Compound Fault Diagnosis Using Optimized MCKD and Sparse Representation for Rolling Bearings

被引:165
作者
Deng, Wu [1 ,2 ]
Li, Zhongxian [1 ]
Li, Xinyan [1 ]
Chen, Huayue [3 ]
Zhao, Huimin [1 ,2 ]
机构
[1] Civil Aviat Univ China, Sch Elect Informat & Automat, Tianjin 300300, Peoples R China
[2] Southwest Jiaotong Univ, Tract Power State Key Lab, Chengdu 610031, Peoples R China
[3] China West Normal Univ, Sch Comp Sci, Nanchong 637002, Peoples R China
基金
中国国家自然科学基金;
关键词
Compounds; Fault diagnosis; Feature extraction; Rolling bearings; Optimization; Dictionaries; Deconvolution; Compound fault diagnosis; feature extraction; intelligent optimization; maximum correlation kurtosis deconvolution (MCKD); sparse representation;
D O I
10.1109/TIM.2022.3159005
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
The effective separation of fault characteristic components is the core of compound fault diagnosis of rolling bearings. The intelligent optimization algorithm has better global optimization performance and fast convergence speed. Aiming at the problem of poor diagnosis effect caused by mutual interference between multiple fault responses, a novel compound fault diagnosis method based on optimized maximum correlation kurtosis deconvolution (MCKD) and sparse representation, namely MDSRCFD, is proposed in this article. For the MCKD, because it is very difficult to set reasonable parameter combination values, artificial fish school (AFS) with global search capability and strong robustness is fully utilized to optimize the key parameters of MCKD to achieve the best deconvolution and fault feature separation. Aiming at the problem that orthogonal matching pursuit (OMP) is difficult to be solved in sparse representation, an artificial bee colony (ABC) with global optimization ability and faster convergence speed is employed to solve OMP to obtain the approximate best atom and realize the reconstruction of signal transient components. The envelope demodulation analysis method is applied to realize feature extraction and fault diagnosis. The simulation and practical application results show that the proposed MDSRCFD can effectively separate and extract the compound fault characteristics of rolling bearings, which can realize the accurate compound fault diagnosis.
引用
收藏
页数:9
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