Have a personal or library account? Click to login
Low-Parameter Critic-Based Multivariate WGAN Model for Clogging Detection in Drives Cover

Low-Parameter Critic-Based Multivariate WGAN Model for Clogging Detection in Drives

Open Access
|May 2025

References

  1. Al-Naseem, O. A. and El-Sayed, M. A. (2013). Analysis of electrical and non-electrical causes of variable frequency drive failures. In 2013 9th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives (SDEMPED) (pp. 221–226). IEEE.
  2. Avor, J. K. and Chang, C.-K. (2019). Reliability Analysis of Application of Variable Frequency Drive on Condensate Pump in Nuclear Power Plant. Journal of International Council on Electrical Engineering, 9(1), pp. 8–14. doi: 10.1080/22348972.2018.1564548
  3. Bear, J., Prügel-Bennett, A. and Hare, J. (2024). Rethinking Deep Thinking: Stable Learning of Algorithms using Lipschitz Constraints. Proeedings of 38th Conference on Neural Information Processing Systems (NeurIPS 2024), arXiv:2410.23451. https://doi.org/10.48550/arXiv.2410.23451
  4. Beattie, A., Mulink, P., Sahoo, S., Christou, I. T., Kalalas, C., Gutierrez-Rojas, D., and Nardelli, P. H. (2022). A Robust and Explainable Data-Driven Anomaly Detection Approach for Power Electronics. In 2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) (pp. 296–301). IEEE.
  5. Bokeh Development Team (BDT) (2018). Bokeh: Python library for interactive visualization. http://www.bokeh.pydata.org.
  6. Chandola, V., Banerjee, A. and Kumar, V. (2009). Anomaly Detection: A Survey. ACM Computing Surveys (CSUR), 41(3), pp. 1–58. doi: 10.1145/1541880.1541882
  7. Chen, Y., Gao, Q. and Wang, X. (2022). Inferential Wasserstein Generative Adversarial Networks. Journal of the Royal Statistical Society Series B: Statistical Methodology, 84(1), pp. 83–113. doi: 10.1111/rssb.12476
  8. Chollet, F. (2018). Deep Learning with Python. New York, Manning Publications.
  9. Ciappa, M. and Fichtner, W. (2000). Lifetime Prediction of IGBT Modules for Traction Applications. IEEE International Reliability Physics Symposium Proceedings, San Jose, CA, USA.
  10. Dai, H., Wang, J., Zhong, Q., Chen, T., Liu, H., Zhang, X. and Lu, R. (2024). A GAN-Based Anomaly Detector Using Multi-Feature Fusion and Selection. Scientific Reports, 14, p. 52378. https://doi.org/10.1038/s41598-024-52378-9
  11. Flach, P. and Kull, M. (2015). Precision-recall-gain curves: PR analysis done right. In: Proceedings of Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, Quebec, Canada.
  12. Gómez, P. I., López, G. M. E., Mijatovic, N. and Dragičević, T. (2024). A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems. IEEE Transactions on Industrial Electronics, vol. 71, no. 10, pp. 13319–13330. doi: 10.1109/TIE.2023.3347839
  13. Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. Cambridge, US, MIT Press.
  14. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A. (2017). Improved Training of Wasserstein GANs. arXiv, arXiv:1704.00028, doi: 10.48550/arXiv.1704.00028
  15. Harris, C., Millman, K., van der Walt, S., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., Del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C. and Oliphant, T. E. (2020). Array Programming with NumPy. Nature, 585, pp. 357–362. doi: 10.1038/s41586-020-2649-2
  16. Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), pp. 90–95. doi: 10.1109/MCSE.2007.55
  17. Kang, Y., Dang, L., Yang, L., Wang, Z., Meng, Y., Li, S., Sun, Y., Wang, Y., and Dong, H. (2023). Research Progress in Failure Mechanism and Health State Evaluation Index System of Welded IGBT Power Modules. Electronics, 12(15), p. 3248. doi: 10.3390/electronics12153248
  18. Kennedy, D. (2021). Common Causes of VFD Failure [online]. Available at: https://goemc.com/2021/05/20/common-causes-of-vfd-failure/ [Accessed 12 Feb, 2025].
  19. Kumar, R., Carroll, C., Hartikainen, A. and Martin, O. (2019). ArviZ a Unified Library for Exploratory Analysis of Bayesian Models in Python. Journal of Open Source Software, 4(33), p. 1143. doi: 10.21105/joss.01143
  20. León-López, K. M., Mouret, F., Arguello, H. and Tourneret, J.-Y. (2021). Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models. IEEE Transactions on Geoscience and Remote Sensing, 60, p. 5402311. doi: 10.1109/TGRS.2021.3101127
