In the Prognostics and Health Management(PHM),remaining useful life(RUL)is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems.Recently,unscented Kalman filt...In the Prognostics and Health Management(PHM),remaining useful life(RUL)is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems.Recently,unscented Kalman filtering(UKF)has been applied widely in the RUL estimation.For a degradation system,the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF.However,in some special degradation systems,their monitored measurements have a linear relation with their degradation states.For these special problems,it may bring estimation errors to use the UKF method directly.Besides,many uncertain factors can result in the fluctuations of the estimated results,which may have a bad influence on the RUL estimation method.As a result,a robust RUL estimation approach is proposed in this paper to reduce the errors and randomness of estimation results for this kind of degradation problems.Firstly,an improved unscented Kalman filtering is established utilizing the Kalman filtering(KF)method and a linear adaptive strategy.The linear adaptive strategy is used to adjust its noise term adaptively.Then,the robust RUL estimation is realized by the improved UKF.At last,three problems are investigated to demonstrate the effectiveness of the proposed method.展开更多
The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). ...The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.展开更多
基金supported by the National Key R&D Program of China(Grant No.2018YFB1701400)the National Science Fund for Distinguished Young Scholars(Grant No.51725502)the Foundation for Innovative Research Groups of the National Natural Science Foundation of China(Grant No.51621004).
文摘In the Prognostics and Health Management(PHM),remaining useful life(RUL)is very important and utilized to ensure the reliability and safety of the operation of complex mechanical systems.Recently,unscented Kalman filtering(UKF)has been applied widely in the RUL estimation.For a degradation system,the relationship between its monitored measurements and its degradation states is assumed to be nonlinear in the conventional UKF.However,in some special degradation systems,their monitored measurements have a linear relation with their degradation states.For these special problems,it may bring estimation errors to use the UKF method directly.Besides,many uncertain factors can result in the fluctuations of the estimated results,which may have a bad influence on the RUL estimation method.As a result,a robust RUL estimation approach is proposed in this paper to reduce the errors and randomness of estimation results for this kind of degradation problems.Firstly,an improved unscented Kalman filtering is established utilizing the Kalman filtering(KF)method and a linear adaptive strategy.The linear adaptive strategy is used to adjust its noise term adaptively.Then,the robust RUL estimation is realized by the improved UKF.At last,three problems are investigated to demonstrate the effectiveness of the proposed method.
基金Supported by Fundamental Research Funds for the Central Universities of China(Grant No.DUT17GF214)
文摘The remaining useful life(RUL) prediction of mechanical products has been widely studied for online system performance reliability, device remanufacturing, and product safety(safety awareness and safety improvement). These studies incorporated many di erent models, algorithms, and techniques for modeling and assessment. In this paper, methods of RUL assessment are summarized and expounded upon using two major methods: physics model based and data driven based methods. The advantages and disadvantages of each of these methods are deliberated and compared as well. Due to the intricacy of failure mechanism in system, and di culty in physics degradation observation, RUL assessment based on observations of performance variables turns into a science in evaluating the degradation. A modeling method from control systems, the state space model(SSM), as a first order hidden Markov, is presented. In the context of non-linear and non-Gaussian systems, the SSM methodology is capable of performing remaining life assessment by using Bayesian estimation(sequential Monte Carlo). Being e ective for non-linear and non-Gaussian dynamics, the methodology can perform the assessment recursively online for applications in CBM(condition based maintenance), PHM(prognostics and health management), remanufacturing, and system performance reliability. Finally, the discussion raises concerns regarding online sensing data for SSM modeling and assessment of RUL.
