Prognostics in Highly Accelerated Limit Testing Using Deep Learning Data Analysis



Published Sep 4, 2023
Tadahiro Shibutani


In this study, an anomaly detection analysis of electronic components was conducted using deep learning algorithms on time-series data of voltage monitored during highly accelerated limit testing (HALT) on inverters used in automobiles and other vehicles. We demonstrated that the anomaly detection technology of time-series data using deep learning could detect equipment anomalies/failures to achieve effective data representation, improving the reliability assurance technology with HALT.

Abstract 112 | PDF Downloads 115



Prognostics, Highly Accelerated, Limit Testing, Anomaly detection, Deep Learning

Angiulli, F., Pizzuti, C. (2002) Fast Outlier Detection in High Dimensional Spaces. Proceedings of European Conference on Principles of Data Mining and Knowledge Discovery, pp. 15-26.

Basudhar, A. and Missoum, S. (2013) Reliability assessment using probabilistic support vector machines. Int. J. Reliability and Safety, Vol. 7, No. 2, pp. 156-173.

Chandora, V., Banerjee, A., Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, Vol. 41, No. 3, pp. 1-58.

Chen, H., Yao, B., Xiao, Q. (2014). The study on application of HALT for DC/DC converter. Proceedings of 2014 International Conference on Reliability, Maintainability and Safety (ICRMS), pp. 797-800.

Cho, K.-H., Jo, H.-C., Kim, E., Park, H.-A., Park, J.-H. (2020) Failure Diagnosis Method of Photovoltaic Generator Using Support Vector Machine. Journal of Electrical Engineering & Technology, Vol. 15, pp. 1669- 1680.

Hochreiter, S. and Schmidhuber, J. (1997). Long Short Term Memory. Neural Computation, Vol. 9, No. 8, pp. 1735- 1780.

Hofmeister, J. P., Vohnout, S., Mitchell, C., Heimes, F. O., Saha, S. (2010). HALT Evaluation of SJ BIST Technology for Electronic Prognostics. Proceedings of International Automatic Testing Conference (IEEE AUTOTESTCON), September 13-16, Orlando, FL, USA.

Ide, T., Lozano, A., Abe, N., Liu, Y. (2009). Proximity- Based AnomalyDetection using Sparse Structure Learning, Proceedings of the 2009 SIAM International Conference on Data Mining, pp. 97-108.

Infineon Technologies, IRFZ48VPbF Product Data Sheet, PD - 94992A, 2010.

International Electronics Comission (IEC) (2013). Methods for product accelerated testing, IEC 62506:2013.

Khan, S. and Yairi, T. (2017) A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, Vol. 107, pp. 241-265.

Ramaswamy, S., Rastogi, R. and Shim, K. (2000). Efficient algorithms for mining outliers from large data sets. Proceedings of ACM Int. Conf. on Managment of Data (SIGMOD’00), pp. 427–438.

Reddy, K. K., Sarkar, S., Venugopalan, V., Giering, M. (2016). Anomaly detection and fault disambiguation in large flight data: A multi-modal Deep Auto-encoder approach. Proceedings of Auunal Conference of the Prognostics and Health Management Society.

Sakamoto, J., Hirata, R., Shibutani, T. (2018). Potential failure mode identification of operational amplifier circuit board by using high accelerated limit test. Microelectronics Reliability, vol. 85, pp. 19-24.

Shao, H., Jiang, H., Zhao, H., Wang, F. (2017). A novel deep autoencoder feature learning method for rotating machinery fault dignosis. Mechanical Systems and Signal Processing, Vol. 95, pp. 187-204.

Sun, W., Shao, S., Zhao, R., Yan, R., Zhang, X., Chen, X. (2016). A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement, Vol. 89, pp. 171-178.

Vos, K., Peng, Z., Jenkins, C., Shajriar, M. R., Borghesani, P., Wang, W. (2022). Vibration-based anomaly detection using LSTM/SVM approaches. Mechanical Systems and Signal Processing, Vol. 169, 108752.

Xiang, S., Qu, Y., Luo, J., Pu, H., Tang, B. (2021) Multicellular LSTM-based deep learning model for aero- engine remaining useful life prediction. Reliability Engineering and System Safety, Vol. 216, 107927.

Ye, Z. and Yu, J. (2021). Health condition monitoring of machines based on long short term memory convolutional autoencoder. Applied Soft Computing, Vol. 17, 107379.

Zhao, R., Wang, J., Yan, R., Mao, K. (2016) Machine learning monitoring with LSTM network. Proceedings of 10th International Conference on Sensing Technology.
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