A new method to identify rotor–stator rubbing positions based on intrinsic time scale decomposition. (March 2022)

• Record Type: Journal Article
• Authors: Yu, Mingyue; Chen, Wangying; Wang, Jinglin; Cong, Haonan
• Is Part Of: Noise & vibration worldwide. Volume 53:Number 3(2022)
• Page Start: 120
• Page End: 132
• Publication Date: 2022-03
• Subjects: normalized energy -- proper rotation component -- rubbing faults -- positions identification -- time scale decomposition; Noise control -- Periodicals; Damping (Mechanics) -- Periodicals; Soundproofing -- Periodicals; Damping (Mechanics); Noise control; Soundproofing; Periodicals; 620.205
• DOI: 10.1177/09574565211055785
• Languages: English
• ISSNs: 0957-4565

Abstract

To effectively identify the rotor–stator rubbing positions in aero-engine, the paper has proposed the combination of intrinsic time-scale decomposition (ITD) and classification algorithm. Regarding that with larger noise component in proper rotation component (PRC) signals after ITD, it will be more difficult to extract the characteristic information of rubbing faults, the PRC correspondings to the largest noise was eliminated. Meanwhile, signals were reconstructed based on residual proper rotation components, and positions of rubbing faults were identified according to the reconstructed signal. As rubbing extent and other factors cannot be completely the same in each rubbing, energy of reconstructed signal has been normalized to reduce the difference. Normalized energy indexes were inputted into classification algorithm as feature vectors to identify the positions of rubbing faults. To identify the superiority of approach, a comparison has been made between the proposed approach and the method of directly extracting normalized energy indexes of acceleration signals. The result of comparison shows that the two methods both work well in the identification rate of training and test samples; as for the identification rate for an unknown sample, the proposed method is superior to the other, with identification rate increasing by 17% and 9.4%.