Uncertainty is a key problem in remaining useful life (RUL) prediction, and measures to
reduce uncertainty are necessary to make RUL prediction truly practical. In this paper, a
right-time prediction method is proposed to reduce the prognostics uncertainty of mechanical systems under unobservable degradation. Correspondingly, the whole RUL prediction process is divided into three parts, including offline modelling, online state estimating and online life predicting. In the offline modelling part, hidden Markov model (HMM) and proportional hazard model (PHM) are built to map the whole degradation path. During operation, the degradation state of the object is estimated in real time. Once the last degradation state reached, the degradation characteristics are extracted, and the survival function is obtained with the fitted PHM. The proposed method is demonstrated on an engine dataset and shows higher accuracy than traditional method. By fusing the extracted degradation characteristics, the obtained survival function can be basis for optimal maintenance with lower uncertainty.
In this paper, a new network model is developed for better expression of the interaction of a complex electromechanical system. The developed model can reflect the current reliability fluctuation followed by system state disturbances. The Failure Mode and Effect Analysis (FMEA) data and indicator data from detection and monitoring is collected and processed for the structure and parameters of this network model. Hidden Markov Model (HMM) is used for transforming indicators into the probability of fault causes. The recorded fault statistic data is considered as the prior knowledge, and the current indicator data can be used to renew the prior probability of component failure and system malfunction. With this model, the current component failure probability and system malfunction probability can be obtained.
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