Bearing Health Condition Prediction Using Deep Belief Network

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Published Oct 2, 2017
Guangquan Zhao Xiaoyong Liu Bin Zhang Guohui Zhang Guangxing Niu Cong Hu

Abstract

Bearings play a critical role in maintaining safety and reliability of rotating machinery. Bearings health condition prediction aims to prevent unexpected failures and minimize overall maintenance costs since it provides decision making information for condition-based maintenance. This paper proposes a Deep Belief Network (DBN)-based data-driven health condition prediction method for bearings. In this prediction method, a DBN is used as the predictor, which includes stacked RBMs and regression output. Our main contributions include development of a deep leaning-based data-driven prognosis solution that does not rely on explicit model equations and prognostic expertise, and providing comprehensive prediction results on five representative run-to-failure bearings. The IEEE PHM 2012 challenge dataset is used to demonstrate the effectiveness of the proposed method, and the results are compared with two existing methods. The results show that the proposed method has promising performance in terms of short-term health condition prediction and remaining useful life prediction for bearings.

How to Cite

Zhao, G., Liu, X., Zhang, B., Zhang, G., Niu, G., & Hu, C. (2017). Bearing Health Condition Prediction Using Deep Belief Network. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2484
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Keywords

bearings, Condition prediction, Deep Belief Network

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Section
Technical Research Papers