SVD and Hankel matrix based de-noising approach for ball bearing fault detection and its assessment using artificial faults

Abstract Ball bearings remain one of the most crucial components in industrial machines and due to their critical role, it is of great importance to monitor their conditions under operation. However, due to the background noise in acquired signals, it is not always possible to identify probable faults. This incapability in identifying the faults makes the de-noising process one of the most essential steps in the field of Condition Monitoring (CM) and fault detection. In the present study, Singular Value Decomposition (SVD) and Hankel matrix based de-noising process is successfully applied to the ball bearing time domain vibration signals as well as to their spectrums for the elimination of the background noise and the improvement the reliability of the fault detection process. The test cases conducted using experimental as well as the simulated vibration signals demonstrate the effectiveness of the proposed de-noising approach for the ball bearing fault detection.

[1]  Jong-Myon Kim,et al.  Singular value decomposition based feature extraction approaches for classifying faults of induction motors , 2013 .

[2]  James A. Cadzow,et al.  Signal enhancement-a composite property mapping algorithm , 1988, IEEE Trans. Acoust. Speech Signal Process..

[3]  P. Swaminathan,et al.  Inner race bearing fault detection using Singular Spectrum Analysis , 2010, 2010 INTERNATIONAL CONFERENCE ON COMMUNICATION CONTROL AND COMPUTING TECHNOLOGIES.

[4]  M. P. Norton,et al.  Fundamentals of Noise and Vibration Analysis for Engineers , 1990 .

[5]  Robert B. Randall,et al.  Enhancement of autoregressive model based gear tooth fault detection technique by the use of minimum entropy deconvolution filter , 2007 .

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

[7]  Paolo Pennacchi,et al.  The combination of empirical mode decomposition and minimum entropy deconvolution for roller bearing diagnostics in non-stationary operation , 2012 .

[8]  Jie Liu,et al.  A Posteriori Error Estimates with Computable Upper Bound for the Nonconforming Rotated Q 1 Finite Element Approximation of the Eigenvalue Problems , 2014 .

[9]  Jun Jing,et al.  Feature Extraction of Pulse Signal Based on Hilbert-Huang Transformation and Singular Value Decomposition , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[10]  Fulei Chu,et al.  Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples , 2013 .

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

[12]  Umberto Meneghetti,et al.  Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings , 2001 .

[13]  Shufeng Ai,et al.  EMD based envelope analysis for bearing faults detection , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[14]  Zhongyuan Su,et al.  Gear fault identification and classification of singular value decomposition based on Hilbert-Huang transform , 2011 .

[15]  Tomasz Barszcz,et al.  Bearings Fault Detection in Gas Compressor in Presence of High Level of Non-Gaussian Impulsive Noise , 2013 .

[16]  Jin Chen,et al.  Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis , 2013 .

[17]  Saeed Vaseghi,et al.  Advanced Signal Processing and Digital Noise Reduction , 1996 .

[18]  Kenan Y. Sanliturk,et al.  Noise elimination from measured frequency response functions , 2005 .

[19]  Fakher Chaari,et al.  Condition Monitoring of Machinery in Non-Stationary Operations , 2012 .

[20]  R. Kumaresan,et al.  Data adaptive signal estimation by singular value decomposition of a data matrix , 1982, Proceedings of the IEEE.

[21]  Robert B. Randall,et al.  The spectral kurtosis: application to the vibratory surveillance and diagnostics of rotating machines , 2006 .

[22]  Gang Chen,et al.  Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis , 2015 .

[23]  Junwu Kan,et al.  Machine Fault Diagnosis Based On Reassigned Wavelet Scalogram and SVD , 2012 .

[24]  Shui Yong Yang,et al.  Auto Gearbox Bearing Fault Diagnosis Method Based on Winger-SVD , 2014 .

[25]  Peter W. Tse,et al.  Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition , 2012 .

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

[27]  Robert B. Randall,et al.  The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis , 2007 .

[28]  Pierre Dehombreux,et al.  Singular Spectrum Analysis for Bearing Defect Detection , 2011 .

[29]  Xuezhi Zhao,et al.  Similarity of signal processing effect between Hankel matrix-based SVD and wavelet transform and its mechanism analysis , 2009 .

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

[31]  Pei Lin Zhang,et al.  Bearing Fault Detection Based on SVD and EMD , 2012 .

[32]  Søren Holdt Jensen,et al.  Reduction of broad-band noise in speech by truncated QSVD , 1995, IEEE Trans. Speech Audio Process..

[33]  Tomasz Barszcz,et al.  Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings , 2014 .

[34]  Yanyang Zi,et al.  Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .