A novel signature extracting approach for inductive oil debris sensors based on symplectic geometry mode decomposition. (November 2021)

• Record Type: Journal Article
• Authors: Yu, Bing; Cao, Nan; Zhang, Tianhong
• Is Part Of: Measurement. Volume 185(2021)
• Publication Date: 2021-11
• Subjects: Symplectic geometry mode decomposition -- Inductive oil debris sensor -- Signal decomposition; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110056
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Application of symplectic geometry mode decomposition to debris signal extraction. Processing method without custom parameters. Method combined with empirical model decomposition. Faster and more efficient method than traditional signal decomposition. Abstract: On line lubricating oil debris measurement is an efficient way to judge the operating status of a machine, and the inductive oil debris sensor is widely adopted. Considering the debris sensor works in a harsh environment, it is a challenge work to reduce or separate the noise via signal processing, especially for the requirements of detecting small-sized wear debris. To overcome the limitations of existing methods, a novel signal decomposition method called symplectic geometry mode decomposition (SGMD) is proposed to extract the signature of oil debris. SGMD can remodel the state and eliminating noise adaptively, and the simulation results manifest that SGMD can extract the signature of debris accurately and effectively. When analyzing the experimental signal, the SGMD and EMD are combined, and the results show that it has a better decomposition ability than EMD or wavelet decomposition.