Vol. 109

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2021-02-03

A Bidirectional LSTM-Based Prognostication of Electrolytic Capacitor

By Delanyo Kwame Bensah Kulevome, Hong Wang, and Xuegang Wang
Progress In Electromagnetics Research C, Vol. 109, 139-152, 2021
doi:10.2528/PIERC20120201

Abstract

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.

Citation


Delanyo Kwame Bensah Kulevome, Hong Wang, and Xuegang Wang, "A Bidirectional LSTM-Based Prognostication of Electrolytic Capacitor," Progress In Electromagnetics Research C, Vol. 109, 139-152, 2021.
doi:10.2528/PIERC20120201
http://www.jpier.org/PIERC/pier.php?paper=20120201

References


    1. Downey, A., Y.-H. Lui, C. Hu, S. Laflamme, and S. Hu, "Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds," Reliab. Eng. Syst. Saf., Vol. 182, 1-12, 2019.
    doi:10.1016/j.ress.2018.09.018

    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.
    doi:10.1016/j.asoc.2020.106351

    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.
    doi:10.1016/j.asoc.2019.03.035

    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.
    doi:10.1109/TPEL.2015.2418198

    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.
    doi:10.1109/ICSRS.2017.8272826

    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.
    doi:10.1109/TIA.2008.2002277

    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.
    doi:10.1016/j.microrel.2018.05.020

    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.
    doi:10.1016/j.asoc.2018.05.018

    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.
    doi:10.2528/PIER20030705

    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.
    doi:10.2528/PIER18012601

    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.
    doi:10.1016/j.asoc.2018.01.036

    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.
    doi:10.1109/ACCESS.2019.2950985

    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.
    doi:10.2528/PIER11010302

    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.
    doi:10.2528/PIER06080201

    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.
    doi:10.1109/TIE.2012.2224074

    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.
    doi:10.1109/2943.974353

    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.
    doi:10.1162/neco.1997.9.8.1735

    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.
    doi:10.1109/TIE.2019.2891463

    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.
    doi:10.1016/j.ress.2019.01.006

    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.
    doi:10.1109/TVT.2018.2805189

    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.
    doi:10.1109/AUTEST.2012.6334578

    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.
    doi:10.1109/IACS.2018.8355444