Inner race bearing fault detection using Singular Spectrum Analysis

A novel method to diagnose the bearing fault is presented. The proposed method is based on the analysis of the bearing vibration signals using Singular Spectrum Analysis (SSA). SSA is a non-parametric technique of time series analysis that decomposes the acquired bearing vibration signals into an additive set of time series to extract information correlated with the condition of the bearing. Information in terms of time-domain features extracted from the SSA processed signal has been presented to a neural network for determination of inner race bearing fault. The result shows the effectiveness of the proposed method.

[1]  B. Datner,et al.  Analysis of Roller/Ball Bearing Vibrations , 1979 .

[2]  Mario Pacas,et al.  Frequency Response Analysis for Rolling-Bearing Damage Diagnosis , 2008, IEEE Transactions on Industrial Electronics.

[3]  Robert X. Gao,et al.  Energy-Based Feature Extraction for Defect Diagnosis in Rotary Machines , 2009, IEEE Transactions on Instrumentation and Measurement.

[4]  Michael J. Devaney,et al.  Adjustable Speed Drive Bearing Fault Detection via Wavelet Packet Decomposition , 2006, 2006 IEEE Instrumentation and Measurement Technology Conference Proceedings.

[5]  Qiang Luo,et al.  Fault dignosis of rolling bearing based on time domain parameters , 2010, 2010 Chinese Control and Decision Conference.

[6]  R. Vautard,et al.  Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series , 1989 .

[7]  D. R. Salgado,et al.  Tool wear detection in turning operations using singular spectrum analysis , 2006 .

[8]  Zhonghua Huang,et al.  Fault Diagnosis Method of Rolling Bearing Based on BP Neural Network , 2009, 2009 International Conference on Measuring Technology and Mechatronics Automation.

[9]  Andrew L. Rukhin,et al.  Analysis of Time Series Structure SSA and Related Techniques , 2002, Technometrics.

[10]  Lin Ma,et al.  Fault diagnosis of rolling element bearings using basis pursuit , 2005 .

[11]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[12]  S. Poyhonen,et al.  Signal processing of vibrations for condition monitoring of an induction motor , 2004, First International Symposium on Control, Communications and Signal Processing, 2004..

[13]  Alberto Bellini,et al.  Detection of Generalized-Roughness Bearing Fault by Spectral-Kurtosis Energy of Vibration or Current Signals , 2009, IEEE Transactions on Industrial Electronics.

[14]  B. Samanta,et al.  ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES , 2003 .

[15]  A. F. Stronach,et al.  Third-order spectral techniques for the diagnosis of motor bearing condition using artificial neural networks , 2002 .

[16]  W. J. Wang,et al.  THE APPLICATION OF PSEUDO-PHASE PORTRAIT IN MACHINE CONDITION MONITORING , 2003 .

[17]  Bo Li,et al.  Detection of common motor bearing faults using frequency-domain vibration signals and a neural network based approach , 1998, Proceedings of the 1998 American Control Conference. ACC (IEEE Cat. No.98CH36207).

[18]  A. Srividya,et al.  Fault diagnosis of rolling element bearing using time-domain features and neural networks , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[19]  Anoushiravan Farshidianfar,et al.  Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine , 2007 .

[20]  Takashi Hiyama,et al.  Fault classification performance of induction motor bearing using AI methods , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[21]  Jie Liu,et al.  An Extended Wavelet Spectrum for Bearing Fault Diagnostics , 2008, IEEE Transactions on Instrumentation and Measurement.

[22]  G. S. Yadava,et al.  Applications of artificial intelligence techniques for induction machine stator fault diagnostics: review , 2003, 4th IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives, 2003. SDEMPED 2003..

[23]  Weike Wang,et al.  THE APPLICATION OF SOME NON-LINEAR METHODS IN ROTATING MACHINERY FAULT DIAGNOSIS , 2001 .

[24]  D. W. Thomas,et al.  Bearing Fault Detection Using Adaptive Noise Cancelling , 1982 .