM, sec |
---|---|---|---|---|---|---|
50 | 10.512 | 153.374 | 10.87 | 282.399 | 11.572 | 156.648 |
100 | 11.231 | 333.218 | 16.197 | 536.306 | 14.684 | 203.9 |
150 | 14.51 | 301.042 | 23.358 | 814.411 | 16.932 | 245.516 |
200 | 19.453 | 367.4 | 33.259 | 1263.825 | 18.923 | 287.556 |
250 | 25.124 | 505.474 | 51.35 | 1694.431 | 20.647 | 341.287 |
Number of Neurons | ETU Bearing–ESN, Percent | ETU Bearing– LSM, Percent | Bearing Data Center–ESN, Percent | Bearing Data Center– LSM, Percent | Gearbox Fault Diagnosis–ESN, Percent | Gearbox Fault Diagnosis–LSM, Percent |
---|---|---|---|---|---|---|
50 | 67.5 | 85 | 51 | 100 | 81 | 100 |
100 | 75 | 80 | 50 | 100 | 86 | 100 |
150 | 72.5 | 90 | 53.8 | 100 | 82 | 100 |
200 | 72.5 | 82.5 | 60.8 | 100 | 90 | 100 |
250 | 65 | 87.5 | 55.2 | 100 | 89 | 100 |
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Kholkin, V.; Druzhina, O.; Vatnik, V.; Kulagin, M.; Karimov, T.; Butusov, D. Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks. Big Data Cogn. Comput. 2023, 7, 110. https://doi.org/10.3390/bdcc7020110
Kholkin V, Druzhina O, Vatnik V, Kulagin M, Karimov T, Butusov D. Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks. Big Data and Cognitive Computing. 2023; 7(2):110. https://doi.org/10.3390/bdcc7020110
Chicago/Turabian StyleKholkin, Vladislav, Olga Druzhina, Valerii Vatnik, Maksim Kulagin, Timur Karimov, and Denis Butusov. 2023. "Comparing Reservoir Artificial and Spiking Neural Networks in Machine Fault Detection Tasks" Big Data and Cognitive Computing 7, no. 2: 110. https://doi.org/10.3390/bdcc7020110