期刊
CHINESE JOURNAL OF AERONAUTICS
卷 27, 期 5, 页码 1086-1096出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.cja.2014.08.013
关键词
Degradation; Information fusion; Kalman filtering; Performance; Prognostics; Remaining useful life; Superstatistics
资金
- State Key Program of National Natural Science of China [61232002]
- Joint Funds of the National Natural Science Foundation of China [60939003]
- China Postdoctoral Science Foundation [2012M521081, 2013T60537]
- Fundamental Research Funds for the Central Universities of China [NS2014066]
- Postdoctoral Science Foundation of Jiangsu Province of China [1301107C]
- Philosophy and Social Science Research Projects in Colleges and Universities in Jiangsu of China [2014SJD041]
Remaining useful life (RUL) prognostics is a fundamental premise to perform condition-based maintenance (CBM) for a system subject to performance degradation. Over the past decades, research has been conducted in RUL prognostics for aeroengine. However, most of the prognostics technologies and methods simply base on single parameter, making it hard to demonstrate the specific characteristics of its degradation. To solve such problems, this paper proposes a novel approach to predict RUL by means of superstatistics and information fusion. The performance degradation evolution of the engine is modeled by fusing multiple monitoring parameters, which manifest non-stationary characteristics while degrading. With the obtained degradation curve, prognostics model can be established by state-space method, and then RUL can be estimated when the time-varying parameters of the model are predicted and updated through Kalman filtering algorithm. By this method, the non-stationary degradation of each parameter is represented, and multiple monitoring parameters are incorporated, both contributing to the final prognostics. A case study shows that this approach enables satisfactory prediction evolution and achieves a markedly better prognosis of RUL. (C) 2014 Production and hosting by Elsevier Ltd. on behalf of CSAA & BUAA.
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