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Appl. Sci. 2017, 7(7), 649; doi:10.3390/app7070649

Remaining Useful Life Prediction of Hybrid Ceramic Bearings Using an Integrated Deep Learning and Particle Filter Approach

1
Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
2
College of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
*
Author to whom correspondence should be addressed.
Academic Editor: César M. A. Vasques
Received: 31 March 2017 / Revised: 7 June 2017 / Accepted: 20 June 2017 / Published: 23 June 2017
(This article belongs to the Special Issue Deep Learning Based Machine Fault Diagnosis and Prognosis)
View Full-Text   |   Download PDF [2014 KB, uploaded 29 June 2017]   |  

Abstract

Bearings are one of the most critical components in many industrial machines. Predicting remaining useful life (RUL) of bearings has been an important task for condition-based maintenance of industrial machines. One critical challenge for performing such tasks in the era of the Internet of Things and Industrial 4.0, is to automatically process massive amounts of data and accurately predict the RUL of bearings. This paper addresses the limitations of traditional data-driven prognostics, and presents a new method that integrates a deep belief network and a particle filter for RUL prediction of hybrid ceramic bearings. Real data collected from hybrid ceramic bearing run-to-failure tests were used to test and validate the integrated method. The performance of the integrated method was also compared with deep belief network and particle filter-based approaches. The validation and comparison results showed that RUL prediction performance using the integrated method was promising. View Full-Text
Keywords: prognostics; deep learning; particle filter; bearing; RUL prediction prognostics; deep learning; particle filter; bearing; RUL prediction
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Deutsch, J.; He, M.; He, D. Remaining Useful Life Prediction of Hybrid Ceramic Bearings Using an Integrated Deep Learning and Particle Filter Approach. Appl. Sci. 2017, 7, 649.

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