Rolling Bearing Fault Diagnosis Based on Nonlinear Underdetermined Blind Source Separation

被引:1
作者
Zhong, Hong [1 ]
Ding, Yang [1 ]
Qian, Yahui [1 ]
Wang, Liangmo [1 ]
Wen, Baogang [2 ]
机构
[1] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Peoples R China
[2] Dalian Polytech Univ, Sch Mech Engn & Automat, Dalian 116034, Peoples R China
基金
中国国家自然科学基金;
关键词
bearing; fault diagnosis; underdetermined blind source separation; fuzzy C-means clustering; sparse component analysis; EMPIRICAL MODE DECOMPOSITION;
D O I
10.3390/machines10060477
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
One challenge of bearing fault diagnosis is that the vibration signals are often a nonlinear mixture of unknown source signals. In addition, the practical installation position also limits the number of observed signals. Hence, bearing fault diagnosis is a nonlinear underdetermined blind source separation (UBSS) problem. In this paper, a novel nonlinear UBSS solution based on source number estimation and improved sparse component analysis (SCA) is proposed. Firstly, the ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), and adaptive threshold singular value decomposition (ATSVD) joint approach is proposed to estimate the source number. Then, the observed signals are transformed into the time-frequency domain by short-time Fourier transform (STFT) to meet the sparsity requirement of SCA. The frequency energy is adopted to increase the accuracy of fuzzy C-means (FCM) clustering, so as to ensure the accuracy estimation of the mixing matrix. The L1-norm minimization is utilized to recover the source signals. Simulation results prove that the proposed UBSS solution can exactly estimate the source number and effectively separate the simulated signals in both linear and nonlinear mixed cases. Finally, bearing fault testbed experiments are conducted to verify the validity of the proposed approach in bearing fault diagnosis.
引用
收藏
页数:18
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