Symplectic Ramanujan Mode Decomposition and its application to compound fault diagnosis of bearings. (October 2022)

• Record Type: Journal Article
• Authors: Cheng, Jian; Yang, Yu; Wu, Xiaowei; Wang, Jian; Wu, Zhantao; Cheng, Junsheng
• Is Part Of: ISA transactions. Volume 129(2022)Part A
• Page Start: 495
• Page End: 503
• Publication Date: 2022-10
• Subjects: Symplectic Ramanujan mode decomposition -- Compound-fault diagnosis -- Ramanujan subspace -- Signal processing -- Periodic impulse extraction; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.12.013
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: The existing compound-fault diagnosis methods of rolling bearings have their own defects, which makes their accuracy of fault diagnosis impossible to be guaranteed. Therefore, this paper attempts to combine symplectic similarity transformation with Ramanujan subspace theory, and then a periodic impulse extraction method called symplectic Ramanujan mode decomposition (SRMD) method is proposed. SRMD separates the components with different fault features through symplectic similarity transformation and hierarchical clustering method to obtain symplectic clustering components (SCCs). At the same time, SRMD uses the Ramanujan subspace theory to extract the major periodic impulse components of each component to be extracted, and then obtains symplectic Ramanujan components (SRCs). The results show that SRMD is a resultful method in compound-fault diagnosis of bearings with excellent periodic impulse extraction ability. Highlights: A novel periodic impulse extraction method, called SRMD, is proposed for composite fault diagnosis of rolling bearings. Symplectic similarity transformation and Ramanujan subspace theory are introduced into composite fault diagnosis of rolling bearings. The proposed method not only has excellent signal separation ability, but also has excellent periodic impulse extraction ability.