An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition. (February 2020)

• Record Type: Journal Article
• Authors: Cheng, Jian; Yang, Yu; Li, Xin; Pan, Haiyang; Cheng, Junsheng
• Is Part Of: Measurement. Volume 151(2020)
• Publication Date: 2020-02
• Subjects: Symplectic geometry mode decomposition -- Adding windows reconstruction -- Variable entropy -- Early gear fault; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.107140
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: An improved signal processing method, called ISGMD, is proposed. The short time series can be obtained by adding a slip window. Variable entropy is proposed to measure the amount of fault feature information. The experimental results show that the ISGMD is effective for early gear fault diagnosis. Abstract: Symplectic geometry mode decomposition (SGMD) is an effective signal processing method, and it has been applied in compound fault diagnosis successfully. However, for early gear fault vibration signals, SGMD has two shortcomings. On the one hand, SGMD directly reconstructs the trajectory matrix through the original time series, which may cause the weak fault features submerged in global time series. Therefore, add a slip window to preprocess the original time series. On the other hand, the symplectic geometry components (SGCs) with low energy and fault feature information are eliminated for denoise. Therefore, variable entropy (VE) weighting is proposed to obtain the weighted symplectic geometry components (WSGCs) containing the vast majority of fault feature information. In conclusion, an improved symplectic geometry mode decomposition (ISGMD) is proposed to overcome the above two shortcomings. Simulated and experimental results indicate that ISGMD is effective for raw vibration signals.