Blind deconvolution assisted with periodicity detection techniques and its application to bearing fault feature enhancement

Abstract Maximum correlated kurtosis deconvolution (MCKD), multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) and maximum second-order cyclostationarity blind deconvolution (CYCBD) have remarkable performances in extracting periodic impulses. However, these deconvolution methods highly rely on the prior period of measured signal and can only enhance the specific periodic impulses. Aiming at these limitations, six kinds of periodicity detection techniques (PDTs) are introduced to adaptively identify the period of repetitive impulses. Further, PDTs-assisted MCKD, MOMEDA and CYCBD are proposed for bearing fault feature enhancement. The improved deconvolution methods have two characteristics: first, the fault period is automatically identified by PDTs according to the characteristics of the measured signal; second, the impulses of different faults can be enhanced adaptively. The analysis results of simulated and experimental datasets demonstrated the better capability of the proposed methods in enhancing bearing fault features with respect to original deconvolution methods and the fast kurtogram method.

[1]  J. Antoni.  Cyclic spectral analysis of rolling-element bearing signals : Facts and fictions , 2007 .

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

[3]  Ming Zhao,et al.  A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox , 2017 .

[4]  Guozheng Li,et al.  Blind source separation of composite bearing vibration signals with low-rank and sparse decomposition , 2019, Measurement.

[5]  Jérôme Antoni,et al.  A subspace method for the blind extraction of a cyclostationary source , 2005, 2005 13th European Signal Processing Conference.

[6]  Carlos Cabrelli,et al.  Minimum entropy deconvolution and simplicity: A noniterative algorithm , 1985 .

[7]  Liu Shaopeng,et al.  L-Kurtosis and its application for fault detection of rolling element bearings , 2018 .

[8]  Zhiwei Wang,et al.  Particle swarm optimization algorithm to solve the deconvolution problem for rolling element bearing fault diagnosis. , 2019, ISA transactions.

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

[10]  Wenhua Du,et al.  Research and application of improved adaptive MOMEDA fault diagnosis method , 2019, Measurement.

[11]  Yi Qin,et al.  Kurtogram manifold learning and its application to rolling bearing weak signal detection , 2018, Measurement.

[12]  Fanrang Kong,et al.  Bearing fault diagnosis based on an improved morphological filter , 2016 .

[13]  Ming J. Zuo,et al.  A new strategy of using a time-varying structure element for mathematical morphological filtering , 2017 .

[14]  Dongying Han,et al.  Signal feature extraction based on cascaded multi-stable stochastic resonance denoising and EMD method , 2016 .

[15]  Yi Zhang,et al.  Faults diagnosis of rolling bearings based on shift invariant K-singular value decomposition with sensitive atom nonlocal means enhancement , 2019, Measurement.

[16]  S. E. Khadem,et al.  Quantitative diagnosis for bearing faults by improving ensemble empirical mode decomposition. , 2018, ISA transactions.

[17]  Minping Jia,et al.  Research on an enhanced scale morphological-hat product filtering in incipient fault detection of rolling element bearings , 2019 .

[18]  Feng Jia,et al.  An Intelligent Fault Diagnosis Method Using Unsupervised Feature Learning Towards Mechanical Big Data , 2016, IEEE Transactions on Industrial Electronics.

[19]  Jérôme Antoni,et al.  Indicators of cyclostationarity: Theory and application to gear fault monitoring , 2008 .

[20]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[21]  J. Antoni,et al.  Fast computation of the spectral correlation , 2017 .

[22]  Radoslaw Zimroz,et al.  Cyclic sources extraction from complex multiple-component vibration signal via periodically time varying filter , 2017 .

[23]  Junsheng Cheng,et al.  Adaptive sparsest narrow-band decomposition method and its applications to rolling element bearing fault diagnosis , 2017 .

[24]  Qing Zhao,et al.  Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection , 2017 .

[25]  Yao Cheng,et al.  Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted and Application to Fault Diagnosis of Rolling Element Bearings , 2019, IEEE Sensors Journal.

[26]  Xiaodong Jia,et al.  A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery , 2017 .

[27]  Asoke K. Nandi,et al.  EXTRACTION OF IMPACTING SIGNALS USING BLIND DECONVOLUTION , 2000 .

[28]  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.

[29]  Siliang Lu,et al.  A review of stochastic resonance in rotating machine fault detection , 2019, Mechanical Systems and Signal Processing.

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

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

[32]  R. Wiggins.  Minimum entropy deconvolution , 1978 .

[33]  Marco Buzzoni,et al.  Blind deconvolution based on cyclostationarity maximization and its application to fault identification , 2018, Journal of Sound and Vibration.

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

[35]  Lingli Cui,et al.  A Novel Weighted Sparse Representation Classification Strategy Based on Dictionary Learning for Rotating Machinery , 2020, IEEE Transactions on Instrumentation and Measurement.

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