Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomne...Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.展开更多
The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that t...The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.展开更多
基金supported by the National Natural Science Foundation of China(61703410,61873175,62073336,61873273,61773386,61922-089)the Basic Research Plan of Shaanxi Natural Science Foundation of China(2022JM-376).
文摘Remaining useful life(RUL)prediction is one of the most crucial components in prognostics and health management(PHM)of aero-engines.This paper proposes an RUL prediction method of aero-engines considering the randomness of failure threshold.Firstly,a random-coefficient regression(RCR)model is used to model the degradation process of aeroengines.Then,the RUL distribution based on fixed failure threshold is derived.The prior parameters of the degradation model are calculated by a two-step maximum likelihood estimation(MLE)method and the random coefficient is updated in real time under the Bayesian framework.The failure threshold in this paper is defined by the actual degradation process of aeroengines.After that,a expectation maximization(EM)algorithm is proposed to estimate the underlying failure threshold of aeroengines.In addition,the conditional probability is used to satisfy the limitation of failure threshold.Then,based on above results,an analytical expression of RUL distribution of aero-engines based on the RCR model considering random failure threshold(RFT)is derived in a closed-form.Finally,a case study of turbofan engine is used to demonstrate the effectiveness and superiority of the RUL prediction method and the parameters estimation method of failure threshold proposed.
基金supported by the China Postdoctoral Science Foundation(2017M623415)。
文摘The value range of the failure threshold will generate an uncertain influence on the prediction results for the remaining useful life(RUL) of equipment. Most of the existing studies on the RUL prediction assume that the failure threshold is a fixed value,as they have difficulty in reflecting the random variation of the failure threshold. In connection with the inadequacies of the existing research, an in-depth analysis is carried out to study the effect of the random failure threshold(RFT) on the prediction results for the RUL. First, a nonlinear degradation model with unit-to-unit variability and measurement error is established based on the nonlinear Wiener process. Second, the expectation-maximization(EM) algorithm is used to solve the estimated values of the parameters of the prior degradation model, and the Bayesian method is used to iteratively update the posterior distribution of the random coefficients. Then, the effects of three types of RFT constraint conditions on the prediction results for the RUL are analyzed, and the probability density function(PDF) of the RUL is derived. Finally,the degradation data of aero-turbofan engines are used to verify the correctness and advantages of the method.
