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

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. Al-NaseemO. A. El-SayedM. 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) 221 226 IEEE Search in Google Scholar

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 AvorJ. K. ChangC.-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 8 14 10.1080/22348972.2018.1564548 Open DOISearch in Google Scholar

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 BearJ. Prügel-BennettA. HareJ. 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 Search in Google Scholar

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. BeattieA. MulinkP. SahooS. ChristouI. T. KalalasC. Gutierrez-RojasD. NardelliP. 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) 296 301 IEEE Search in Google Scholar

Bokeh Development Team (BDT) (2018). Bokeh: Python library for interactive visualization. http://www.bokeh.pydata.org. Bokeh Development Team (BDT) 2018 Bokeh: Python library for interactive visualization http://www.bokeh.pydata.org. Search in Google Scholar

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 ChandolaV. BanerjeeA. KumarV. 2009 Anomaly Detection: A Survey ACM Computing Surveys (CSUR) 41 3 1 58 10.1145/1541880.1541882 Open DOISearch in Google Scholar

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 ChenY. GaoQ. WangX. 2022 Inferential Wasserstein Generative Adversarial Networks Journal of the Royal Statistical Society Series B: Statistical Methodology 84 1 83 113 10.1111/rssb.12476 Open DOISearch in Google Scholar

Chollet, F. (2018). Deep Learning with Python. New York, Manning Publications. CholletF. 2018 Deep Learning with Python New York Manning Publications Search in Google Scholar

Ciappa, M. and Fichtner, W. (2000). Lifetime Prediction of IGBT Modules for Traction Applications. IEEE International Reliability Physics Symposium Proceedings, San Jose, CA, USA. CiappaM. FichtnerW. 2000 Lifetime Prediction of IGBT Modules for Traction Applications IEEE International Reliability Physics Symposium Proceedings San Jose, CA, USA Search in Google Scholar

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 DaiH. WangJ. ZhongQ. ChenT. LiuH. ZhangX. LuR. 2024 A GAN-Based Anomaly Detector Using Multi-Feature Fusion and Selection Scientific Reports 14 52378 https://doi.org/10.1038/s41598-024-52378-9 Search in Google Scholar

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. FlachP. KullM. 2015 Precision-recall-gain curves: PR analysis done right In: Proceedings of Advances in Neural Information Processing Systems 28 (NIPS 2015) Montreal, Quebec, Canada Search in Google Scholar

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 GómezP. I. LópezG. M. E. MijatovicN. DragičevićT. 2024 A Self-Commissioning Edge Computing Method for Data-Driven Anomaly Detection in Power Electronic Systems IEEE Transactions on Industrial Electronics 71 10 13319 13330 10.1109/TIE.2023.3347839 Open DOISearch in Google Scholar

Goodfellow, I., Bengio, Y. and Courville, A. (2016). Deep Learning. Cambridge, US, MIT Press. GoodfellowI. BengioY. CourvilleA. 2016 Deep Learning Cambridge, US MIT Press Search in Google Scholar

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 GulrajaniI. AhmedF. ArjovskyM. DumoulinV. CourvilleA. 2017 Improved Training of Wasserstein GANs arXiv, arXiv:1704.00028, 10.48550/arXiv.1704.00028 Open DOISearch in Google Scholar

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 HarrisC. MillmanK. van der WaltS. GommersR. VirtanenP. CournapeauD. WieserE. TaylorJ. BergS. SmithN. J. KernR. PicusM. HoyerS. van KerkwijkM. H. BrettM. HaldaneA. Del RíoJ. F. WiebeM. PetersonP. Gérard-MarchantP. SheppardK. ReddyT. WeckesserW. AbbasiH. GohlkeC. OliphantT. E. 2020 Array Programming with NumPy Nature 585 357 362 10.1038/s41586-020-2649-2 Open DOISearch in Google Scholar

Hunter, J. D. (2007). Matplotlib: A 2D Graphics Environment. Computing in Science & Engineering, 9(3), pp. 90–95. doi: 10.1109/MCSE.2007.55 HunterJ. D. 2007 Matplotlib: A 2D Graphics Environment Computing in Science & Engineering 9 3 90 95 10.1109/MCSE.2007.55 Open DOISearch in Google Scholar

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 KangY. DangL. YangL. WangZ. MengY. LiS. SunY. WangY. DongH. 2023 Research Progress in Failure Mechanism and Health State Evaluation Index System of Welded IGBT Power Modules Electronics 12 15 3248 10.3390/electronics12153248 Open DOISearch in Google Scholar

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]. KennedyD. 2021 Common Causes of VFD Failure [online]. Available at: https://goemc.com/2021/05/20/common-causes-of-vfd-failure/ [Accessed 12 Feb, 2025]. Search in Google Scholar

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 KumarR. CarrollC. HartikainenA. MartinO. 2019 ArviZ a Unified Library for Exploratory Analysis of Bayesian Models in Python Journal of Open Source Software 4 33 1143 10.21105/joss.01143 Open DOISearch in Google Scholar

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 León-LópezK. M. MouretF. ArguelloH. TourneretJ.-Y. 2021 Anomaly Detection and Classification in Multispectral Time Series Based on Hidden Markov Models IEEE Transactions on Geoscience and Remote Sensing 60 5402311 10.1109/TGRS.2021.3101127 Open DOISearch in Google Scholar

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. LüerF. BöhmC. 2021 Anomaly Detection using Generative Adversarial Networks: A Review In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining Singapore Search in Google Scholar

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 ManaserhY. M. TradatM. I. HoangC. H. SammakiaB. OrtegaA. NematiK. SeymourM. J. 2021 Degradation of Fan Performance in Cooling Electronics: Experimental Investigation and Evaluating Numerical Techniques International Journal of Heat and Mass Transfer 174 121291 10.1016/j.ijheatmasstransfer.2021.121291 Open DOISearch in Google Scholar

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 OtsukiM. OnozawaY. KanemaruH. SekiY. MatsumotoT. 2003 A Study on the Short-Circuit Capability of Field-Stop IGBTs IEEE Transactions on Electron Devices 50 1525 1531 10.1109/TED.2003.813505 Open DOISearch in Google Scholar

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 PetersonD. 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 Search in Google Scholar

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. QiS. ChenJ. ChenP. WenP. ShanW. XiongL. 2023 An Effective WGAN-Based Anomaly Detection Model for IoT Multivariate Time Series Kyoto, Japan Pacific-Asia Conference on Knowledge Discovery and Data Mining Search in Google Scholar

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. SurówkaA. TanR. SaberiA. FirlaM. 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 Search in Google Scholar

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. SurówkaA. MikkeläT. KavalaA. FirlaM. 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 Search in Google Scholar

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 SurówkaA. TanR. SaberiA. FirlaM. 2024b Out of Bounds Anomaly Scores in Anomaly Detection in Variable Frequency Drives Using Temperature Signals IEEE Transactions on Industry Applications 60 5 6988 7000 10.1109/TIA.2024.3427712 Open DOISearch in Google Scholar

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 TangS. ShiH. SongB. TaoY. TanS. 2025 Physically-Consistent-WGAN Based Small Sample Fault Diagnosis for Industrial Processes Chinese Journal of Chemical Engineering 78 163 174 10.1016/j.cjche.2024.10.028 Open DOISearch in Google Scholar

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 TuliS. CasaleG. JenningsN. 2022 TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data arXiv preprint 2201.07284 10.48550/arXiv.2201.07284 Open DOISearch in Google Scholar

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 XiaX. PanX. LiN. HeX. MaL. ZhangX. DingN. 2023 GAN-Based Anomaly Detection: A Review Neurocomputing 493 497 535 10.1016/j.neucom.2021.12.093 Open DOISearch in Google Scholar

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 XuL. XuK. QinY. LiY. HuangX. LinZ. JiX. 2022 TGAN-AD: Transformer-Based GAN for Anomaly Detection of Time Series Data Applied Sciences 12 16 8085 10.3390/app12168085 Open DOISearch in Google Scholar

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. YellamatiD. ArthuE. JamesS. MorrisG. HeydtT. GrafE. 2013 Predictive Reliability Models for Variable Frequency Drives Based on Application Profiles Orlando, FL: USA Search in Google Scholar

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 Yinka-BanjoC. UgotOA. 2020 A review of generative adversarial networks and its application in cybersecurity Artif Intell Rev 53 1721 1736 10.1007/s10462-019-09717-4 Open DOISearch in Google Scholar

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 ZhangC. YangT. 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 7008 10.3390/en16197008 Open DOISearch in Google Scholar

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 ZhangM. GómezP. I. XuQ. DragicevicT. 2023 Review of Online Learning for Control and Diagnostics of Power Converters and Drives: Algorithms, Implementations and Applications Renewable and Sustainable Energy Reviews 186 113627 10.1016/j.rser.2023.113627 Open DOISearch in Google Scholar

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 ZmrhalV. BoháčJ. 2021 Pressure Loss of Flexible Ventilation Ducts for Residential Ventilation: Absolute Roughness and Compression Effect Journal of Building Engineering 44 103320 10.1016/j.jobe.2021.103320 Open DOISearch in Google Scholar