Reliability evaluation of demand-based warm standby systems with capacity storage

Jia, Heping; Peng, Rui; Yang, Li; Wu, Tianyi; Liu, Dunnan; Li, Yanbin

doi:10.1016/j.ress.2021.108132

Cited by 57 publications

(15 citation statements)

References 30 publications

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“…Assessing system reliability is one of the critical activities in the lifecycle management of systems as its results help decision‐makers to grasp the health conditions of the system so as to facilitate the subsequent maintenance activities 1,2 . The key problems of assessing the system reliability are twofold 3,4 . The first is how to cope with various uncertainties affecting all the system's components.…”

Section: Introductionmentioning

confidence: 99%

Evidential network‐based system reliability assessment by fusing multi‐source evidential information

2023

Quality & Reliability Eng

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Expert knowledge is an important information source for reliability assessment of those systems with limited time‐to‐failure data. For a better understanding of the degradation profiles of systems, multiple experts are oftentimes invited to express their judgments and the associated uncertainties on the reliability‐related measures of the system. Evidential variables, as an alternative of uncertainty quantification, have been extensively used in expert systems to quantify the epistemic uncertainty of the elicited expertise. When eliciting the reliability‐related information by evidential variables, experts only need to express the possible ranges of reliability‐related measures and their associated probabilities. Such a type of information well caters to the experts’ elicitation process. In this article, an evidential network (EN)‐based reliability assessment method is put forth by fusing multi‐source evidential information. The proposed method mainly contains three steps. In the first place, the multi‐source evidential information related to all components is elicited from experts in the form of evidential variables. Next, the evidential variable of the component reliability is assessed via a constrained optimization model by treating all pieces of multi‐source evidential information as constraints. The component reliability results are transformed into pieces of mass functions of the components’ states under the theory of belief functions. The system reliability‐box and the reliability‐box over time are, therefore, calculated by the EN model by inputting all mass functions of components’ states. A pipeline system and a chip cutting system are exemplified to examine the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Evidential network‐based system reliability assessment by fusing multi‐source evidential information

2023

Quality & Reliability Eng

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“…Failures of safety‐critical systems during mission execution will result in disastrous consequences (Bai et al., 2021; Brito et al., 2010; Levitin et al., 2018; Levitin, Finkelstein et al 2019; Wang, 2021; Xiao, Yi, et al., 2020). To reduce the huge costs associated with unexpected system failures, extensive research has been devoted to determining optimal risk control strategies, with the aim of improving system reliability during mission execution (Jia et al., 2022; Levitin et al., 2014; Peng et al., 2016; Xiao et al., 2021; Zhang et al., 2016). Performing routine preventive maintenance is one of the most commonly used approaches in engineering practice to mitigate the risk of system failure.…”

Section: Introductionmentioning

confidence: 99%

Mission risk control via joint optimization of sampling and abort decisions

2023

Self Cite

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Information‐driven mission abort is an effective way to control the failure risk of safety‐critical systems during mission executions. We investigate the optimal sampling and mission abort decisions of partially observable safety‐critical systems, where the underlying system health state can only be revealed by sampling. In contrast to previous studies, we employ partial health information to jointly determine: (a) whether to execute sampling, and (b) when to abort the mission in a dynamic manner, so as to minimize the expected total cost incurred by sampling, mission failure, and system malfunction. Dynamic sampling and mission abort policies are devised following the belief state, whose optimization model is cast into the framework of a partially observable Markov decision process. Some structural insights with regard to the value function, control limit selection, and optimality existence are presented. The performance of the proposed sampling and abort policy is tested by numerical experiments, which are proved to outperform other heuristic abort policies in mission loss control.

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“…Some researchers (cf. [30][31][32] ) explored the performance indices of machine repairable system with warm standby provisioning and a failure-prone repair facility. For more comprehensive theories and excellent reviews on the imperfect repair facility, the researcher may refer to the following investigations (cf.…”

Section: Introductionmentioning

confidence: 99%

“…The repairer may be imperfect any number of times before it gives a perfect repair. Some researchers (cf 30–32 …”

Section: Introductionmentioning

confidence: 99%

Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays

Shekhar

Devanda

Kaswan

2023

Quality & Reliability Eng

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This prospective study focuses on analyzing the reliability characteristics of multi‐unit systems with standby provisioning, accounting for failures, degradation, random delays, and probabilistic imperfections. The investigation sheds light on how these unreliable attributes hinder the performance and availability of machining systems. Given the frequent occurrence of these negative attributes within machining systems, their impact on production flow, performance, and resource utilization is significant. Moreover, these unfavorable attributes hinder the adoption of advanced technologies that rely on the continuous availability of machining systems. Thorough research on operational traits serves as a foundation for developing solutions to enhance machining system efficiency and elucidates the underlying causes of machining system failures throughout their performance lifecycle. The reliability analysis employs a state‐of‐the‐art queue‐theoretic approach. In order to model the stochastic behavior of the investigated machine repair problem in a systematic manner, we consider various statistically independent failure modes, including active/standby unit failure, degraded failure, switching failure, and common‐cause failure. The unreliable characteristics of machining systems are further compounded by factors such as imperfect fault coverage, reboot delay, and imperfect repair, necessitating a comprehensive examination to strategically implement preventive, corrective, and predictive measures. The seamless operation of multi‐unit machining systems is essential for the successful integration of advanced technologies such as cloud computing, industry 4.0, and IoT. Failures, delays, degradation, and imperfections within machining systems have detrimental effects on their efficiency and availability, demanding in‐depth investigation. To facilitate numerical experimentation and sensitivity analysis of the reliability aspects of the proposed machining system, we develop performance indices such as system reliability, mean‐time‐to‐failure, and failure frequency. These metrics provide valuable insights for decision‐makers seeking to implement measures that ensure uninterrupted availability of the machining system.

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Reliability evaluation of demand-based warm standby systems with capacity storage

Cited by 57 publications

References 30 publications

Evidential network‐based system reliability assessment by fusing multi‐source evidential information

Evidential network‐based system reliability assessment by fusing multi‐source evidential information

Mission risk control via joint optimization of sampling and abort decisions

Reliability analysis of standby provision multi‐unit machining systems with varied failures, degradations, imperfections, and delays

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