The Experts below are selected from a list of 263376 Experts worldwide ranked by ideXlab platform
Zhou Donghua - One of the best experts on this subject based on the ideXlab platform.
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Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification
IEEE Transactions on Reliability, 2008Co-Authors: Xu Zhengguo, Ji Yindong, Zhou DonghuaAbstract:This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden Degradation Process. The hidden Degradation Process is firstly identified by use of particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the Degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.
Xu Zhengguo - One of the best experts on this subject based on the ideXlab platform.
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Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification
IEEE Transactions on Reliability, 2008Co-Authors: Xu Zhengguo, Ji Yindong, Zhou DonghuaAbstract:This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden Degradation Process. The hidden Degradation Process is firstly identified by use of particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the Degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.
Yao Wang - One of the best experts on this subject based on the ideXlab platform.
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reliability modeling for dependent competing failure Processes with mutually dependent Degradation Process and shock Process
Reliability Engineering & System Safety, 2018Co-Authors: Haiyang Che, Shengkui Zeng, Jianbin Guo, Yao WangAbstract:Abstract Many systems experience dependent competing failure Processes resulting from simultaneous exposure to Degradation Processes and random shocks. Moreover, the Degradation Process and shock Process may be mutually dependent. On the one hand, shocks can cause sudden Degradation increments, which accelerate Degradation Process. On the other hand, the occurrence intensity of shock Process will increase with the accumulation of Degradation. Due to the mutual dependence between Degradation and random shocks, the arrival shocks can cause abrupt Degradation and then facilitate the occurrence of random shocks recursively. Therefore, the intensity is dependent on the number of arrival shocks, and the shock Process cannot be described by the Poisson Process used in previous studies. In this paper, a Facilitation model, which is a special type of Markov point Process, is introduced to model the shock Process. Furthermore, based on the Facilitation model, a novel analytical reliability model with the mutual dependence is developed. A case of a jet pipe servo valve is presented to demonstrate the developed model. The result showed that the reliability declines significantly when considering the mutual dependence.
Yuanchen Fang - One of the best experts on this subject based on the ideXlab platform.
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time series chain graph for modeling reliability covariates in Degradation Process
Reliability Engineering & System Safety, 2020Co-Authors: Nasser Fard, Yuanchen FangAbstract:Abstract In product health management, Degradation modeling methods have been recognized as essential and effective for the lifetime and remaining useful life (RUL) estimations. In many applications, covariate-related data provided by product users can be regarded as fragments of life-cycle records. For a particular fragment, it is possible to suggest several possible Degradation conditions simultaneously. These Degradation conditions may lead to different results of the RUL estimation. One way to solve such a problem is to increase the life-cycle Degradation model's screening capacity of Degradation conditions. In this paper, time series chain graph (TSCG), which could effectively determine the possible Degradation conditions by modeling the dependencies between time-varying risk factors and performance measurements, is proposed. The procedures of model construction based on observed time series and the use of the proposed model for RUL prediction are given. Based on the inherent complexity of the TSCG structure, it is possible to distinguish the Degradation conditions better so that RUL's identification is more reliable. Finally, the validity of the proposed model is illustrated by a turbofan engine Degradation case study, which consists of the time series for engine operation and Degradation Process.
Ji Yindong - One of the best experts on this subject based on the ideXlab platform.
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Real-time Reliability Prediction for a Dynamic System Based on the Hidden Degradation Process Identification
IEEE Transactions on Reliability, 2008Co-Authors: Xu Zhengguo, Ji Yindong, Zhou DonghuaAbstract:This paper introduces a real-time reliability prediction method for a dynamic system which suffers from a hidden Degradation Process. The hidden Degradation Process is firstly identified by use of particle filtering based on measurable outputs of the considered dynamic system. Then the system's reliability is predicted according to the model of the Degradation path. We analyze the identification algorithm mathematically, and validate the effectiveness of this method through computer simulations of a three-vessel water tank. This real-time reliability prediction method is beneficial to the dynamic system's condition monitoring, and may be further helpful to make a proper predictive maintenance policy for the system.