RUL prediction based on two‐phase wiener process

Abstract

The degradation model and the remaining useful life (RUL) prediction are two key metrics for machine health prognostics. However, the precision of RUL prediction highly depends on the validity of the degradation model. Consequently, the uncertainty of RUL prediction based on the two-phase degradation process may cause the estimated product reliability to differ from its actual value and lead to a faulty predictive maintenance strategy. However, recent studies have rarely compared the RUL predictions based on two-phase and single-phase degradation models. In this study, a real-time RUL prediction approach was developed for a two-phase linear Wiener degradation process. A two-phase Wiener degradation model was proposed along with an estimation method for the change point and model parameters. Based on a state-space model and Markov chain Monte Carlo, the drift coefficient of the degradation model was updated, and a real-time RUL prediction model was developed. To examine whether the twophase Wiener degradation model was incorrectly assumed to be single-phase, a case study was conducted to analyze RUL prediction based on the original and incorrect models. The results demonstrated that to obtain a more accurate RUL prediction, a sufficient number of samples should be used in addition to updating the drift coefficient of the degradation model when new measurement data are available.

Remaining useful life prediction for nonlinear two-phase degradation process with measurement errors and imperfect prior information

“…To further illustrate the superiority of the proposed EM algorithm and Kalman smoothing method, the maximum likehood estimation (MLE) algorithm is comparedwith it. Liu's method [26] is selected in the linear phase and Si's method [29] is selected…”

“…The PDF and prediction values of RUL are shown in figure 5. As a comparison, Liu's method [26] (i.e. the RUL prediction method of the two-phase linear model), Zhang's method [24] (i.e.…”

“…Chen et al [19] adopted a twophase Gaussian process with a change point to establish the degradation model and estimated the model parameters and the change point simultaneously. Liu et al [26] established a two-phase linear Wiener degradation model and ignored the influence of the second-phase parameters on the first-phase degradation. Wang et al [27] and Zhang et al [24] developed a general degradation model framework based on a two-phase Wiener process and derived a probability density function (PDF) of RUL in a closed form.…”

Fusing sensitive degradation features with uncertainty analysis for RUL prediction of rotating machines

“…The whole process of proposed RUL prediction method mainly includes degradation feature extraction and selection, HI construction and RUL prediction [20,21], which is illustrated in figure 1.…”

Modeling of transformer oil-paper insulation deterioration based on improved Wiener model

“…Gao et al [2] took the exponential deterioration paths as logarithmic numbers, linearized them, and then combined them with the Wiener model for the deterioration modeling. Liu et al [3] used the time scale transformation to convert nonlinear data to linear for deterioration modeling. Then some scholars analyzed the magnitude of capacitance loss value of metallized film capacitors by the Wiener model and discussed the reliability of capacitor operation.…”