Measuring Conflicts of Multisource Imprecise Information in Multistate System Reliability Assessment

Xiahou, Tangfan; Zeng, Zhiguo; Liu, Yu; Huang, Hong‐Zhong

doi:10.1109/tr.2021.3087531

Cited by 11 publications

(11 citation statements)

References 35 publications

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“…To evaluate the reliability of a hierarchical system, the methods that can fuse the information from multiple levels and properly handle the uncertainty propagation are, therefore, always preferred. 28,29 The evidence theory is a widely applied uncertainty qualification method. 30 Compared with the p-box theory, the evidence theory is more widely used as the p-box model is a special case of the evidence theory which is only suitable for continuous variables.…”

Section: Introductionmentioning

confidence: 99%

“…Oftentimes, the estimation of the system reliability is an aggregation of information from various levels. To evaluate the reliability of a hierarchical system, the methods that can fuse the information from multiple levels and properly handle the uncertainty propagation are, therefore, always preferred 28,29 . The evidence theory is a widely applied uncertainty qualification method 30 .…”

Section: Introductionmentioning

confidence: 99%

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An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

Huang

Shao

Xiahou

et al. 2023

Quality & Reliability Eng

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Modern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time-to-failure data. For a hierarchical system, different levels' reliability-related data from monitoring or observations can provide additional information to update the system's reliability. However, with the limitation of sensing and monitoring technologies and vague judgments from experts, the reliability data inevitably contain epistemic uncertainty. Properly fusing the imprecise data can still improve the accuracy of system reliability assessment. In this article, we propose a reliability assessment approach based on the evidential network (EN) model to update the hierarchical systems' reliability by aggregating the system-level imprecise knowledge. First, the evidence theory is applied to quantify the epistemic uncertainty associated with the imprecise system-level reliability data. Then, an EN model is created to realize the uncertainty propagation and fusion of imprecise data. Finally, at any particular time instant, the system reliability can be updated with the aid of imprecise data based on the proposed EN model. The effectiveness of the proposed method is validated by a real engineering case of a wind turbine system. Overall, the results shown from the case study support that the proposed method can update the reliability of hierarchical systems by using system-level imprecise data.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

Huang

Shao

Xiahou

et al. 2023

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

show abstract

“…In engineering practice, the collected data/knowledge, however, come from multiple sources. The multi‐source information is collected due to two reasons 25 . First, the reliability‐related information is usually collected from multiple experts with each owning its knowledge background.…”

Section: Introductionmentioning

confidence: 99%

“…The multi-source information is collected due to two reasons. 25 First, the reliability-related information is usually collected from multiple experts with each owning its knowledge background. Second, the reliability-related measures are heterogeneous, such as the MTTF of components, the component state probability, and the components' degradation model parameters.…”

mentioning

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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“…The epistemic uncertainty of degradation model parameters arises when the time-tofailure data of the studied systems/components is limited and the estimated degradation model parameters are uncertain. Sometimes, especially for newly developed systems with no time-to-failure data, experts are invited to express their judgements on the degradation model parameters, while the elicited information/knowledge may contain epistemic uncertainty [8], [9], [10].…”

Section: Introductionmentioning

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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Measuring Conflicts of Multisource Imprecise Information in Multistate System Reliability Assessment

Cited by 11 publications

References 35 publications

An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

An evidential network approach to reliability assessment by aggregating system‐level imprecise knowledge

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

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

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