Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life(RUL) for the deteriorating systems.This paper presents a method based on probabilistic and stochastic ...Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life(RUL) for the deteriorating systems.This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically monitored degradation processes with dynamic time-varying operational conditions and conditionspecific failure zones.The method assumes that the degradation rate is influenced by specific operational condition and moreover,the transition between different operational conditions plays the most important role in affecting the degradation process.These operational conditions are assumed to evolve as a discrete-time Markov chain(DTMC).The failure thresholds are also determined by specific operational conditions and described as different failure zones.The 2008 PHM Conference Challenge Data is utilized to illustrate our method,which contains mass sensory signals related to the degradation process of a commercial turbofan engine.The RUL estimation method using the sensor measurements of a single sensor was first developed,and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy.The effectiveness and advantages of the proposed method are presented in a comparison with existing methods for the same dataset.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(No.YWF-14-ZDHXY-16)
文摘Dynamic time-varying operational conditions pose great challenge to the estimation of system remaining useful life(RUL) for the deteriorating systems.This paper presents a method based on probabilistic and stochastic approaches to estimate system RUL for periodically monitored degradation processes with dynamic time-varying operational conditions and conditionspecific failure zones.The method assumes that the degradation rate is influenced by specific operational condition and moreover,the transition between different operational conditions plays the most important role in affecting the degradation process.These operational conditions are assumed to evolve as a discrete-time Markov chain(DTMC).The failure thresholds are also determined by specific operational conditions and described as different failure zones.The 2008 PHM Conference Challenge Data is utilized to illustrate our method,which contains mass sensory signals related to the degradation process of a commercial turbofan engine.The RUL estimation method using the sensor measurements of a single sensor was first developed,and then multiple vital sensors were selected through a particular optimization procedure in order to increase the prediction accuracy.The effectiveness and advantages of the proposed method are presented in a comparison with existing methods for the same dataset.
