Condition Monitoring of Mechanical Components Based on MEMED-NLOPE under Multiscale Features. (11th April 2022)

• Record Type: Journal Article
• Authors: Wang, Xuan; She, Bo; Shi, Zhangsong; Sun, Shiyan; Qin, Fenqi
• Other Names: Feng Ke Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2022(2022)
• Publication Date: 2022-04-11
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2022/3145402
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : An increasing popularity of researches focuses on the vibration signal with the characteristics of nonstationary, nonlinear, and strong noise interference. A nonlinear dimension and feature reduction method called multiple empirical mode entropy decomposition-nonlocal orthogonal preserving embedding (MEMED-NLOPE) is proposed to implement condition monitoring in this paper. Different from multiple empirical mode decomposition (MEMD), MEMED adopts maximum entropy method, which can directly output the subsignal with the maximum correlation and realize nonlinear dimensionality reduction. Besides, multiscale feature extraction method is used during preprocessing nonlinear data process, which realizes feature reduction. Finally, nonlocal orthogonal preserving embedding algorithm-exponentially weighted moving average (NLOPE-EWMA) realizes the automatic detection of the fault. Taking the laboratory rolling bearing test and naval gun pendulum mechanism test as cases, the effectiveness of MEMED-NLOPE is verified.