Wang, Zhenya and Luo, Qiusheng and Chen, Hui and Zhao, Jingshan and Yao, Ligang and Zhang, Jun and Chu, Fulei (2024) A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples. Computers in Industry, 159. p. 104099. ISSN 0166-3615, DOI https://doi.org/10.1016/j.compind.2024.104099.
Full text not available from this repository.Abstract
As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine ` s reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83 %, 100 %, and 100 %, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97 %. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.
| Item Type: | Article |
|---|---|
| Funders: | National Key R & D Program of China (2022YFB4702401), National Natural Science Foundation of China (NSFC) (52375043) ; (52375009), Postdoctoral Fellowship Program of CPSF (GZC20231284), Fujian Provincial Science and Technology Major Special Project (2021HZ024006) ; (2022HZ026025) |
| Uncontrolled Keywords: | Aero-engine bearing; Fault diagnosis; Multiscale phase entropy; Bonobo optimizer; Support vector machine |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 17 Sep 2024 06:25 |
| Last Modified: | 17 Sep 2024 06:25 |
| URI: | http://eprints.um.edu.my/id/eprint/45117 |
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