The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum

The rolling element bearing is a key part in many mechanical equipment. The accurate and timely diagnosis of its faults is critcal for predictive maintenance. Vibration signals from a defective bearing with a localized fault contain a series of impulsive responses, which result from the impacts of the defective part(s) with other elements and inevitable noise. Most researches carried out have focused on fault location identification. However, limited work has been reported for fault severity estimation, which is critical to make decision for maintenance actions. To improve current diagnostic capability,. This paper presents a new approach to detection and diagnosis of bearing fault severity based on vibration analysis using modulation signal bispectrum (MSB). It models the vibration sources from bearing defects as an impact process with constant size but three different lengths corresponding to outer race fault, inner race fault and roller fault, respectively. The results shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for accurate fault detection and diagnosis for different bearing fault severity.

[1]  Tomasz Barszcz,et al.  Fault Detection Enhancement in Rolling Element Bearings Using the Minimum Entropy Deconvolution , 2012 .

[2]  Keheng Zhu,et al.  Fault Diagnosis of Rolling Bearings Based on IMF Envelope Sample Entropy and Support Vector Machine , 2013 .

[3]  Long Zhang,et al.  Bearing fault diagnosis using multi-scale entropy and adaptive neuro-fuzzy inference , 2010, Expert Syst. Appl..

[4]  Teruo Igarashi,et al.  Studies on the Vibration and Sound of Defective Rolling Bearings : First Report : Vibration of Ball Bearings with One Defect , 1982 .

[5]  P. D. McFadden,et al.  Model for the vibration produced by a single point defect in a rolling element bearing , 1984 .

[6]  Fengshou Gu,et al.  A Study of Motor Bearing Fault Diagnosis using Modulation Signal Bispectrum Analysis of Motor Current Signals , 2013 .

[7]  Jianping Xuan,et al.  Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing , 2009, Expert Syst. Appl..

[8]  Xudong Zhao,et al.  Reliable fault diagnosis method using ensemble fuzzy ARTMAP based on improved Bayesian belief method , 2014, Neurocomputing.

[9]  N. Tandon,et al.  A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings , 1999 .

[10]  Yaguo Lei,et al.  A new approach to intelligent fault diagnosis of rotating machinery , 2008, Expert Syst. Appl..

[11]  N. Tandon,et al.  An analytical model for the prediction of the vibration response of rolling element bearings due to a localized defect , 1997 .

[12]  Kenneth A. Loparo,et al.  A new bearing fault detection and diagnosis scheme based on hidden Markov modeling of vibration signals , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[13]  Kenneth A. Loparo,et al.  Estimation of the running speed and bearing defect frequencies of an induction motor from vibration data , 2004 .

[14]  S.A.V. Satya Murty,et al.  Roller element bearing fault diagnosis using singular spectrum analysis , 2013 .

[15]  Andrew Ball,et al.  Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment , 2011 .

[16]  E. P. de Moura,et al.  Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses , 2011 .