This paper discusses recent research on the condition mon- itoring (CM) approach for aluminium electrolytic capacitors (AEC) used in power electronics equipment such as switched- mode power supplies (SMPS). Capacitors are identified as the most critical component with the highest percentage of failure in AEC. CM offers a better paradigm for AEC due to its long- lasting ability (endurance). This study proposes accelerated life testing through electrical stress and long-term frequency testing for the AEC component. An experiment test bench was set up to monitor the critical electrical parameters such as dissipation factor (D), equivalent series resistance (ESR), capacitance (Cp), and impedance (Z), which serve as health indicators (HI) for the evaluation of the AECs. Time-domain features were extracted from the measured data, and the best features were selected using the correlation-based technique. This research contributes to developing a cost-effective CM approach for AECs used in power electronics equipment, which can reduce downtime and maintenance costs.
Condition monitoring, aluminium electrolytic capacitors, health index, maintenance costs
Amaral, A. M. R., & Cardoso, A. J. M. (2007). Using newton-raphson method to estimate the condition of aluminum electrolytic capacitors. In (p. 827-832). doi: 10.1109/ISIE.2007.4374704.
Bayo, K. A., & Jang-Wook, H. (2022). Towards data-driven fault diagnostics framework for smps-aec using supervised learning algorithms. Electronics, 11(16). doi: 10.3390/electronics11162492
Bhargava C., B. V., & Y., S. (2018). Condition monitoring of aluminium electrolytic capacitors using accelerated life testing: A comparison. International Journal of Quality and Reliability Management, 35(8), 1671-1682. doi: https://doi.org/10.1108/IJQRM-06-2017-0115
Cachada, A., Barbosa, J., Leit˜no, P., Gcraldcs, C. A., Deusdado, L., Costa, J., . . . Romero, L. (2018). Maintenance 4.0: Intelligent and predictive mainte-nance system architecture. In 2018 ieee 23rd international conference on emerging technologies and factory automation (etfa) (Vol. 1, p. 139-146). doi: 10.1109/ETFA.2018.8502489
Duan, C., & Chen, P. (2023). Adaptive maintenance scheme for degrading devices with dynamic conditions and random failures. IEEE Transactions on Industrial Informatics, 19(3), 2508-2519. doi: doi: 10.1109/TII.2022.3182789.
Fei, X., Bin, C., Jun, C., & Shunhua, H. (2020). Literature review: Framework of prognostic health management for airline predictive maintenance. In 2020 chinese control and decision conference (ccdc) (p. 5112-5117). doi: 10.1109/CCDC49329.2020.9164546
Jami Torki, A. S., Charles Joubert. (2023). Electrolytic capacitor: Properties and operation. Journal of Energy Storage, 58(106330). doi: https://doi.org/10.1016/j.est.2022.106330
Jedtberg H., L. M., Buticchi G. (2017). A method for hotspot temperature estimation of aluminium electrolytic capacitors. In (p. 3235-3241). doi: 10.1109/ECCE.2017.8096586
Kareem A.B., H. J.-W. (2022). A feature engineering assisted cm technology for smps output aluminium electrolytic capacitors (aec) considering d-esr-q-z parameters. MDPI Processes, 10(1091). doi: https://doi.org/10.3390/pr10061091
Kushwaha, P., Buckchash, H., & Raman, B. (2017). Anomaly-based intrusion detection using filter-based feature selection on kdd-cup 99. In Tencon 2017 - 2017 ieee region 10 conference (p. 839-844). doi: 10.1109/TENCON.2017.8227975
R. Cousseau, E. M., N. Patin, & Idkhajine, L. (2013). A methodology for studying aluminium electrolytic capacitors wear-out in automotive cases. In (p. 1-10). doi: 10.1109/EPE.2013.6631846.
Shahraki, A. F., Al-Dahidi, S., Taleqani, A. R., & Yadav, O. P. (2023). Using lstm neural network to predict the remaining useful life of electrolytic capacitors in dynamic operating conditions. Journal of Risk and Reliability, 237(1), 16-28. doi: https://doi.org/10.1177/1748006X221087503
Urbanowicz, R. J., Meeker, M., La Cava, W., Olson, R. S., & Moore, J. H. (2018). Relief-based feature selection: Introduction and review. Journal of Biomedical Informatics, 85, 189-203. doi: https://doi.org/10.1016/j.jbi.2018.07.014
Wang, H., & Blaabjerg, F. (2021). Power electronics reliability: State of the art and outlook. IEEE Journal of Emerging and Selected Topics in Power Electronics, 9(6), 6476-6493. doi: 10.1109/JESTPE.2020.3037161
Zonta, T., da Costa, C. A., da Rosa Righi, R., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the industry 4.0: A systematic literature review. Computers Industrial Engineering, 150, 106889.
This work is licensed under a Creative Commons Attribution 3.0 Unported License.