Compound fault diagnosis of rotating machinery based on adaptive maximum correlated kurtosis deconvolution and customized multiwavelet transform

Although compound fault diagnosis of rotating machinery based on vibration signals is a prominent method, it is still a challenge due to coupled fault features immersed in strong background noise. An adaptive deconvolution and denoising technology based on adaptive maximum correlated kurtosis deconvolution and multiwavelet transform is studied here. In combination with Hilbert envelope spectrum analysis, a novel compound fault diagnosis method is proposed in this paper. Based on analysis using maximum correlated kurtosis deconvolution (MCKD) theory, a parameter range estimation method is given which is favorable for MCKD parameter optimization. Flexible standard multiwavelets are used in post-processing of MCKD to enhance the denoising effect further. The minimum of a compound faults characteristic index composed of M-shift correlated kurtosis and square envelope spectrum entropy is adopted as the optimization criterion to set reasonable parameters for MCKD and construct customized multiwavelets using particle swarm optimization. The effectiveness of the proposed method is demonstrated by both simulated signal and practical vibration signals of a rotor test rig and an aero engine rotor experimental rig with different compound faults. The superior effectiveness and reliability of the proposed method are confirmed by comparison with other fault detection methods.

[1]  Li Li,et al.  Blind vibration component separation and nonlinear feature extraction applied to the nonstationary vibration signals for the gearbox multi-fault diagnosis , 2013 .

[2]  Fulei Chu,et al.  Compound faults detection in gearbox via meshing resonance and spectral kurtosis methods , 2017 .

[3]  Mir Mohammad Ettefagh,et al.  Diagnosis of combined faults in Rotary Machinery by Non-Naive Bayesian approach , 2017 .

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

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

[6]  Guoping Chen,et al.  Vibration modelling and verifications for whole aero-engine , 2015 .

[7]  Jing Yuan,et al.  Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review , 2016 .

[8]  Martin Vetterli,et al.  Balanced multiwavelets theory and design , 1998, IEEE Trans. Signal Process..

[9]  Salah Bouhouche,et al.  Application of Wavelet Transform for Fault Diagnosis in Rotating Machinery , 2012 .

[10]  Weidong Cheng,et al.  Compound faults detection of rolling element bearing based on the generalized demodulation algorithm under time-varying rotational speed , 2016 .

[11]  C. Chui,et al.  A study of orthonormal multi-wavelets , 1996 .

[12]  Yaguo Lei,et al.  Application of an improved maximum correlated kurtosis deconvolution method for fault diagnosis of rolling element bearings , 2017 .

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

[14]  Xiang-Gen Xia,et al.  Why and how prefiltering for discrete multiwavelet transforms , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[15]  Shuilong He,et al.  Multifractal entropy based adaptive multiwavelet construction and its application for mechanical compound-fault diagnosis , 2016 .

[16]  Hongkai Jiang,et al.  An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis , 2013 .

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

[18]  V. Strela.  Multiwavelets--theory and applications , 1996 .

[19]  Haiyang Pan,et al.  Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[20]  D. Hardin,et al.  Fractal Functions and Wavelet Expansions Based on Several Scaling Functions , 1994 .

[21]  Min Wang,et al.  A method for the compound fault diagnosis of gearboxes based on morphological component analysis , 2016 .

[22]  Jing Yuan,et al.  Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain , 2016 .

[23]  Zi Yanyang,et al.  Construction of adaptive redundant multiwavelet packet and its application to compound faults detection of rotating machinery , 2012 .

[24]  Y. Zi,et al.  Adaptive multiwavelets via two-scale similarity transforms for rotating machinery fault diagnosis , 2009 .

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

[26]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

[27]  Hooshang Jazayeri-Rad,et al.  Modeling of a solid oxide fuel cell power plant using an ensemble of neural networks based on a combination of the adaptive particle swarm optimization and Levenberg–Marquardt algorithms , 2014 .

[28]  A. S. Sekhar,et al.  Detection and monitoring of coupling misalignment in rotors using torque measurements , 2015 .

[29]  Yanyang Zi,et al.  Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet , 2013 .

[30]  Jun Du,et al.  Layered clustering multi-fault diagnosis for hydraulic piston pump , 2013 .

[31]  Xiaoyuan Zhang,et al.  Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines , 2013 .