Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process

Che, Haiyang; Zeng, Shengkui; Guo, Jianbin; Wang, Yao

doi:10.1016/j.ress.2018.07.018

Cited by 86 publications

(56 citation statements)

References 34 publications

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“…However, in this study the shock arrival depends on the number of shocks arriving to the system and the total degradation, so the Poisson process is not suitable for the shock arrival process. Che et al [26] showed that random shock process ( )   ,0 N t t  can be modeled as a facilitation model using intensity function λi(t). As it is proved in [26] the probability of having i shocks by time t can be calculated as the following equation.…”

Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

“…Che et al [26] showed that random shock process ( )   ,0 N t t  can be modeled as a facilitation model using intensity function λi(t). As it is proved in [26] the probability of having i shocks by time t can be calculated as the following equation. [26] the total degradation referred to one component; however, in this paper, the shock process is for the system and () S Xt will be the sum of components degradation.…”

Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

“…As it is proved in [26] the probability of having i shocks by time t can be calculated as the following equation. [26] the total degradation referred to one component; however, in this paper, the shock process is for the system and () S Xt will be the sum of components degradation. γ indicates the effect of the current degradation levels on the intensity and η is the facilitation factor which shows the effect of abrupt degradation on the shock process.…”

Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

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Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Yousefi

Coit

Zhu

2020

Computers & Industrial Engineering

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In this paper, a repairable multi-component system is studied where all the components can be repaired individually within the system. The whole system is inspected at inspection intervals and the failed components are detected and replaced with a new one, while the other components continue functioning. Replacing components individually within the system makes their initial age to be different at each inspection time. Different initial age of all the components have affect on the system reliability and probability of failure and consequently the optimal inspection time, which is for the whole system not individual components. A dynamic maintenance policy is proposed to find the next inspection time based on the initial age of all the components. Two competing failure processes of degradation and a shock process are considered for each component. In our paper, there is a mutual dependency between the degradation process and shock process. Each incoming shock adds additional abrupt damages on the cumulative degradation path of all the components, and the shock arrival process is affected by the system degradation process.A realistic numerical example is presented to illustrate the proposed reliability and maintenance model.

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Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

Section: Reliability Analysis Of Multi-component System Subject To Mumentioning

confidence: 99%

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Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Yousefi

Coit

Zhu

2020

Computers & Industrial Engineering

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“…According to (7), we integrate Pr (FIE j,k \TE j,i ) and Pr (FIE j,k \TE j,p ) evaluated in Step 3 (particularly, (16), (18), (21), (23), and (25) for Pr (FIE j,k \TE j,i ) and (15), (17), (20), and (22), Table 3, for Pr (FIE j,k \TE j,p ), and Pr (SF|FIE j,k \TE j,i ) evaluated in Step 4 (particularly, (26), (27), (28), and (29), and Then according to (9), Q can be computed by integrating (10), (11), (30), (31), and (32). Finally, according to (1), the unreliability of the example WBAN system is evaluated by integrating (8) and (9).…”

Section: Step 5: Integrate For Final System Unreliabilitymentioning

confidence: 99%

“…Note that a rich body of research has been conducted for other types of competing failures. For instance, competitions of different failure causes or modes (eg, degradations, shocks, and catastrophic failures) were modeled for s ‐dependent, s ‐independent, and mutually exclusive cases. In previous studies, competitions of different failure modes were considered for designing maintenance policies.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling of correlated competitions and dependent components with random failure propagation time

Xing

Zhao

Wang

et al. 2020

Quality & Reliability Eng

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Function dependence takes place in systems where the malfunction of certain trigger component(s) causes other system components (referred to as dependent components) to become unusable or inaccessible. Systems undergoing function dependence often exhibit diverse statuses due to competitions in the time domain between propagated failure from a dependent component and local failure of the corresponding trigger component. If the former wins (ie, occurring first), a failure propagation effect is induced, crashing the entire system. If the latter wins, a failure isolation effect may be induced quarantining the damage from the propagated failure (the isolation effect can occur in a deterministic or probabilistic manner depending on applications). Existing models addressing such competitions have restrictive assumptions such as uncorrelated competitions from multiple function dependence groups, zero or negligible failure propagation time, and deterministic failure isolation effect. This paper advances the state of the art by proposing a combinatorial reliability model for systems undergoing correlated, probabilistic competitions and random failure propagation time for dependent components. A case study of a wireless body area network system for patient monitoring is performed to illustrate the proposed methodology and effects of different model parameters on the system reliability.

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Reliability analysis for multi‐state systems subject to distinct random shocks

Cao

Dong

2021

Quality & Reliability Eng

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This paper analyzes the competing and dependent failure processes for multi‐state systems suffering from four typical random shocks. Reliability analysis for discrete degradation is conducted by explicitly modeling the state transition characteristics. Semi‐Markov model is employed to explore how the system vulnerability and potential transition gap affect the state residence time. The failure dependence is specified as that random shocks can not only lead to different abrupt failures but also cause sudden changes on the state transition probabilities, making it easier for the system to stay at the degraded states. Reliability functions for all the exposed failure processes are presented based on the corresponding mechanisms. Interactions between different failure processes are also taken into account to evaluate the actual reliability levels in the context of degradation and distinct random shocks. An illustrative example of a multi‐state air conditioning system is studied to demonstrate how the proposed method can be applied to the engineering practice.

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Reliability modeling for dependent competing failure processes with mutually dependent degradation process and shock process

Cited by 86 publications

References 34 publications

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Dynamic maintenance policy for systems with repairable components subject to mutually dependent competing failure processes

Reliability modeling of correlated competitions and dependent components with random failure propagation time

Reliability analysis for multi‐state systems subject to distinct random shocks

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