An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis

Yevkin, Olexandr

doi:10.1002/qre.1861

Cited by 56 publications

(37 citation statements)

References 14 publications

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“…However, in the real case, each expert has a different weight according to his or her own background such as in job tenure, education, experience, and of course paradigm shift. Consequently, as discussed in Shoar et al, Yevkin, and Yazdi et al, employing a heterogeneous group of expert has enough superiority for precision compared with homogenous one. Thus, in this study, standard method is engaged for weighting experts group selected in a heterogeneous manner …”

Section: Materials and Methodologymentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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Failure modes and effects analysis (FMEA) is a safety and reliability technique that is widely used to evaluate, design, and process a system against diverse possible ways through which the potential failure has a tendency to occur. In conventional FMEA, the risk evaluation is determined by risk priority number (RPN) obtained by multiplying of three risk factors—severity, occurrence, and detection. However, because of many shortages in conventional FMEA, the RPN scores have been widely criticized along issues bothering on ambiguity and vagueness, scoring, appraising, evaluating, and selecting corrective actions. In this paper, we propose a new integrated fuzzy smart FMEA framework where the combination of fuzzy set theory, analytical hierarchy process (AHP), and data envelopment analysis (DEA) is used, respectively, to handle uncertainty and to increase the reliability of the risk assessment. These are achieved by employing a heterogeneous group of experts and determining the efficiency of FMEA mode with adequate priority and corrective actions using RPN, time, and cost as indicators. A numerical example (aircraft landing system) is provided to exemplify the feasibility and effectiveness of the proposed model. The outputs of the proposed model compared with the conventional risk assessment technique results show its effectiveness, reliability, and propensity for real applications.

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Section: Materials and Methodologymentioning

confidence: 99%

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Daneshvar

Yazdi

Adesina³

2020

Quality & Reliability Eng

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“…Aiming at these challenges mentioned above, many efficient diagnostic methods have been proposed. In order to model the dynamic failure characteristics, DFT [6], Markov model [28] and dynamic Bayesian networks (DBN) [9,26] have been proposed to capture the above mentioned dynamic failure behaviors. DFT is widely used to model the dynamic systems as the extensions of the traditional static fault trees with sequence-and function-dependent failure behaviors.…”

Section: Introductionmentioning

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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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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“…Dynamic fault trees [17], Markov chain models for the lifecycle analysis [18], and Weibull failure analysis [19] may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available during a specific operation [20].…”

Section: Introductionmentioning

confidence: 99%

“…ISO 31000 on risk management [7] and NORSOK z-013 on risk and emergency preparedness analysis [15]) suggest updates of risk analysis in conjunction with major changes in the plant or organization or every five years. Falck et al [16] share such concern and affirm that risk assessment performed for the design phase of a plant is not suitable for the following phases.Dynamic fault trees [17], Markov chain models for the lifecycle analysis [18], and Weibull failure analysis [19] may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available during a specific operation [20].…”

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confidence: 99%

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

Paltrinieri

Landucci

Rossi

2017

Volume 3B: Structures, Safety and Reliability

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Recent major accidents in the offshore oil and gas (O&G) industry have showed inadequate assessment of system risk and demonstrated the need to improve risk analysis. While direct causes often differ, the failure to update risk evaluation on the basis of system changes/modifications has been a recurring problem. Risk is traditionally defined as a measure of the accident likelihood and the magnitude of loss, usually assessed as damage to people, to the environment, and/or economic loss. Recent revisions of such definition include also aspects of uncertainty. However, Quantitative Risk Assessment (QRA) in the offshore O&G industry is based on consolidated procedures and methods, where periodic evaluation and update of risk is not commonly carried out. Several methodologies were recently developed for dynamic risk analysis of the offshore O&G industry. Dynamic fault trees, Markov chain models for the lifecycle analysis, and Weibull failure analysis may be used for dynamic frequency evaluation and risk assessment update. Moreover, dynamic risk assessment methods were developed in order to evaluate the risk by updating initial failure probabilities of events (causes) and safety barriers as new information are made available. However, the mentioned techniques are not widely applied in the common O&G offshore practice due to several reasons, among which their complexity has a primary role. More intuitive approaches focusing on a selected number of critical factors have also been suggested, such as the Risk Barometer or the TEC2O. