Abstract
The latest demands for remaining useful life (RUL) prediction are online prediction, real-time prediction and adaptive prediction. This paper addresses the demands of RUL prediction and proposes a novel framework of parallel simulation based adaptive prediction for equipment RUL. In the framework, a Wiener state space model (WSSM) is developed to achieve the aim, which considers the whole historical data and monitoring noise. Driven by the online observation data, the degradation state is estimated by the Kalman filter based data assimilation and the WSSM parameters are updated by the expectation maximum algorithm. An analytical RUL distribution considering the distribution of the degradation state is obtained based on the concept of the first hitting time. A case study for GaAs laser device is provided to substantiate the superiority of the proposed method compared with the competing method of traditional Wiener process. The results show that the parallel simulation method can provide better RUL prognostic accuracy.
Highlights
In the field of equipment maintenance support, the PHM is an important means to achieve equipment accurate maintenance [1]
The corresponding degradation data is obtained by online monitoring equipment and the remaining useful life is predicted through offline feature extraction
This paper focuses on the evolutionary modeling of parallel simulation based adaptive prediction for the equipment remaining useful life (RUL)
Summary
In the field of equipment maintenance support, the PHM is an important means to achieve equipment accurate maintenance [1]. On the basis of the linear Wiener process, Wang [1] first introduced the adaptive drift coefficient and used the Kalman filter to estimate the degradation state by utilizing the whole historical data. Si [18] proposed an iterative degradation model based on the linear Wiener process, which took the uncertainty of the drift state into account and reduced the prediction uncertainty. There were many researches on equipment RUL prediction based on the linear Wiener process, few researchers predicted the RUL by considering the measurement noise and the whole historical data simultaneously. On the basis of degradation modeling, the parallel simulation based equipment RUL prediction framework is proposed. The framework realizes the online prediction, real-time prediction and adaptive prediction of RUL
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