Adaptive correlated Kurtogram and its applications in wheelset-bearing system fault diagnosis

Abstract As one of the crucial subsystems, the performance of the wheelset-bearing system will significantly affect the running safety, stability and comfort of a high-speed train. Compared with the traditional rotating machinery vibration signals, those of wheelset-bearing systems have a distinct difference, for instance, the wheelset tread damage induced vibrations and bearing fault-related vibrations coexist and have similar wave-forms. Therefore, it is necessary to advance a reasonable method to identify the two kinds of cyclo-stationary components. To achieve this purpose, the adaptive correlated Kurtogram (ACK) is proposed. The proposed ACK method has two advantages: the first one is it can adaptively generate the paving of the plane for Kurtogram by using scale-space representation (SSR) to guarantee the detected boundaries cover the resonant frequency band well; the second one is it can highlight the special periodic impulses by correlated kurtosis. The scale-scale plane (SSP), which is obtained by SSR, is used to represent the frequency distribution characteristic of the acquired signal, firstly. After that, the paving of the plane is arbitrarily divided based on the SSP. Then the empirical wavelet transform (EWT) is employed to construct the sub-band signals according to the paving of the plane. The correlated kurtosis values with interesting periodic are calculated to distinguish the special repetitive transients. The nodes with the largest correlated kurtosis values are used to conduct a further envelope spectrum analysis. ACK method improves the effectiveness of the identification of the frequency bands containing impact information, and is capable of detecting multiple frequency bands simultaneously. The proposed ACK method is demonstrated by the simulated signal and two experimental signals acquired from a wheelset-bearing system test bench. The results indicate that the ACK can simultaneously extract the wheelset tread damage and bearing fault-related vibration component, and it is superior to the other traditional methods.

[1]  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 .

[2]  Alessandro Fasana,et al.  The Autogram: An effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis , 2018 .

[3]  Zechao Liu,et al.  Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet , 2020 .

[4]  Ram Bilas Pachori,et al.  Fourier-Bessel series expansion based empirical wavelet transform for analysis of non-stationary signals , 2018, Digit. Signal Process..

[5]  Lei Xu,et al.  Modelling of vehicle-track related dynamics: a development of multi-finite-element coupling method and multi-time-step solution method , 2020, Vehicle System Dynamics.

[6]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[7]  Weiwei Liu,et al.  Development of a morphological convolution operator for bearing fault detection , 2018 .

[8]  Ming Zhao,et al.  Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis , 2016 .

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

[10]  Jianming Ding,et al.  Automatic detection of a wheelset bearing fault using a multi-level empirical wavelet transform , 2019, Measurement.

[11]  Ming J. Zuo,et al.  Fault detection method for railway wheel flat using an adaptive multiscale morphological filter , 2017 .

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

[13]  Qing Zhao,et al.  Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .

[14]  Jianhui Lin,et al.  A modified scale-space guiding variational mode decomposition for high-speed railway bearing fault diagnosis , 2019, Journal of Sound and Vibration.

[15]  Yanli Yin,et al.  Fault detection and diagnosis of a wheelset-bearing system using a multi-Q-factor and multi-level tunable Q-factor wavelet transform , 2019 .

[16]  Nicola Bosso,et al.  Wheel flat detection algorithm for onboard diagnostic , 2018, Measurement.

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

[18]  Hao Cha Vibration Performance of High-speed Vehicles with Axle Box Bearing , 2018 .

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

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

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

[22]  Yaguo Lei,et al.  Envelope harmonic-to-noise ratio for periodic impulses detection and its application to bearing diagnosis , 2016 .

[23]  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 .

[24]  J. Antoni Fast computation of the kurtogram for the detection of transient faults , 2007 .

[25]  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..

[26]  Peter W. Tse,et al.  An enhanced Kurtogram method for fault diagnosis of rolling element bearings , 2013 .

[27]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[28]  Kun Zhang,et al.  Adaptive Kurtogram and its applications in rolling bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[29]  Binqiang Chen,et al.  Detecting of transient vibration signatures using an improved fast spatial–spectral ensemble kurtosis kurtogram and its applications to mechanical signature analysis of short duration data from rotating machinery , 2013 .

[30]  M. Feldman Hilbert transform in vibration analysis , 2011 .

[31]  Yong Qin,et al.  A simple and fast guideline for generating enhanced/squared envelope spectra from spectral coherence for bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[32]  Lei Xu,et al.  Matrix coupled model for the vehicle–track interaction analysis featured to the railway crossing , 2021 .

[33]  Cai Yi,et al.  Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings , 2018 .

[34]  Yaguo Lei,et al.  Application of an improved kurtogram method for fault diagnosis of rolling element bearings , 2011 .