A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery |
| |
| Affiliation: | 1. School of Mechatronics Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, PR China;2. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, PR China;1. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. State Key Laboratory of Technologies in Space Cryogenic Propellants, Beijing 100028, China;1. College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, Hunan, PR China;2. Department of Energy Sciences, Lund University, Lund 22100, Sweden;1. School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China;2. State Key Laboratory of Technologies in Space Cryogenic Propellants, Beijing 100028, China |
| |
| Abstract: | In order to extract fault features of large-scale power equipment from strong background noise, a hybrid fault diagnosis method based on the second generation wavelet de-noising (SGWD) and the local mean decomposition (LMD) is proposed in this paper. In this method, a de-noising algorithm of second generation wavelet transform (SGWT) using neighboring coefficients was employed as the pretreatment to remove noise in rotating machinery vibration signals by virtue of its good effect in enhancing the signal–noise ratio (SNR). Then, the LMD method is used to decompose the de-noised signals into several product functions (PFs). The PF corresponding to the faulty feature signal is selected according to the correlation coefficients criterion. Finally, the frequency spectrum is analyzed by applying the FFT to the selected PF. The proposed method is applied to analyze the vibration signals collected from an experimental gearbox and a real locomotive rolling bearing. The results demonstrate that the proposed method has better performances such as high SNR and fast convergence speed than the normal LMD method. |
| |
| Keywords: | Rotating machinery Fault diagnosis Second generation wavelet de-noising Local mean decomposition Signal–noise ratio |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
