Abstract
Residual useful lifetime (RUL) prediction plays a key role of failure prediction and health management (PHM) in equipment. Aiming at the problems of residual life prediction without comprehensively considering multistage and individual differences in equipment performance degradation at present, we explore a prediction model that can fit the multistage random performance degradation. Degradation modeling is based on the random Wiener process. Moreover, according to the degradation monitoring data of the same batch of equipment, we apply the expectation maximization (EM) algorithm to estimate the prior distribution of the model. The real-time remaining life distribution of the equipment is acquired by merging prior information of real-time degradation data and historical degradation monitoring data. The accuracy of the proposed model is demonstrated by analyzing a practical case of metalized film capacitors, and the result shows that a better RUL estimation accuracy can be provided by our model compared with existing models.
Highlights
Prognostics and health management (PMH) has been a systematic method utilized to evaluate the reliability or residual life of actual life-cycle conditions in a system, predict failure degree, reduce the risk in operating, improve task completion rate, and make maintenance decisions
There are a great deal of research studies as to Residual useful lifetime (RUL) prediction have been developed for a large wide range of industrial products, for example, bearings [5, 6], gearboxes [7], lithium batteries [8], organic light emitting diodes [9], and laser generators [10]. e main idea of RUL is to realize the life prediction by obtaining the distribution or expectation of RUL according to the effective information such as equipment failure mechanism, condition monitoring (CM) data, and failure time data [11, 12]
Aiming at the above problems, in this paper, a multistage random degradation model based on the Wiener process is proposed to estimate the RUL of equipment aiming at above issues
Summary
Prognostics and health management (PMH) has been a systematic method utilized to evaluate the reliability or residual life of actual life-cycle conditions in a system, predict failure degree, reduce the risk in operating, improve task completion rate, and make maintenance decisions. In reference to the attenuation process of the brightness of light emitting diodes, Wang et al in [9] assumed that individual differences exist in the change points and utilized a two-stage Wiener process model and Bayesian estimation method to analyze the degradation data under logarithmic transformation. Aiming at the above problems, in this paper, a multistage random degradation model based on the Wiener process is proposed to estimate the RUL of equipment aiming at above issues. Assuming that the parameters of the degradation model follow, respectively, certain random distributions to describe the differences between single equipment, an EM algorithm is used for iteratively estimating the prior distribution of the degradation model parameters based on the historical degradation data and historical failure time data. By analyzing the degradation process of metallized film capacitors, the remaining life expectancy of individual capacitors can be predicted
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