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

作     者：Zhou, Qiuyang Zhang, Yuhui Yi, Cai Lin, Jianhui He, Liu Hu, Qiwei 

作者机构：Southwest Jiaotong Univ State Key Lab Tract Power Chengdu 610031 Peoples R China Qingdao Univ Sci & Technol Coll Automat & Elect Engn Qingdao 260061 Peoples R China China Railway Elect Survey Design & Res Inst Co L Tianjin 300250 Peoples R China 

出 版 物：《IEEE SENSORS JOURNAL》 (IEEE传感器杂志)

年 卷 期：2021年第21卷第16期

页      面：18132-18145页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0804[工学-仪器科学与技术] 0702[理学-物理学] 

基　　金：National Natural Science Foundation of China National Key Research and Development Program of China [2020YFB1200300ZL-03, 2018YFB1201603-14] China Postdoctoral Science Foundation [2019M663899XB] Fundamental Research Foundations for the Central Universities [2682020CX50] Research Fund of the State Key Laboratory of Traction Power [2020TPL-T14] 

主　　题：Dictionaries Convolution Matching pursuit algorithms Fault diagnosis Vibrations Convolutional codes Encoding Bearing fault diagnosis convolutional sparse coding asymmetric Gaussian chirplet model pathfinder algorithm orthogonal matching pursuit 

摘      要：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.