希尔伯特-黄变换
特征提取
反褶积
算法
熵(时间箭头)
断层(地质)
控制理论(社会学)
近似熵
模式识别(心理学)
计算机科学
数学
人工智能
白噪声
统计
物理
量子力学
地质学
地震学
控制(管理)
作者
Xiangyu Zhou,Yibing Li,Li Jiang,Li Zhou
出处
期刊:Measurement
[Elsevier]
日期:2021-03-01
卷期号:173: 108469-108469
被引量:40
标识
DOI:10.1016/j.measurement.2020.108469
摘要
Extracting fault feature is hard to realize because of weak fault impact components and environmental noise interference in vibration signals. Thus, a hybrid fault diagnosis method based on parameter-adaptive variational mode decomposition (VMD) and multi-point optimal minimum entropy deconvolution (MOMEDA) is proposed. Firstly, whale optimization algorithm (WOA) is employed to solve VMD parameter selection problem. Then a series of modes are obtained by parameter-adaptive VMD. Secondly, the effective modes whose index values are greater than the average index value are selected for reconstruction to enhance the impulse related to fault characteristics. Finally, periodic pulse signal is extracted from the reconstructed signal by MOMEDA. Fault characteristic frequencies can be identified from envelope spectra. The proposed method is verified to be effective based on two different experimental datasets. Moreover, the comparisons with fast kurtogram, ensemble empirical mode decomposition (EEMD) and the other latest methods further highlight its superiority of fault feature extraction.
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