Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction



Published Oct 3, 2016
Jason Deutsch David He


Traditional data driven prognostics requires establishing explicit model equations and much prior knowledge about signal processing techniques and prognostic expertise, and therefore is limited in the age of big data. This paper presents a deep learning based approach for bearing remaining useful life (RUL) prediction with big data. This approach has the ability to automatically extract important features that can be used for RUL predictions. The presented approach is tested and validated using data collected from bearing run-to-failure tests and compared with existing PHM methods. The test results show the promising bearing RUL prediction performance of the deep learning based approach.

How to Cite

Deutsch, J., & He, D. (2016). Using Deep Learning Based Approaches for Bearing Remaining Useful Life Prediction. Annual Conference of the PHM Society, 8(1).
Abstract 1195 | PDF Downloads 526



bearing RUL prediction, deep learning, restricted boltzman machine

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