An adaptive and efficient variational mode decomposition and its application for bearing fault diagnosis

被引:107
作者
Jiang, Xingxing [1 ]
Wang, Jun [1 ]
Shen, Changqing [1 ]
Shi, Juanjuan [1 ]
Huang, Weiguo [1 ]
Zhu, Zhongkui [1 ]
Wang, Qian [2 ]
机构
[1] Soochow Univ, Sch Rail Transportat, 8 Jixue Rd, Suzhou 215137, Jiangsu, Peoples R China
[2] Soochow Univ, Wenzheng Coll, Suzhou, Peoples R China
来源
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL | 2021年 / 20卷 / 05期
基金
中国博士后科学基金; 中国国家自然科学基金; 国家重点研发计划;
关键词
Adaptive signal processing; convergent tendency; fault diagnosis; rolling element bearing; variational mode decomposition; SPECTRAL KURTOSIS; VMD; OPTIMIZATION; EXTRACTION; TRANSFORM; SPEED;
D O I
10.1177/1475921720970856
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Variational mode decomposition has been widely applied to machinery fault diagnosis during these years. However, it remains difficult to set proper hyperparameters for the variational mode decomposition, including number of decomposed modes, initial center frequencies, and balance parameter. Moreover, the low efficiency of the existing variational mode decomposition methods hinders their applications to practical diagnostic task. This article proposes an adaptive and efficient variational mode decomposition method after thoroughly investigating its convergence property characteristic. A convergent tendency phenomenon is discovered and is explained mathematically for the first time. Motivated by the convergent tendency phenomenon, the proposed method rapidly and adaptively determines the number and the optimal initial center frequencies of signal latent modes with the guidance of the convergent tendencies of the initial center frequencies changing from small to large. In the proposed method, the number of decomposed modes and initial center frequencies are not hyperparameters that require to be set in advance any more, but are parameters learned from the analyzed signals. The determined parameters enable efficient extraction of the main latent modes contained in the analyzed signals. Therefore, the proposed variational mode decomposition method represents a major improvement in parameter adaption and decomposition efficiency over the existing variational mode decomposition methods. In the application for bearing fault diagnosis, the faulty modes are selected adaptively and the corresponding balance parameters are further optimized efficiently. Two experimental cases validate the proposed method and its superiority over the existing variational mode decomposition methods and the classical fast spectral kurtosis in bearing fault diagnosis.
引用
收藏
页码:2708 / 2725
页数:18
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