Facility health maintenance through SVR-driven degradation prediction Online publication date: Wed, 30-Jul-2008
by Xiangang Cao, Pingyu Jiang, Guanghui Zhou
International Journal of Materials and Product Technology (IJMPT), Vol. 33, No. 1/2, 2008
Abstract: In order to realise the health monitoring and maintenance of complex facilities with multiple degradation parameters, a facility synthetic failure probability model to map between inputs and probability of failure is established through adopting the logistic regression to synthesise each degradation parameter. Then, a SVR-driven degradation trend prediction and estimate of Remaining Useful Life (RUL) method is put forward. Last, based on Monte-Carlo method, a multi-parameters equipment emulator according with Weibull distribution is established to test the model. The results show that these methods are practicable.
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