  21. Lüer, F. and Böhm, C. (2021). Anomaly Detection using Generative Adversarial Networks: A Review. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, Singapore.
  22. Manaserh, Y. M., Tradat, M. I., Hoang, C. H., Sammakia, B., Ortega, A., Nemati, K., Seymour, M. J. (2021). Degradation of Fan Performance in Cooling Electronics: Experimental Investigation and Evaluating Numerical Techniques. International Journal of Heat and Mass Transfer, 174, p. 121291. doi: 10.1016/j.ijheatmasstransfer.2021.121291
  23. Otsuki, M., Onozawa, Y., Kanemaru, H., Seki, Y., Matsumoto, T. (2003). A Study on the Short-Circuit Capability of Field-Stop IGBTs. IEEE Transactions on Electron Devices, 50, pp. 1525–1531. doi: 10.1109/TED.2003.813505
  24. Peterson, D. (2022). Teardown: What’s Inside a Variable Frequency Drive (VFD)? Control Automation, technical article, website: https://control.com/technical-articles/teardown-whats-inside-a-vfd/, accessed on 05-05-2025
  25. Qi, S., Chen, J., Chen, P., Wen, P., Shan, W. and Xiong, L. (2023). An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series. Kyoto, Japan: Pacific-Asia Conference on Knowledge Discovery and Data Mining.
  26. Surówka, A., Tan, R., Saberi, A., and Firla, M. (2023). Performance of machine-learning-based algorithms for anomaly detection in variable frequency drives using temperature signals. In: 2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Chania, Greece, 28–31 August 2023.
  27. Surówka, A., Mikkelä, T., Kavala, A., and Firla, M. (2024a). Dual-mode hidden Markov models for smart detection of clogging in variable frequency drives. In: IEEE 21st International Power Electronics and Motion Control Conference, Pilsen, Czech Republic, 2024.
  28. Surówka, A., Tan, R., Saberi, A. and Firla, M. (2024b). Out of Bounds Anomaly Scores in Anomaly Detection in Variable Frequency Drives Using Temperature Signals. IEEE Transactions on Industry Applications, 60(5), pp. 6988–7000. doi: 10.1109/TIA.2024.3427712
  29. Tang, S., Shi, H., Song, B., Tao, Y., and Tan, S. (2025). Physically-Consistent-WGAN Based Small Sample Fault Diagnosis for Industrial Processes. Chinese Journal of Chemical Engineering, 78, pp. 163–174. doi: 10.1016/j.cjche.2024.10.028
  30. Tuli, S., Casale, G. and Jennings, N. (2022). TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data. arXiv preprint, p. 2201.07284. doi: 10.48550/arXiv.2201.07284
  31. Xia, X., Pan, X., Li, N., He, X., Ma, L., Zhang, X., and Ding, N. (2023). GAN-Based Anomaly Detection: A Review. Neurocomputing, 493, pp. 497–535. doi: 10.1016/j.neucom.2021.12.093
  32. Xu, L., Xu, K., Qin, Y., Li, Y., Huang, X., Lin, Z., and Ji, X. (2022). TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data. Applied Sciences, 12(16), 8085. doi: 10.3390/app12168085
  33. Yellamati, D., Arthu, E., James, S., Morris, G., Heydt, T., and Graf, E. (2013). Predictive Reliability Models for Variable Frequency Drives Based on Application Profiles. Orlando, FL: USA.
  34. Yinka-Banjo, C., and Ugot, OA. (2020). A review of generative adversarial networks and its application in cybersecurity. Artif Intell Rev 53, 1721–1736. doi: 10.1007/s10462-019-09717-4
  35. Zhang, C. and Yang, T. (2023). Anomaly Detection for Wind Turbines Using Long Short-Term Memory-Based Variational Autoencoder Wasserstein Generation Adversarial Network under Semi-Supervised Training. Energies, 16(19), p. 7008. doi: 10.3390/en16197008
  36. Zhang, M., Gómez, P. I., Xu, Q. and Dragicevic, T. (2023). Review of Online Learning for Control and Diagnostics of Power Converters and Drives: Algorithms, Implementations and Applications. Renewable and Sustainable Energy Reviews, 186, p. 113627. doi: 10.1016/j.rser.2023.113627
  37. Zmrhal, V. and Boháč, J. (2021). Pressure Loss of Flexible Ventilation Ducts for Residential Ventilation: Absolute Roughness and Compression Effect. Journal of Building Engineering, 44, p. 103320. doi: 10.1016/j.jobe.2021.103320
DOI: https://doi.org/10.2478/pead-2025-0008 | Journal eISSN: 2543-4292 | Journal ISSN: 2451-0262
Language: English
Page range: 125 - 139
Submitted on: Feb 26, 2025
|
Accepted on: Apr 17, 2025
|
Published on: May 17, 2025
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2025 Artur Dawid Surówka, Marcin Kosiba, Teemu Mikkelä, Asko Kavala, Marcin Firla, published by Wroclaw University of Science and Technology
This work is licensed under the Creative Commons Attribution 4.0 License.