Fuzzy Reliability Assessment of Systems With Multiple-Dependent Competing Degradation Processes

Lin, Yan‐Hui; Zio, Enrico

doi:10.1109/tfuzz.2014.2362145

Cited by 37 publications

(39 citation statements)

References 40 publications

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“…Traditional reliability assessment methods regard the degradation process of system reliability or SOH as determined and seek to construct the underlying degradation model from a large number of historical data of similar equipments, without taking account the dynamics of operating conditions or specificity for a individual equipment [3][4][5][6][7][8]. Actually as noted by Bian, by now the majority of reliability prediction models are based on the assumption that the prevailing operating conditions are regarded as temporarily constant or irrelevant to the evolution process [9].…”

Section: Introductionmentioning

confidence: 99%

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao

Zio

Zhao

2018

Reliability Engineering & System Safety

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Modeling the evolution of system reliability in the presence of Condition Monitoring (CM) signals is an important issue for improved reliability assessment and system lifetime prediction. In practice, during its lifetime, a system usually works under varying operating conditions due to internal or external factors such as the ambient environments, operational profiles or workloads. In this context, the system reliability can show varying evolution behaviors (follow changing underlying trajectories), which presents new challenges to describe precisely the dynamics of system reliability. Thus, this paper proposes a novel data-driven approach to address the problems including the identification of varying operating conditions, the construction and dynamical updating of evolution model, and finally the online prediction of system reliability, focusing on systems under one common and typical case of varying operating conditions, the multi-state operating condition. Experiments based on artificial data and some widely studied real reliability cases reveal that the proposed method has superior performance compared with some existing benchmark approaches, in the case under consideration. This improved reliability prediction provides fundamental basis for advanced prognostics such as the Remaining Useful Life (RUL) estimation.

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Section: Introductionmentioning

confidence: 99%

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Tao

Zio

Zhao

2018

Reliability Engineering & System Safety

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“…Song et al established the reliability models considering four failure conditions under the independent failure modes for a multicomponent series system, the effectiveness of which has been proved by numerical analysis. Lin et al investigated the fuzzy reliability evaluation methods for several dependent degradation failure modes, and the dependence of the deterioration process was described by a piecewise‐deterministic Markov model. The catastrophic failure mode was not considered in their model.…”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15] The approaches using condition monitoring information for system degradation modeling with competing failure modes usually include fuzzy physics-based model, 16 random shock models, 10,12,17 Wiener process, 18 and Markov models. 13,19,20 Among these models, Markov models are applied most extensively in the areas of reliability analysis and optimal maintenance decision-making. Besides, in many practical applications of CBM, the healthy or unhealthy states of the system cannot be observed directly.…”

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confidence: 99%

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

Makiš

Zhao

et al. 2018

Quality & Reliability Eng

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Most maintenance optimization models of gear systems have considered single failure mode. There have been very few papers dealing with multiple failure modes, considering mostly independent failure modes. In this paper, we present an optimal Bayesian control scheme for early fault detection of the gear system with dependent competing risks. The system failures include degradation failure and catastrophic failure. A three‐state continuous‐time–homogeneous hidden Markov model (HMM), namely the model with unobservable healthy and unhealthy states, and an observable failure state, describes the deterioration process of the gear system. The condition monitoring information as well as the age of the system are considered in the proposed optimal Bayesian maintenance policy. The objective is to maximize the long‐run expected average system availability per unit time. The maintenance optimization model is formulated and solved in a semi‐Markov decision process (SMDP) framework. The posterior probability that the system is in the warning state is used for the residual life estimation and Bayesian control chart development. The prediction results show that the mean residual lives obtained in this paper are much closer to the actual values than previously published results. A comparison with the Bayesian control chart based on the previously published HMM and the age‐based replacement policy is given to illustrate the superiority of the proposed approach. The results demonstrate that the Bayesian control scheme with two dependent failure modes can detect the gear fault earlier and improve the availability of the system.

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“…Traditional reliability assessment computes the reliability of an equipment based on failure data from a (large) number of similar equipment [1,10,17,19,9]. This provides the reliability for the somewhat average equipment, without taking into account the specificity of the physical process of degradation and the related monitored data of the individual equipment under assessment.…”

Section: Introductionmentioning

confidence: 99%

“…In Lin et al [10], the simulation of a Piecewise Deterministic Markov Process (PDMP) model is performed for the reliability assessment of multiple dependent components with multi-state and continuous degradation processes, where the system degradation state is supposed to be precisely known and no uncertainty is considered. PDMP gives a same average result for different systems, without considering information on the specific system.…”

Section: Introductionmentioning

confidence: 99%

System dynamic reliability assessment and failure prognostics

Liu

Zio

2017

Reliability Engineering & System Safety

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Traditionally, equipment reliability assessment is based on failure data from a population of similar equipment, somewhat giving an average description of the reliability performance of an equipment, not capturing the specificity of the individual equipment. Monitored degradation data of the equipment can be used to specify its behavior, rendering dynamic the reliability assessment and the failure prognostics of the equipment, as shown in some recent literature. In this paper, dynamic reliability assessment and failure prognostics with noisy monitored data are developed for a system composed of dependent components. Parallel Monte Carlo simulation and recursive Bayesian method are integrated in the proposed modelling framework to assess the reliability and to predict the Remaining Useful Life (RUL) of the system. The main contribution of the paper is to propose a framework to estimate the degradation state of a system composed of dependent degradation components whose conditions are monitored (even without knowing the initial system degradation state) and to dynamically assess the system risk and RUL. As case study, a subsystem of the residual heat removal system of a nuclear power plant is considered. The results shows the significance of the proposed method for tailored reliability assessment and failure prognostics.

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Fuzzy Reliability Assessment of Systems With Multiple-Dependent Competing Degradation Processes

Cited by 37 publications

References 40 publications

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

A novel support vector regression method for online reliability prediction under multi-state varying operating conditions

Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes

System dynamic reliability assessment and failure prognostics

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