A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis

Abstract Rolling element bearings are broadly applied in various industrial machines, such as railway axles, gearboxes, electric motors, and turbines. Bearing fault diagnosis is important for preventing unexpected accidents and has been a hot research topic in last decades. Recently, a new signal processing method called the variational mode decomposition (VMD) has drawn substantial interest from researchers and engineers, and its applications for fault diagnosis have been widely discussed. The application of VMD can adaptively extract the IMFs and efficiently reduce the general problem of modal aliasing. The main problem of the VMD is that the number of instinct mode functions (IMFs) cannot be automatically determined, so the decomposition results are not reliable if the number of IMFs is not adequate. In this paper, a modified scale-space VMD is proposed to estimate the number of IMFs and optimize some other parameters of VMD in advance by applying scale-space representation to have a prior understand of the analysed signal and derive the parameters. A set of faulty industrial railway axle bearing signals are studied to verify the effectiveness of the proposed method. The results show that the proposed method can automatically decompose the resonance frequency bands of the faulty bearing signal and reveal the fault pattern by further conducting an envelope analysis.

[1]  Robert B. Randall,et al.  Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study , 2015 .

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

[3]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[4]  V. A. Morozov,et al.  Linear and nonlinear ill-posed problems , 1975 .

[5]  Robert B. Randall,et al.  Differential Diagnosis of Gear and Bearing Faults , 2002 .

[6]  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 .

[7]  Jijian Lian,et al.  Adaptive variational mode decomposition method for signal processing based on mode characteristic , 2018, Mechanical Systems and Signal Processing.

[8]  Yanxue Wang,et al.  Filter bank property of variational mode decomposition and its applications , 2016, Signal Process..

[9]  Gedong Jiang,et al.  Feed-axis gearbox condition monitoring using built-in position sensors and EEMD method , 2011 .

[10]  Jianming Ding,et al.  Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique , 2018 .

[11]  Rajendra Singh,et al.  VIBRATION TRANSMISSION THROUGH ROLLING ELEMENT BEARINGS, PART II: SYSTEM STUDIES , 1990 .

[12]  Jiming Ma,et al.  A fault diagnosis method for roller bearing based on empirical wavelet transform decomposition with adaptive empirical mode segmentation , 2018 .

[13]  Haiyang Pan,et al.  Adaptive parameterless empirical wavelet transform based time-frequency analysis method and its application to rotor rubbing fault diagnosis , 2017, Signal Process..

[14]  I. Soltani Bozchalooi,et al.  An energy operator approach to joint application of amplitude and frequency-demodulations for bearing fault detection ☆ , 2010 .

[15]  Hee-Seok Oh,et al.  Enhancement of variational mode decomposition with missing values , 2018, Signal Process..

[16]  Guang Meng,et al.  Nonlinear Chirp Mode Decomposition: A Variational Method , 2017, IEEE Transactions on Signal Processing.

[17]  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.

[18]  Tony Lindeberg,et al.  Scale-Space for Discrete Signals , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[20]  Jing Wang,et al.  Basic pursuit of an adaptive impulse dictionary for bearing fault diagnosis , 2014, 2014 International Conference on Mechatronics and Control (ICMC).

[21]  Kathryn Heal,et al.  A parameterless scale-space approach to find meaningful modes in histograms - Application to image and spectrum segmentation , 2014, Int. J. Wavelets Multiresolution Inf. Process..

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

[23]  Tony Lindeberg,et al.  Scale-Space Theory in Computer Vision , 1993, Lecture Notes in Computer Science.

[24]  Qiang Miao,et al.  A parameter-adaptive VMD method based on grasshopper optimization algorithm to analyze vibration signals from rotating machinery , 2018, Mechanical Systems and Signal Processing.

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

[26]  R. Randall,et al.  OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS , 2000 .

[27]  A Tikhonov,et al.  Solution of Incorrectly Formulated Problems and the Regularization Method , 1963 .

[28]  Yanyang Zi,et al.  Independence-oriented VMD to identify fault feature for wheel set bearing fault diagnosis of high speed locomotive , 2017 .

[29]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series , 1964 .

[30]  Abdollah Bagheri,et al.  Structural system identification based on variational mode decomposition , 2018 .

[31]  Robert B. Randall,et al.  Vibration-based Condition Monitoring: Industrial, Aerospace and Automotive Applications , 2011 .

[32]  Robert B. Randall,et al.  Rolling element bearing diagnostics—A tutorial , 2011 .

[33]  S. I. McNeill,et al.  Decomposing a signal into short-time narrow-banded modes , 2016 .

[34]  Peter W. Tse,et al.  Wavelet Analysis and Envelope Detection For Rolling Element Bearing Fault Diagnosis—Their Effectiveness and Flexibilities , 2001 .

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

[36]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[37]  Xiaofeng Liu,et al.  Application of correlation matching for automatic bearing fault diagnosis , 2012 .

[38]  P. D. McFadden,et al.  Vibration monitoring of rolling element bearings by the high-frequency resonance technique — a review , 1984 .

[39]  Jinglong Chen,et al.  Mono-component feature extraction for mechanical fault diagnosis using modified empirical wavelet transform via data-driven adaptive Fourier spectrum segment , 2016 .

[40]  Fan Jiang,et al.  An Improved VMD With Empirical Mode Decomposition and Its Application in Incipient Fault Detection of Rolling Bearing , 2018, IEEE Access.