NDLI: A Fault Diagnosis Method of Rolling Bearings Using Empirical Mode Decomposition and Hidden Markov Model

Content Provider	IEEE Xplore Digital Library
Author	Bin Wu Changjian Feng Minjie Wang
Copyright Year	2006
Description	Author affiliation: Sch. of Mech. Eng., Dalian Univ. of Technol. (Bin Wu; Changjian Feng; Minjie Wang)
Abstract	This paper describes a new approach to detect localized rolling bearing defects based on empirical mode decomposition (EMD) and hidden Markov model (HMM). In view of the non-stationary characteristics of bearing fault vibration signals, using EMD method, the original non-stationary vibration signal can be decomposed into a finite number of stationary signals. The stationary signal adapts itself better to the conditions of fault characteristic parameter based on power spectrum analysis and also show bearing fault characteristics clearly. By setting envelope-singles fault-characteristic parameters of each main stationary signal to train HMM, this study also presents a method of pattern recognition for bearing fault diagnosis using HMM. Experimental results show that (1) the approach has successful bearing fault detection rates as high as 98% for every single fault; (2) although fault styles sometimes are confusing, the approach proves better at recognizing combinations of these faults
Starting Page	5697
Ending Page	5701
File Size	220624
Page Count	5
File Format	PDF
ISBN	1424403324
DOI	10.1109/WCICA.2006.1714166
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-06-21
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Rolling bearings Hidden Markov models Vibrations Mechanical engineering Paper technology Educational institutions Signal analysis Pattern recognition Fault detection fault diagnosis EMD HMM rolling bearing
Content Type	Text
Resource Type	Article

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