Facility health maintenance through SVR-driven degradation prediction

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.