Convolutional Sparse Coding Using Pathfinder Algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection

Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse r...

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Vydáno v:	IEEE sensors journal Ročník 21; číslo 16; s. 18132 - 18145
Hlavní autoři:	Zhou, Qiuyang, Zhang, Yuhui, Yi, Cai, Lin, Jianhui, He, Liu, Hu, Qiwei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 15.08.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Algorithms asymmetric Gaussian chirplet model Asymmetry Axleboxes Bearing fault diagnosis Coding Convolution Convolutional codes convolutional sparse coding Dictionaries Encoding Fault detection Fault diagnosis Kurtosis Matched pursuit Matching Matching pursuit algorithms orthogonal matching pursuit pathfinder algorithm Representations Signal processing Vibrations Waveforms Wavelet transforms
ISSN:	1530-437X, 1558-1748
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Abstract	Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.
AbstractList	Sparse representation has been widely used in bearing fault impact detection, which can find the impact that best matches the fault waveform from the pre-defined dictionary and recover the fault impulse waveform. However, the current dictionary of sparse representation and the efficiency of sparse representation algorithm need to be improved. In order to accurately detect the fault impulse in the original signal, a convolutional sparse coding using pathfinder algorithm-optimized orthogonal matching pursuit with asymmetric Gaussian chirplet model (CSC-OAGCM) is proposed in this paper. A new time-frequency atom prototype, AGCM, is used to match the fault impulse waveform. The specific application steps of the proposed algorithm are as follows: Firstly, a convolution dictionary is constructed with atoms generated by AGCM. Subsequently, based on the convolution dictionary, a pathfinder algorithm-optimized orthogonal matching pursuit algorithm is used to solve the sparse representation and optimize the atomic parameters to achieve the best approximation of the original signal. In other words, the proposed method detects the convolutional sparse patterns in the signal. A simulation signal, two sets of mixed signals of experimental data collected from the experimental platform and an axle box vibration signal collected from the actual operating train are used to verify the effectiveness of proposed method. Additionally, the spectral kurtosis and empirical wavelet transform are also used to process these signals, and their processing results are compared with those obtained by the proposed method to demonstrate the superiority of the proposed method.
Author	He, Liu Zhou, Qiuyang Zhang, Yuhui Yi, Cai Lin, Jianhui Hu, Qiwei
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SubjectTerms	Algorithms asymmetric Gaussian chirplet model Asymmetry Axleboxes Bearing fault diagnosis Coding Convolution Convolutional codes convolutional sparse coding Dictionaries Encoding Fault detection Fault diagnosis Kurtosis Matched pursuit Matching Matching pursuit algorithms orthogonal matching pursuit pathfinder algorithm Representations Signal processing Vibrations Waveforms Wavelet transforms
Title	Convolutional Sparse Coding Using Pathfinder Algorithm-Optimized Orthogonal Matching Pursuit With Asymmetric Gaussian Chirplet Model in Bearing Fault Detection
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