Next Article in Journal
Higher‐Order Interactions in Quantum Optomechanics: Revisiting Theoretical Foundations
Next Article in Special Issue
Planetary Gearbox Fault diagnosis via Joint Amplitude and Frequency Demodulation Analysis Based on Variational Mode Decomposition
Previous Article in Journal
Early Diagnosis of Dementia from Clinical Data by Machine Learning Techniques
Previous Article in Special Issue
Detection of Eccentricity Faults in Five-Phase Ferrite-PM Assisted Synchronous Reluctance Machines
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(7), 649;

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

Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
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)
Full-Text   |   PDF [2014 KB, uploaded 29 June 2017]   |  


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

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top