Improved singular spectrum decomposition-based 1.5-dimensional energy spectrum for rotating machinery fault diagnosis

Fault diagnosis of rotating machinery has always been being a challenge thanks to the various effects of nonlinear factors. To address this problem, combining the concepts of improved singular spectrum decomposition with 1.5-dimensional energy spectrum in this paper, a novel method is presented for diagnosing the partial faults of rotating machinery. Within the proposed algorithm, waveform matching extension is firstly introduced to suppress the end effect of singular spectrum decomposition and obtain several singular spectrum components (SSCs) whose instantaneous features have physical meaning. Meanwhile, a new sensitive index is put forward to choose adaptively the sensitive SSCs containing the principal fault characteristic signatures. Subsequently, 1.5-dimensional energy spectrum of the selected sensitive SSC is conducted to acquire the defective frequency and identify the fault type of rotating machinery. The validity of the raised algorithm is proved through the applications in the fault detection of gear and rolling bearing. It turned out that the proposed method can improve signal’s decomposition results and is able to detect effectively the local faults of gear or rolling bearing. The studies provide a new perspective for the improvement in damage detection of rotating machinery.

[1]  Dejie Yu,et al.  Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes , 2015 .

[2]  Peter W. Tse,et al.  The design of a new sparsogram for fast bearing fault diagnosis: Part 1 of the two related manuscripts that have a joint title as “Two automatic vibration-based fault diagnostic methods using the novel sparsity measurement – Parts 1 and 2” , 2013 .

[3]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

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

[5]  Feng Wu,et al.  Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform , 2015 .

[6]  Ming Zeng,et al.  Normalized complex Teager energy operator demodulation method and its application to fault diagnosis in a rubbing rotor system , 2015 .

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

[8]  Ram Bilas Pachori,et al.  Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform , 2016, Signal Process..

[9]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[10]  Peng LI,et al.  Hilbert-Huang transform with adaptive waveform matching extension and its application in power quality disturbance detection for microgrid , 2016 .

[11]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[12]  Ling Xiang,et al.  A new wind turbine fault diagnosis method based on ensemble intrinsic time-scale decomposition and WPT-fractal dimension , 2015 .

[13]  Miguel A. Ferrer,et al.  Application of the Teager-Kaiser energy operator in bearing fault diagnosis. , 2013, ISA transactions.

[14]  Jianhua Cai,et al.  Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum , 2016 .

[15]  Minping Jia,et al.  Compound fault diagnosis of rotating machinery based on OVMD and a 1.5-dimension envelope spectrum , 2016 .

[16]  Cheng Wei.  Research and Application of Ensemble Empirical Mode Decomposition Principle and 1.5 Dimension Spectrum Method , 2009 .

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

[18]  A. S. Sekhar,et al.  Fault detection in rotor bearing systems using time frequency techniques , 2016 .

[19]  Aijun Hu.  New process method for end effects of HILBERT-HUANG transform , 2008 .

[20]  Yi Yang,et al.  A rotating machinery fault diagnosis method based on local mean decomposition , 2012, Digit. Signal Process..

[21]  Joshua R. Smith,et al.  The local mean decomposition and its application to EEG perception data , 2005, Journal of The Royal Society Interface.

[22]  Xiaodong Wang,et al.  Rolling bearing fault detection using an adaptive lifting multiwavelet packet with a dimension spectrum , 2013 .

[23]  Joël M. H. Karel,et al.  Singular Spectrum Decomposition: a New Method for Time Series Decomposition , 2014, Adv. Data Sci. Adapt. Anal..

[24]  Junsheng Cheng,et al.  Application of support vector regression machines to the processing of end effects of Hilbert Huang transform , 2007 .

[25]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[26]  I. Soltani Bozchalooi,et al.  Teager energy operator for multi-modulation extraction and its application for gearbox fault detection , 2010 .

[27]  Danilo P. Mandic,et al.  Empirical Mode Decomposition-Based Time-Frequency Analysis of Multivariate Signals: The Power of Adaptive Data Analysis , 2013, IEEE Signal Processing Magazine.

[28]  Hong Fan,et al.  Rotating machine fault diagnosis using empirical mode decomposition , 2008 .

[29]  Tang Gui-j.  Fault diagnosis for roller bearings based on EEMD de-noising and 1.5-dimensional energy spectrum , 2014 .

[30]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[31]  Li Xiaoqing,et al.  Time-optimal Algorithms for Continuous Scans of Wafer Stage of Step-scan Lithography Tool , 2008 .

[32]  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 .

[33]  Guanghua Xu,et al.  Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization , 2014, Chinese Journal of Mechanical Engineering.

[34]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[35]  Zong Meng,et al.  Restraining Method for End Effect of B-spline Empirical Mode Decomposition Based on Neural Network Ensemble , 2013 .

[36]  J. Antoni.  The spectral kurtosis: a useful tool for characterising non-stationary signals , 2006 .

[37]  Jia Minping.  Study on processing method and analysis of end problem of Hilbert-Huang spectrum , 2006 .

[38]  Wei Li,et al.  Fault diagnosis of rotating machinery with a novel statistical feature extraction and evaluation method , 2015 .

[39]  Fulei Chu,et al.  Ensemble Empirical Mode Decomposition-Based Teager Energy Spectrum for Bearing Fault Diagnosis , 2013 .

[40]  Joël M. H. Karel,et al.  Sleep apnea detection directly from unprocessed ECG through singular spectrum decomposition , 2015, 2015 Computing in Cardiology Conference (CinC).