A review on the application of blind deconvolution in machinery fault diagnosis. (15th January 2022)

• Record Type: Journal Article
• Authors: Miao, Yonghao; Zhang, Boyao; Lin, Jing; Zhao, Ming; Liu, Hanyang; Liu, Zongyang; Li, Hao
• Is Part Of: Mechanical systems and signal processing. Volume 163(2022)
• Publication Date: 2022-01-15
• Subjects: Blind deconvolution -- Machinery fault diagnosis -- Signal processing -- Feature extraction; BDMs Blind deconvolution methods -- WT Wavelet transform -- EMD Empirical mode decomposition -- LMD Local mean decomposition -- VMD Variational mode decomposition -- SVD Singular value decomposition -- KSVD K- Singular value decomposition -- SK Spectral kurtosis -- STFT Short time Fourier transform -- WPT Wavelet packet transform -- MED Minimum entropy deconvolution -- HOS Higher-order statistics -- EVA Eigenvector algorithm -- FIR Finite impulse response -- OFM Objective function method -- OF Objective function -- MCKD Maximum correlated kurtosis deconvolution -- IMCKD Improved MCKD -- CK Correlated kurtosis -- HNR Harmonics-to-noise-ratio -- SHMD Sparse maximum harmonics-to-noise-ratio deconvolution -- OMED Optimal minimum entropy deconvolution -- MOMED Multipoint optimal minimum entropy deconvolution -- CYCBD Maximum second-order cyclostationarity blind deconvolution -- ACYCBD Adaptive CYCBD -- MAKD Maximum average kurtosis deconvolution -- PSO Particle swarm optimization -- MGD Minimum generalized Lp/Lq deconvolution -- MOMEDA Multipoint Optimal Minimum Entropy Deconvolution Adjusted -- AMOMEDA Adaptive MOMEDA -- SNR Signal to noise ratio -- EWT Empirical wavelet transform -- SVM Support vector machines -- CNN Convolutional neural network -- AR Autoregressive; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108202
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A review of blind deconvolution methods (BDMs) in machinery fault diagnosis. History, principle, classic method and applications of BDMs are reviewed. Modified BDMs are classified to review according to their basic principle and merits and limitations are verified. Future prospects of BDMs in machinery fault detection is discussed. Abstract: Fault diagnosis is of significance for ensuring the safe and reliable operation of machinery equipment. Due to the heavy noise and interference, it is difficult to detect the fault directly from the measured signal. Hence, signal processing techniques that can achieve feature extraction, signal denoising, and fault identification are the most common tools in the field. Blind deconvolution methods (BDMs), as one of the most classic methods, have been studied extensively and applied fully for machinery fault diagnosis. Up to now, plenty of publications about the studies and applications of BDMs for machinery fault diagnosis have been presented to academic journals, technical reports, and conference proceedings. This paper intends to survey and summarize the current progress of BDMs applied in machinery fault diagnosis, as well as provides a comprehensive review of BDMs from history to state-of-the-art methods and finally to research prospects. Firstly, the theoretical background and brief history of BDMs are introduced. Secondly, the modified BDMs are classified to review their basic principles. After that their merits and limitations as well as the performance analysis are summarized. Thirdly, the research and application on machinery fault detection using BDMs are overviewed. Finally, the prospects of BDMs in machinery fault diagnosis are discussed.