Abstract
Remaining useful life (RUL) prediction has been a topic of practical interest in many fields involving preventive intervention, including manufacturing, medicine and healthcare. While most of the conventional approaches suffer from censored failures arising and statistically circumscribed assumptions, few attempts have been made to predict RUL by developing a survival learning machine that explores the underlying relationship between time-varying prognostic variables and failure-free survival probability. This requires a purely data-driven prediction approach, devoid of any a survival model and all statistical assumptions. To this end, we propose a time-dependent survival neural network that additively estimates a latent failure risk and performs multiple binary classifications to generate prognostics of RUL-specific probability. We train the neural network by a new survival learning criterion that minimizes the censoring Kullback-Leibler divergence and guarantees monotonicity of the resulting probability. Experiments on four datasets demonstrate the great promise of our approach in real applications.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61672157, the Natural Sciences and Engineering Research Council of Canada (NSERC) under Grant No. 396097-2010, the program PAFI of Centre de Recherche du CHUS.
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Zhang, J., Wang, S., Chen, L., Guo, G., Chen, R., Vanasse, A. (2019). Time-Dependent Survival Neural Network for Remaining Useful Life Prediction. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11439. Springer, Cham. https://doi.org/10.1007/978-3-030-16148-4_34
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