Symplectic geometry packet decomposition and its applications to gear fault diagnosis. (15th July 2022)

• Record Type: Journal Article
• Authors: Cheng, Jian; Yang, Yu; Li, Xin; Cheng, Junsheng
• Is Part Of: Mechanical systems and signal processing. Volume 174(2022)
• Publication Date: 2022-07-15
• Subjects: Symplectic geometry packet decomposition -- Noise robustness -- Gear fault diagnosis -- Signal decomposition; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109096
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel time–frequency signal decomposition method, called SGPD, is proposed for gear fault diagnosis. The noise reduction order and the number of effective components are determined by two-order correction contribution rate. Multi-layer decomposition is used to reduce noise and keep the fault information to the maximum extent. The simulated and experimental results show that the proposed method is effective for gear fault diagnosis. Abstract: There are many signal decomposition methods in gear fault diagnosis at present, such as ensemble empirical mode decomposition (EEMD), wavelet transform (WT), singular spectral analysis (SSA) and symplectic geometry mode decomposition (SGMD). However, these methods have some defects. Especially, when analyze gear fault signals with strong background noise, the noise reduction performance of EEMD and WT cannot meet the requirements. SSA and SGMD can delete fault information as noise, which seriously affect the accuracy of fault diagnosis. Therefore, a novel multi-layer decomposition method, symplectic geometry packet decomposition (SGPD) is proposed. In essence, SGPD combines symplectic geometry theory and multi-layer decomposition idea of wavelet packet to decompose the signal into a series of independent components containing the main fault information. SGPD not only has excellent signal decomposition ability, but also can minimize noise while retaining the fault information of the original signals in the process of sufficiently decomposing the non-steady signal. The analysis results of the emulational and experimental signals indicate that SGPD has strong signal decomposition capabilities and noise robustness.