Residual Lifetime Prediction with Multistage Stochastic Degradation for Equipment

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.

Environmental Externalities of Secondhand Markets—Based on a Dutch Auctioning Company

“…Using the survey data, the residual lifetime dependent on the age of a good and the expected total lifespan will be estimated. Existing methods to predict residual lives usually focus on the technical failure rate, such as the one proposed by Gao et al [52]. As this is not the scope of this paper, a new model is introduced.…”

Job Adjustment Strategy for Predictive Maintenance in Semi-Fully Flexible Systems Based on Machine Health Status

“…Li et al [28] proposed a method for predicting the RUL by changing the degradation rate of systems and causing conditional signal jumps to change points as the two factors. With this information, a multi-stage stochastic degradation model was proposed by [29], using Bayesian updating methods to extract real-time data from machines and update the degradation model for finding the RUL for degraded machines. Further, numerous studies focused on the modelling of the degradation process with an insight to capture the degradation [28][29][30][31][32].…”

“…With this information, a multi-stage stochastic degradation model was proposed by [29], using Bayesian updating methods to extract real-time data from machines and update the degradation model for finding the RUL for degraded machines. Further, numerous studies focused on the modelling of the degradation process with an insight to capture the degradation [28][29][30][31][32]. Few more papers studied various techniques for predicting the RUL and understanding the progression of degradation in machines [22,[33][34][35][36][37].…”