Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

Sallak, Mohamed; Schön, Walter; Aguirre, Felipe

doi:10.1109/tr.2013.2240888

Cited by 38 publications

(15 citation statements)

References 64 publications

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“…Each node represents a variable, and arcs indicate direct conditional relations between the connected nodes. DEN, an extension of evidential network, takes into account the time by defining different nodes to model variables with respect to different time slices [21]. It includes the initial network and the temporal transition network.…”

Section: Denmentioning

confidence: 99%

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Fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers

Duan¹,

Longfei²,

Lin³

2017

EiN

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Fault diagnosis For complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers diagnostyka uszkodzeń systemu złożonego oparta na dynamicznych sieciach dowodowych oraz wieloatrybutowej metodzie podejmowania decyzji z wykorzystaniem liczb interwałowych The complexity of modern system structures and failure mechanisms makes it very difficult to locate the system fault. It has characteristics of dynamics of failure, diversity of distribution and epistemic uncertainties, which increase the challenges in the fault diagnosis significantly. This paper presents a fault diagnosis framework for complex systems within which the failure rates of components are expressed in interval numbers. Specifically, it uses a dynamic fault tree (DFT) to model the dynamic fault behaviors and deals with the epistemic uncertainties using Dempster-Shafer (D-S) theory and interval numbers. Furthermore, a solution is proposed to map a DFT into a dynamic evidential network (DEN) to calculate the reliability parameters. Additionally, diagnostic importance factor (DIF), Birnbaum importance measure (BIM) and heuristic information values (HIV) are taken into account comprehensively in order to obtain the best fault search scheme using an improved VIKOR algorithm. Finally, an illustrative example is given to demonstrate the efficiency of this method. Keywords: diagnosis strategy, D-S theory, interval numbers, dynamic evidential network, VIKOR.Złożoność However, these methods assume that all components obey to the same distribution and cannot handle the challenge (2). Furthermore, these methods, which are usually assumed that the failure rates of the components are considered as crisp values describing their reliability characteristics, have been found to be inadequate to deal with the challenge (3) mentioned above. Therefore, fuzzy sets theory has been introduced as a useful tool to handle the challenge (3). The fuzzy fault tree analysis model employs fuzzy sets and possibility theory, and deals with ambiguous, qualitatively incomplete and inaccurate information [8,16,18]. To deal with the challenge (1) and (3), fuzzy DFT analysis has been introduced [13][14] which employs a DFT to construct the fault model and calculates the reliability results based on the continuous-time BN under fuzzy numbers. However, these approaches cannot handle the challenge (2). For this purpose, Mi et al. proposed a new reliability assessment approach which used a DFT to model the dynamic characteristics within complex systems and estimated the parameters of different life distributions using the coefficient of variation (COV) method [19]. To a certain extent, this method can meet the above challenges. But it is confined to the reliability analysis and cannot be used for the fault diagnosis. Dugan introduced a diagnostic importance factor (DIF) to determine the diagnosis sequence using DFT analysis [1]. However, the solution for DFT is based on Markov Chain which has an apparent state space explosion problem. In the work...

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Section: Denmentioning

confidence: 99%

“…when component i fails it causes the system to move from a working to a failed state. BIM of a component i can be interpreted as the rate at which the system's reliability improves as the reliability of component i is improved [21]. Analytically, Birnbaum's importance interval measure of a component i can be defined using D-S theory by the following equation:…”

mentioning

confidence: 99%

Fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers

Duan¹,

Longfei²,

Lin³

2017

EiN

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“…On the other hand, the DSTE has been challenged on particular aspects by some experts in the area of safety and reliability analyses (e.g., [42], [43]), as it seems to suffer from major drawbacks such as the computational efforts and the difficulties that may be encountered when eliciting the probability masses from a number of experts [44], [45].…”

Section: Consider a Modelmentioning

confidence: 99%

Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems

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Zio

2015

IEEE Trans. Rel.

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The aim of this paper is to address the maintenance optimization problem when the maintenance models encode stochastic processes, which rely on parameters that are imprecisely known, and when these parameters are only determined through information elicited from experts. A genetic algorithms (GA)-based technique is proposed to deal with such uncertainty setting; this approach requires addressing three main issues: i) the representation of the uncertainty in the parameters and its propagation onto the fitness values; ii) the development of a ranking method to sort the obtained uncertain fitness values, in case of single-objective optimization; and iii) the definition of Pareto dominance, for multi-objective optimization problems. A known hybrid Monte Carlo-Dempster-Shafer Theory of Evidence method is used to address the first issue, whereas two novel approaches are developed for the second and third issues. For verification, a practical case study is considered concerning the optimization of maintenance for the nozzle system of a turbine in the Oil & Gas industry.

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“…The importance measure (Im) is used in the maintainability and reliability context for finding the most crucial component to be fixed in order to achieve the highest increase in the reliability of the system [33,67]. In the context of this paper, the importance measure shows how urgent this indicator is in the public and network's eye.…”

Section: Social Performancementioning

confidence: 99%

A Step towards Developing a Sustainability Performance Measure within Industrial Networks

Shokravi

Kurnia

2014

Sustainability

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Despite the plethora of literature in sustainability and supply chain management in the recent years, a quantitative tool that measures the sustainability performance of an industrial supply network, considering the uncertainties of existing data, is hard to find. This conceptual paper is aimed at establishing a quantitative measure for the sustainability performance of industrial supply networks that considers aleatory and epistemic uncertainties in its environmental performance evaluation. The measure is built upon economic, environmental and social performance evaluation models. These models address a number of shortcomings in the literature, such as incomplete and inaccurate calculation of environmental impacts, as well as the disregard for aleatory and epistemic uncertainties in the input data and, more importantly, the scarce number of quantitative social sustainability measures. Dyadic interactions are chosen for the network, while the network members have a revenue-sharing relationship. This relationship promotes sharing of the required information for the use of the proposed model. This measure provides an approach to quantify the environmental, social and economic sustainability performances of a supply network. Moreover, as this measure is not specifically designed for an industrial sector, it can be employed over an evolving and diverse industrial network.

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Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties

Cited by 38 publications

References 64 publications

Fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers

Fault diagnosis for complex systems based on dynamic evidential network and multi-attribute decision making with interval numbers

Genetic Algorithms in the Framework of Dempster-Shafer Theory of Evidence for Maintenance Optimization Problems

A Step towards Developing a Sustainability Performance Measure within Industrial Networks

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