RUL Estimation for Package Failure of Power Electronic Devices Using Integral Mean of Precursor Signal

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published Sep 4, 2023
Zhonghai Lu Chao Guo Pol Ghesquiere Kai Kriegel Gerhard Mitic

Abstract

Package failure like bond-wire lift-off is one common cause of failure for discrete power electronic devices such as Schottky diodes. To estimate their Remaining Useful Lifetime (RUL), forward voltage drop is often used as the precursor signal. Prior researches use the direct forward voltage feature and its derived features to construct neural networks for RUL prediction. These features can reflect the instant health condition of the device in the current time or time window, but miss to represent the accumulated effect of gradually decreasing health conditions. In the paper, we formulate the integral mean feature of forward voltage drop and propose to use it to conduct RUL estimation. By the integral mean feature, we are able to capture the device's health condition in an accumulated fashion. Our experiments show that our approach is superior in generalization performance when compared to the forward voltage feature and its statistical features based neural networks for RUL estimation.

Abstract 191 | PDF Downloads 158

##plugins.themes.bootstrap3.article.details##

Keywords

Remaining Useful Lifetime, Prognostics and Health Management, Power Electronic Devices

References
Celaya, J. R., Wysocki, P., & Goebel, K. (2009). IGBT Accelerated Aging Data Set. (NASA Prognostics Data Repository, NASA Ames Research Center, Moffett Field, CA)

Chao, M. A., Kulkarni, C., Goebel, K., & Fink, O. (2022). Fusing physics-based and deep learning models for prognostics. Reliability Engineering & System Safety, 217, 107961.

Ciappa, M. (2002). Selected failure mechanisms of modern power modules. Microelectronics Reliability, 42(4), 653-667. doi: https://doi.org/10.1016/S0026- 2714(02)00042-2

Ciappa, M. (2008). Lifetime modeling and prediction of power devices. In 5th international conference on integrated power electronics systems (pp. 1–9).

Hanif, A., Yu, Y., DeVoto, D., & Khan, F. (2018). A comprehensive review toward the state-of-the-art in failure and lifetime predictions of power electronic devices. IEEE Transactions on Power Electronics, 34(5), 4729–4746.

He, C., Yu, W., Zheng, Y., & Gong, W. (2021). Machine learning based prognostics for predicting remaining useful life of IGBT – NASA IGBT accelerated ageing case study. In 2021 ieee 5th information technology, networking, electronic and automation control conference (itnec) (Vol. 5, pp. 1357–1361).

Held, M., Jacob, P., Nicoletti, G., Scacco, P., & Poech, M.-H. (1997). Fast power cycling test of IGBT modules in traction application. In Proceedings of second international conference on power electronics and drive systems (Vol. 1, p. 425-430 vol.1). doi: 10.1109/PEDS.1997.618742

International Electro technical Commission. (2004). Semiconductor devices mechanical and climatic test methods part 34: Power cycling. International standard IEC 60749-34.

Ismail, A., Saidi, L., Sayadi, M., & Benbouzid, M. (2020). A new data-driven approach for power IGBT remaining useful life estimation based on feature reduction technique and neural network. Electronics, 9(10), 1571.

Li, W., Wang, B., Liu, J., Zhang, G., & Wang, J. (2020, November). IGBT aging monitoring and remaining lifetime prediction based on long short-term memory (LSTM) networks. Microelectronics Reliability, 114.

Li, X., Zhang, W., & Ding, Q. (2019). Deep learningbased remaining useful life estimation of bearings using multi-scale feature extraction. Reliability engineering & system safety, 182, 208–218.

Lu, Y., & Christou, A. (2019). Prognostics of IGBT modules based on the approach of particle filtering. Microelectronics Reliability, 92, 96-105. doi: https://doi.org/10.1016/j.microrel.2018.11.012

Manson, S. S., & Dolan, T. J. (1966, 12). Thermal stress and low cycle fatigue. Journal of Applied Mechanics, 33(4), 957-957. doi: 10.1115/1.3625225

Otto, A., & Rzepka, S. (2019). Lifetime modelling of discrete power electronic devices for automotive applications. In Ame 2019 - automotive meets electronics; 10th gmm-symposium (p. 1-6).

Salameh, A., & Hosseinalibeiki, H. (2022, 07). Application of deep neural network in fatigue lifetime estimation of solder joint in electronic devices under vibration loading. Welding in the World, 66. doi: 10.1007/s40194- 022-01349-7

Thoben, M., & Reiter, T. (2021, May). Guideline for lifetime calculation of power modules annex of ECPE GUIDELINE AQG 324. In (p. Annex II.D 1-14).

Zhao, S., Blaabjerg, F., & Wang, H. (2021). An overview of artificial intelligence applications for power electronics. IEEE Transactions on Power Electronics, 36(4), 4633-4658.

Zhao, S., Peng, Y., Zhang, Y., & Wang, H. (2022). Parameter Estimation of Power Electronic Converters With Physics-Informed Machine Learning. IEEE Transactions on Power Electronics, 37(10), 11567-11578.
Section
Special Session Papers