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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Veröffentlicht in:IEEE sensors journal Jg. 21; H. 16; S. 18132 - 18145
Hauptverfasser: Zhou, Qiuyang, Zhang, Yuhui, Yi, Cai, Lin, Jianhui, He, Liu, Hu, Qiwei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 15.08.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2021.3086015