A grade-life fuzzy inference fusion prognostic model for aircraft engine bearings

Xuewen Miao; Yongguo Niu; Yun Yang; Shuyue Yin; Jie Hong

doi:10.1117/12.915001

4 May 2012 A grade-life fuzzy inference fusion prognostic model for aircraft engine bearings

Xuewen Miao, Yongguo Niu, Yun Yang, Shuyue Yin, Jie Hong

Proceedings Volume 8409, Third International Conference on Smart Materials and Nanotechnology in Engineering; 84093C (2012) https://doi.org/10.1117/12.915001
Event: Third International Conference on Smart Materials and Nanotechnology in Engineering, 2011, Shenzhen, China

Abstract

Prognostics and Health Management (PHM) technologies for potential application on aircraft have been maturing rapidly recently since it can ensure safety, equipment reliability, and reduction of costs. The service life prediction of aircraft engine is vital part of PHM technology. Research on practical and verifiable prediction methods for service life of bearing plays a critical role in improving the reliability and safety of aircraft engines. In the paper, the concept of Grade-Life (GL) is introduced to describe the service life of the bearing. A grade-life prognostic model of aircraft engine bearing, which is based on the fuzzy logic inference, is proposed. Firstly, the mathematical model is discussed, which is used to predict the physics-based GL (PGL). Then, the diagnostic estimation model based on SVM is given in details, which is exploited to predict the empirical GL (EPL). Thirdly, a fuzzy logic inference method is adopted to fuse two GL predicted results. Finally, the grade-life prognostic model is verified by the run-to-failure data acquired from accelerated life test of an aircraft bearing. The results accredit that this model provides for a more practical and reliable prediction for service life of bearings.

Citation Download Citation

Xuewen Miao, Yongguo Niu, Yun Yang, Shuyue Yin, and Jie Hong "A grade-life fuzzy inference fusion prognostic model for aircraft engine bearings", Proc. SPIE 8409, Third International Conference on Smart Materials and Nanotechnology in Engineering, 84093C (4 May 2012); https://doi.org/10.1117/12.915001

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