Double-dictionary signal decomposition method based on split augmented Lagrangian shrinkage algorithm and its application in gearbox hybrid faults diagnosis. (13th October 2018)

• Record Type: Journal Article
• Authors: Yang, Xiaoqing; Ding, Kang; He, Guolin; Li, Yongzhuo
• Is Part Of: Journal of sound and vibration. Volume 432(2018)
• Page Start: 484
• Page End: 501
• Publication Date: 2018-10-13
• Subjects: Double-dictionary -- Coupling modulation signal -- SALSA -- Hard threshold de-noising; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2018.06.064
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Gearbox with hybrid distributed and localized faults usually generates coupled modulation vibration signal. It is hard to decompose the coupled signal for precise diagnosis. To solve this problem, a novel signal decomposition method is proposed on the basis of double-dictionary and split augmented Lagrangian shrinkage algorithm (SALSA). The dictionary possessing high similarity to fault features consists of steady modulation sub-dictionary and impact modulation sub-dictionary. The SALSA is improved by adding a hard threshold de-noising to obtain optimal sparse coefficients of steady modulation and impact modulation components. Key parameters including Lagrange multipliers ( λ s, λ p ), penalty factor μ of SALSA and hard threshold ε are studied to determine their optimal value ranges. Kinds of simulation signals show the effectiveness of the proposed method, and experimental tests on fixed-shaft gearbox and planetary gearbox further verify the reliability. Comparative analyses with methods respectively based on matching pursuit and tunable Q-factor wavelet transform indicate that the proposed method is superior to the other two methods in calculation efficiency and anti-noise performance, especially when these two kinds of modulation components are completely coupled in some resonance bands. Highlights: A double-dictionary with high similarity to fault feature is designed. SALSA and hard threshold de-noising are combined for optimal sparse coefficients. Influences of parameters on SALSA and signal decomposition result are researched. Simulations and experimental tests verify the effectiveness of proposed method.