Adaptive parametric dictionary design of sparse representation based on fault impulse matching for rotating machinery weak fault detection

[1]  Huibin Lin,et al.  Gearbox coupling modulation separation method based on match pursuit and correlation filtering , 2016 .

[2]  Cheng Junsheng,et al.  Application of an impulse response wavelet to fault diagnosis of rolling bearings , 2007 .

[3]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[4]  Yang Yang,et al.  Detection of rub-impact fault for rotor-stator systems: A novel method based on adaptive chirp mode decomposition , 2019, Journal of Sound and Vibration.

[5]  Zhipeng Feng,et al.  Application of atomic decomposition to gear damage detection , 2007 .

[6]  Ming J. Zuo,et al.  Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis: A review with examples , 2017 .

[7]  Michael Elad,et al.  Multi-Scale Dictionary Learning Using Wavelets , 2011, IEEE Journal of Selected Topics in Signal Processing.

[8]  Guangming Dong,et al.  Wigner–Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis , 2011 .

[9]  Yaguo Lei,et al.  Application of the EEMD method to rotor fault diagnosis of rotating machinery , 2009 .

[10]  Xuefeng Chen,et al.  Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

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

[12]  Haifeng Tang,et al.  Sparse representation based latent components analysis for machinery weak fault detection , 2014 .

[13]  Ali Sadollah,et al.  Water cycle algorithm for solving constrained multi-objective optimization problems , 2015, Appl. Soft Comput..

[14]  Cheng Zhang,et al.  Transient extraction based on minimax concave regularized sparse representation for gear fault diagnosis , 2020 .

[15]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[16]  Weiguo Huang,et al.  Multiple Enhanced Sparse Decomposition for Gearbox Compound Fault Diagnosis , 2020, IEEE Transactions on Instrumentation and Measurement.

[17]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[18]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[19]  Yonghao Miao,et al.  Detection and recovery of fault impulses via improved harmonic product spectrum and its application in defect size estimation of train bearings , 2016 .

[20]  Shih-Fu Ling,et al.  Bearing failure detection using matching pursuit , 2002 .

[21]  Ardeshir Bahreininejad,et al.  Water cycle algorithm - A novel metaheuristic optimization method for solving constrained engineering optimization problems , 2012 .

[22]  Carlos Mateo,et al.  Short-time Fourier transform with the window size fixed in the frequency domain , 2017, Digit. Signal Process..

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

[24]  Yong Qin,et al.  Sparse classification based on dictionary learning for planet bearing fault identification , 2018, Expert Syst. Appl..

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

[26]  Jun Wang,et al.  Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis , 2020, IEEE Transactions on Instrumentation and Measurement.

[27]  Antoine Tahan,et al.  A comparative study between empirical wavelet transforms and empirical mode decomposition methods: application to bearing defect diagnosis , 2016 .

[28]  Zhiyong Lu,et al.  A study of information technology used in oil monitoring , 2005 .

[29]  Davide Anguita,et al.  Novel efficient technologies in Europe for axle bearing condition monitoring – the MAXBE project , 2016 .

[30]  Lida Zhu,et al.  Chatter detection in milling process based on VMD and energy entropy , 2018 .

[31]  Zhengjia He,et al.  Wheel-bearing fault diagnosis of trains using empirical wavelet transform , 2016 .

[32]  Kjersti Engan,et al.  Recursive Least Squares Dictionary Learning Algorithm , 2010, IEEE Transactions on Signal Processing.

[33]  Qiang Miao,et al.  Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis , 2018 .

[34]  Zheng Huang,et al.  Online condition monitoring of rolling stock wheels and axle bearings , 2016 .

[35]  Jinfeng Zhang,et al.  Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[36]  Qiang Miao,et al.  Bearing fault diagnosis using a whale optimization algorithm-optimized orthogonal matching pursuit with a combined time–frequency atom dictionary , 2018, Mechanical Systems and Signal Processing.

[37]  Gaigai Cai,et al.  Sparse representation of transients in wavelet basis and its application in gearbox fault feature extraction , 2015 .

[38]  Youming Chen,et al.  A normal distribution model for diffuse radiation versus incidence angle , 2019, Solar Energy.