Symplectic geometry transformation based periodic segment method: Algorithm and Applications

• Published in: IEEE transactions on instrumentation and measurement Vol. 72; p. 1
• Main Authors: Pan, Haiyang, Zhang, Ying, Cheng, Jian, Zheng, Jinde
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Background noise; Covariance matrices; Decomposition; Defects; Eigenvalues; Estimation; Fault diagnosis; Feature extraction; Geometry; Hankel matrices; Matrix decomposition; Noise intensity; Optimized periodic segment matrix; Period estimation; periodic pulse extraction; Roller bearings; Rolling bearings; Segments; Symplectic geometry transformation based periodic segment; Transformations
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3271006

Symplectic geometry mode decomposition (SGMD) method takes the Hankel matrix as the trajectory matrix, and the eigenvalue of trajectory matrix can be obtained by symplectic geometry similarity transformation (SGST). However, with the increase of noise intensity, SGMD method based on Hankel matrix cannot distinguish the fault signal excited by defects and background noise at the same order of magnitude. Based on this, a symplectic geometry transformation based periodic segment (SGT-PS) method is proposed. In SGT-PS, a neighboring peak method is designed to estimate the signal period and determine the variable parameters, which overcomes the defect that SGMD is difficult to extract the periodic pulse components. Meanwhile, optimized periodic segment matrix (OPSM) is defined to segment periodic pulse information, reduce the accumulated error and improve the accuracy of periodic pulse extraction. The analysis results of roller bearing fault signals show that SGT-PS is an effective signal decomposition method, which can accurately extract the periodic pulse.