Fuzzy Reliability Assessment of Safety Instrumented Systems Accounting for Common Cause Failure

Yu, Hongping; Zhao, Yue; Li, Mo

doi:10.1109/access.2020.3010878

Cited by 5 publications

(2 citation statements)

References 43 publications

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“…Therefore, one of our future works is to assess the system reliability-box by fusing the multiple sources of information/knowledge under the evidential variable framework. Furthermore, the studied system in our work is binary-state, our future work is to extend the proposed method under the multi-state systems [33], [34], [35]. The curse of dimension of the number of focal elements should be carefully treated in the context of multistate systems.…”

Section: Discussionmentioning

confidence: 99%

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Zhao

2022

IEEE Access

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Advanced engineering systems possess a large number of components with complicated failure dependencies. To accurately assess the system reliability, the degradation models of components should be known in advance and the model parameters should be accurately estimated via a large quantity of historical time-to-failure data. In real-world situations, due to limited data, lack of knowledge, and vague judgments from experts, components' degradation model parameters are, however, inevitably encountered with epistemic uncertainty and oftentimes quantified as evidential variables. In this article, upper and lower bounds of system reliability, termed as reliability-box, are estimated when components' degradation model parameters are elicited from experts and quantified by evidential variables. In the first place, the constrained optimization model is leveraged to assess the reliability-box of each component by giving the evidential variable of the component's degradation model parameters. Next, based on the system structure, the evidential network of the system is constructed to propagate the epistemic uncertainty from the component level to the system level. Therefore, the focal elements of the evidential variable of system reliability, i.e., the system reliability bounds, can be assessed via the belief and plausibility functions to the mass function of the leaf node of the evidential network. The effectiveness of the proposed methods is demonstrated by a rolling system in the chip cutting detection module.

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Section: Discussionmentioning

confidence: 99%

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Zhao

2022

IEEE Access

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“…In general, two types of uncertainties are considered in engineering practice, that is, aleatory uncertainty and epistemic uncertainty 5 . The aleatory uncertainty is mainly caused by inherent randomness and mainly quantified by probabilistic measures, whereas the epistemic uncertainty is raised from lack of knowledge, scarcity of data, ambiguity, subjectivity, and dubious information, 6 and is mainly quantified by Bayesian approaches, 7 interval theory, 5,8 fuzzy theory, 9–11 and the theory of belief functions 12,13 . Traditional reliability assessment methods are conducted based on probabilistic models by leveraging a large quantity of time‐to‐failure data.…”

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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A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

Bahramnejad

Movahhedinia

2021

Computing

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Fuzzy Reliability Assessment of Safety Instrumented Systems Accounting for Common Cause Failure

Cited by 5 publications

References 43 publications

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

Reliability-Box of Systems Under Model Parameter Uncertainty Based on Evidential Variable and Evidential Network

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

A fuzzy arithmetic-based analytical reliability assessment framework (FAARAF): case study, cognitive radio vehicular networks with drivers

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