Abstract
In practical applications, the failure of large-scale complex equipment is often caused by the simultaneous degradation of multiple components. It is necessary to predict the remaining useful life (RUL) of the equipment with multiple degradation indicators. This article proposes a new joint-RUL-prediction method in the presence of multiple degradation indicators based on parameter correlation. The stochastic process model is established for each degradation indicator, and the model parameters are estimated by kernel smoothing particle filter (KS-PF) and maximum likelihood estimation (MLE). Meanwhile, to facilitate the dependencies between multiple degradation indicators, correlations of the degradation model parameter between multiple degradation indicators are established in KS-PF. In addition, optimal tuning (OT) is introduced to choose the best kernel parameter. A case study on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset is applied to verify the proposed method, the experiment shows that the proposed joint-RUL-prediction method based on parameter correlation possesses a superior prediction performance compared with that by using a single degradation indicator.
Highlights
A S an essential part of prognostics and health management (PHM), remaining useful life (RUL) prediction can effectively help to improve the reliability of equipment, avoid the huge loss caused by equipment failure, and reduce the cost of equipment maintenance
The weighted particles {xij, aij, bij, wji }Ni=1 can be obtained at time tj using this joint estimation method with multiple degradation indicators based on parameter correlation, and the obtained particle xij = [xi1,j, · · ·, xiK,j]T representing the estimated states of multiple indicators is multidimensional with each dimension represents the state of the corresponding degradation indicator
In this paper, a joint-RUL-prediction method with multiple degradation indicators based on parameter correlation is proposed by combining the stochastic process model and particle filter (PF)
Summary
A S an essential part of prognostics and health management (PHM), remaining useful life (RUL) prediction can effectively help to improve the reliability of equipment, avoid the huge loss caused by equipment failure, and reduce the cost of equipment maintenance. Fang et al [18] proposed a bivariate stochastic process model, with the dependencies establishing on the degradation states, and the Bayesian method was used for parameter estimation. With the establishment of correlations of model parameters, the degradation states of indicators which are estimated using model parameters can capture the long term dependencies among different indicators effectively. A joint-RUL-prediction method based on the stochastic process model and PF is proposed in this paper, characterizing the dependencies between multiple degradation indicators as the correlations between parameters of the models built for the corresponding indicators. The long-term dependencies among different indicators are taken into consideration using the correlation between parameters, and the multivariate degradation modeling in the proposed method can provide accurate information of dependencies for RUL prediction.
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