Abstract
Accurate remaining useful life (RUL) prediction has a great significance to improve the reliability and safety for key equipment. However, it often occur imperfect or even no prior degradation information in practical application for the existing RUL prediction methods, which could produce prediction error. To solve this issue, this paper proposes a two-step RUL prediction method based on Wiener processes with reasonably fusing the failure time data and field degradation data. First, we obtain some interesting natures of parameters estimation based on the basic linear Wiener process. These natures explain the relationship between the parameters estimation results and the feature of degradation data, i.e. item sample numbers, detection time and detect frequency, and give the basis regarding how to reasonably fuse the failure time data and field degradation data. Second, under the Bayesian framework, we further propose a two-step method by fusing the failure time data and field degradation data with considering the random effects based on the proposed natures of parameters estimation. In this method, we propose an EM algorithm to estimate the mean and variance drift parameter of Wiener processes by the failure time data. Next, we generalize this two-step RUL prediction method to the nonlinear Wiener process. Last, we use two case studies to demonstrate the usefulness and superiority of the proposed method.
Highlights
Engineering practice shows that prognostics and health management (PHM) can reduce maintenance costs, improve the reliability and safety, and reduce the risk of failure events [1]
From the above works of the remaining useful life (RUL) prediction based on linear Wiener process and nonlinear Wiener process, we can observe that when the prior information is accurate, M1 and M2 obtain similar results
RUL prediction is great important in PHM
Summary
Engineering practice shows that prognostics and health management (PHM) can reduce maintenance costs, improve the reliability and safety, and reduce the risk of failure events [1]. We present a RUL prediction method for the degradation model with random effects by these natures of parameters estimation This method first applies field degradation data to estimate the fixed parameters that represent common characteristics of the model, and use history failure time data to estimate the prior distributions of drift parameter that represent personality features based on the EM algorithm. (3) Based on the above conclusions, we can use the failure time data to estimate the prior information of drift parameter, and the field degradation data to estimate the diffusion parameter Proof: Given the drift parameter λv of a specific item, according to the nature of Wiener process, the failure time Tv obeys inverse Gaussian distribution, and the likelihood function of Tv can be written as follow: L(Tv|λv) =. The PDF of the RUL can be written as follows [10]: fLk |x1:k ,w (lk |x1:k , w) 1
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