Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.

[1]  Cancan Yi,et al.  Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing , 2017 .

[2]  Gangbing Song,et al.  A review of damage detection methods for wind turbine blades , 2015 .

[3]  Robert B. Randall,et al.  THE RELATIONSHIP BETWEEN SPECTRAL CORRELATION AND ENVELOPE ANALYSIS IN THE DIAGNOSTICS OF BEARING FAULTS AND OTHER CYCLOSTATIONARY MACHINE SIGNALS , 2001 .

[4]  Xuejun Li,et al.  Multi-Fault Detection of Rolling Element Bearings under Harsh Working Condition Using IMF-Based Adaptive Envelope Order Analysis , 2014, Sensors.

[5]  Gangbing Song,et al.  Health Status Monitoring of Cuplock Scaffold Joint Connection Based on Wavelet Packet Analysis , 2015 .

[6]  Dinesh Kumar,et al.  Estimation of independent and dependent components of non-invasive EMG using fast ICA: validation in recognising complex gestures , 2011, Computer methods in biomechanics and biomedical engineering.

[7]  T. Adali,et al.  Complex ICA of Brain Imaging Data [Life Sciences] , 2007, IEEE Signal Processing Magazine.

[8]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[9]  S. Sanei,et al.  Tensor Based Singular Spectrum Analysis for Automatic Scoring of Sleep EEG , 2015, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  Guillaume Gelle,et al.  BLIND SOURCE SEPARATION: A TOOL FOR ROTATING MACHINE MONITORING BY VIBRATIONS ANALYSIS? , 2001 .

[11]  Lei Huang,et al.  Bayesian Information Criterion for Source Enumeration in Large-Scale Adaptive Antenna Array , 2016, IEEE Transactions on Vehicular Technology.

[12]  Ganesh R. Naik,et al.  Single channel blind source separation based local mean decomposition for Biomedical applications , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[13]  G. Naik,et al.  Transradial Amputee Gesture Classification Using an Optimal Number of sEMG Sensors: An Approach Using ICA Clustering , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  Sabine Van Huffel,et al.  Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis , 2010, IEEE Transactions on Biomedical Engineering.

[15]  Alexander Ypma,et al.  Learning methods for machine vibration analysis and health monitoring , 2001 .

[16]  Ayan Sadhu,et al.  An integrated multivariate empirical mode decomposition method towards modal identification of structures , 2017 .

[17]  Ganesh R. Naik,et al.  Using Blind Source Separation on accelerometry data to analyze and distinguish the toe walking gait from normal gait in ITW children , 2014, Biomed. Signal Process. Control..

[18]  Cancan Yi,et al.  A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition , 2016 .

[19]  Eric Moulines,et al.  A blind source separation technique using second-order statistics , 1997, IEEE Trans. Signal Process..

[20]  Gangbing Song,et al.  Structural health monitoring of multi-spot welded joints using a lead zirconate titanate based active sensing approach , 2015 .

[21]  J. Antoni.  Blind separation of vibration components: Principles and demonstrations , 2005 .

[22]  M. Zelmat,et al.  Blind source separation filters-based-fault detection and isolation in a three tank system , 2014 .

[23]  Ming Liang,et al.  Separation of fault features from a single-channel mechanical signal mixture using wavelet decomposition , 2007 .

[24]  W. L. Woo,et al.  Single-Channel Source Separation Using EMD-Subband Variable Regularized Sparse Features , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[25]  Fuling Bian,et al.  Geo-Informatics in Resource Management and Sustainable Ecosystem , 2013, Communications in Computer and Information Science.

[26]  M.E. Davies,et al.  Source separation using single channel ICA , 2007, Signal Process..

[27]  Ganesh R. Naik,et al.  Single-Channel EMG Classification With Ensemble-Empirical-Mode-Decomposition-Based ICA for Diagnosing Neuromuscular Disorders , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Gang Tang,et al.  A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition , 2014, PloS one.

[29]  Application of Empirical Model Decomposition and Independent Component Analysis to Magnetic Anomalies Separation: A Case Study for Gobi Desert Coverage in Eastern Tianshan, China , 2016 .

[30]  Jean Gotman,et al.  Validation of Shared and Specific Independent Component Analysis (SSICA) for Between-Group Comparisons in fMRI , 2016, Front. Neurosci..

[31]  Søren Holdt Jensen,et al.  New Results on Single-Channel Speech Separation Using Sinusoidal Modeling , 2011, IEEE Transactions on Audio, Speech, and Language Processing.

[32]  G. Song,et al.  Crack detection and leakage monitoring on reinforced concrete pipe , 2015 .

[33]  Norden E. Huang,et al.  Complementary Ensemble Empirical Mode Decomposition: a Novel Noise Enhanced Data Analysis Method , 2010, Adv. Data Sci. Adapt. Anal..

[34]  Ganesh R. Naik,et al.  Edge Effect Elimination in Single-Mixture Blind Source Separation , 2013, Circuits, Systems, and Signal Processing.

[35]  Gangbing Song,et al.  Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing , 2016 .

[36]  Han Xiao,et al.  A Novel Mechanical Fault Diagnosis Scheme Based on the Convex 1-D Second-Order Total Variation Denoising Algorithm , 2016 .

[37]  Min Li,et al.  Hierarchical ensemble-based data fusion for structural health monitoring , 2010 .

[38]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[39]  Minqiang Xu,et al.  A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy , 2016 .

[40]  Feipeng Da,et al.  Regenerated Phase-Shifted Sinusoid-Assisted Empirical Mode Decomposition , 2016, IEEE Signal Processing Letters.

[41]  Yongtang Li,et al.  Single-Mixture Source Separation Using Dimensionality Reduction of Ensemble Empirical Mode Decomposition and Independent Component Analysis , 2012, Circuits, Systems, and Signal Processing.

[42]  Jing Guo,et al.  A Time-Frequency Algorithm for Noisy ICA , 2015, GRMSE.

[43]  Diego H. Milone,et al.  Using multiple frequency bins for stabilization of FD-ICA algorithms , 2016, Signal Process..

[44]  Sami F. Masri,et al.  Damage detection for a frame structure model using vibration displacement measurement , 2012 .

[45]  Rifai Chai,et al.  Driver Fatigue Classification With Independent Component by Entropy Rate Bound Minimization Analysis in an EEG-Based System , 2017, IEEE Journal of Biomedical and Health Informatics.