Knowing the state-of-health (SOH) of equipment, device or component is very essential for the secure and dependable operation of a system. Electrolytic capacitors are undoubtedly one of the essential components of power supply modules used in aerial and underwater vehicles, and every equipment requires a conversion of voltage from one level to another. This has encouraged research into the components of the power supply used in such systems of which electrolytic capacitor is of interest in this study. In this paper, we explore a new approach to implementing prognostics and health management (PHM) for electrolytic capacitors and propose a method of estimating the SOH leading to the prediction of the remaining useful life (RUL). This is accomplished by using a bidirectional long short-term memory (BLSTM) network to capture the degradation trends. We demonstrate the power and leverage that this method brings to bear in encoding time-domain dependencies in accurately estimating the SOH bereft of state models as employed in traditional methods. We validate the proposed approach using capacitor data recorded at different electrical over-stress accelerated aging conditions. The proposed method surpasses other existing methods in RUL prediction as indicated by the error and relative accuracy.
2. Xia, M., X. Zheng, M. Imran, and M. Shoaib, "Data-driven prognosis method using hybrid deep recurrent neural network," Appl. Soft Comput., Vol. 93, 106351, 2020.
3. Du, P., J. Wang, W. Yang, and T. Niu, "A novel hybrid model for short-term wind power forecasting," Appl. Soft Comput., Vol. 80, 93-106, 2019.
4. Rigamonti, M., P. Baraldi, E. Zio, D. Astigarraga, and A. Galarza, "Particle filter-based prognostics for an electrolytic capacitor working in variable operating conditions," IEEE Trans. Power Electron., Vol. 31, No. 2, 1567-1575, 2015.
5. Celaya, J. R., C. S. Kulkarni, S. Saha, G. Biswas, and K. Goebel, "Accelerated aging in electrolytic capacitors for prognostics," Proceedings of the Annual Reliability and Maintainability Symposium, 1-6, 2012.
6. Renwick, J., C. S. Kulkarni, and J. R. Celaya, "Analysis of electrolytic capacitor degradation under electrical overstress for prognostic studies," Proceedings of the Annual Conference of the Prognostics and Health Management Society, Vol. 6, 2015.
7. Jamshidi, M. B. and N. Alibeigi, "Neuro-fuzzy system identification for remaining useful life of electrolytic capacitors," 2017 2nd International Conference on System Reliability and Safety (ICSRS), 227-231, 2017.
8. Lee, K.-W., M. Kim, J. Yoon, S. Bin Lee, and J.-Y. Yoo, "Condition monitoring of DC-link electrolytic capacitors in adjustable-speed drives," IEEE Trans. Ind. Appl., Vol. 44, No. 5, 1606-1613, 2008.
9. Qin, Q., S. Zhao, S. Chen, D. Huang, and J. Liang, "Adaptive and robust prediction for the remaining useful life of electrolytic capacitors," Microelectron. Reliab., Vol. 87, 64-74, 2018.
10. Garcia-Garcia, A., S. Orts-Escolano, S. Oprea, V. Villena-Martinez, P. Martinez-Gonzalez, and J. Garcia-Rodriguez, "A survey on deep learning techniques for image and video semantic segmentation," Appl. Soft Comput., Vol. 70, 41-65, 2018.
11. Chen, X., Z. Wei, M. Li, and P. Rocca, "A review of deep learning approaches for inverse scattering problems (invited review)," Progress In Electromagnetics Research, Vol. 167, 67-81, 2020.
12. Liu, C., M.-H. Yang, and X.-W. Sun, "Towards robust human millimeter wave imaging inspection system in real time with deep learning," Progress In Electromagnetics Research, Vol. 161, 87-100, 2018.
13. Lin, Y., X. Li, and Y. Hu, "Deep diagnostics and prognostics: An integrated hierarchical learning framework in PHM applications," Appl. Soft Comput., Vol. 72, 555-564, 2018.
14. Zhang, L., J. Lin, B. Liu, Z. Zhang, X. Yan, and M. Wei, "A review on deep learning applications in prognostics and health management," IEEE Access, Vol. 7, 162415-162438, 2019.
15. Cabanas, M. F., F. Pedrayes Gonzalez, M. G. Melero, C. H. Rojas Garcıa, G. A. Orcajo, J. M. Cano Rodrıguez, and J. G. Norniell, "Insulation fault diagnosis in high voltage power transformers by means of leakage flux analysis," Progress In Electromagnetics Research, Vol. 114, 211-234, 2011.
16. Faiz, J. and B. M. Ebrahimi, "Mixed fault diagnosis in three-phase squirrel-cage induction motor using analysis of air-gap magnetic field," Progress In Electromagnetics Research, Vol. 64, 239-255, 2006.
17. Vasan, A. S. S., B. Long, and M. Pecht, "Diagnostics and prognostics method for analog electronic circuits," IEEE Trans. Ind. Electron., Vol. 60, No. 11, 5277-5291, 2012.
18. Venet, P., F. Perisse, M. H. El-Husseini, and G. Rojat, "Realization of a smart electrolytic capacitor circuit," IEEE Ind. Appl. Mag., Vol. 8, No. 1, 16-20, 2002.
19. Kulkarni, C., G. Biswas, J. Celaya, and K. Goebel, "Prognostic techniques for capacitor degradation and health monitoring," The Maintenance & Reliability Conference, MARCON, 2011.
20. Gupta, A., O. P. Yadav, D. DeVoto, and J. Major, "A review of degradation behavior and modeling of capacitors," ASME 2018 International Technical Conference and Exhibition on Packaging and Integration of Electronic and Photonic Microsystems, Vol. 51920, 2018.
21. Leite, A. V. T., H. J. A. Teixeira, A. J. Marques Cardoso, and R. M. Esteves Araujo, "A simple ESR identification methodology for electrolytic capacitors condition monitoring," Proceedings of the 20th International Congress on Condition Monitoring and Diagnostic Engineering Management, COMADEM’07, 75-84, 2007.
22. Hochreiter, S. and J. Schmidhuber, "Long short-term memory," Neural Comput., Vol. 9, No. 8, 1735-1780, 1997.
23. Pascanu, R., T. Mikolov, and Y. Bengio, "On the difficulty of training recurrent neural networks," International Conference on Machine Learning, 1310-1318, 2013.
24. Huang, C.-G., H.-Z. Huang, and Y.-F. Li, "A bidirectional LSTM prognostics method under multiple operational conditions," IEEE Trans. Ind. Electron., Vol. 66, No. 11, 8792-8802, 2019.
25. Huang, C.-G., X.-Y. Li, H.-Z. Huang, and Y.-F. Li, "Fault prognosis of engineered systems: A deep learning perspective," 2019 Annual Reliability and Maintainability Symposium (RAMS), 1-7, 2019.
26. Merity, S., N. S. Keskar, and R. Socher, "Regularizing and optimizing LSTM language models," International Conference on Learning Representations, 2018.
27. Chen, J., H. Jing, Y. Chang, and Q. Liu, "Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process," Reliab. Eng. Syst. Saf., Vol. 185, 372-382, 2019.
28. Srivastava, N., G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, "Dropout: A simple way to prevent neural networks from overfitting," J. Mach. Learn. Res., Vol. 15, No. 1, 1929-1958, 2014.
29. Zhang, Y., R. Xiong, H. He, and M. G. Pecht, "Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries," IEEE Trans. Veh. Technol., Vol. 67, No. 7, 5695-5705, 2018.
30. Kulkarni, C. S., J. R. Celaya, G. Biswas, and K. Goebel, "Prognostics of power electronics, methods and validation experiments," 2012 IEEE AUTOTESTCON Proceedings, 194-199, 2012.
31. Kulkarni, C., G. Biswas, X. Koutsoukos, J. Celaya, and K. Goebel, "Integrated diagnostic/ prognostic experimental setup for capacitor degradation and health monitoring," 2010 IEEE AUTOTESTCON, 1-7, 2010.
32. Shatnawi, A., G. Al-Bdour, R. Al-Qurran, and M. Al-Ayyoub, "A comparative study of open source deep learning frameworks," 2018 9th International Conference on Information and Communication Systems (ICICS), 72-77, 2018.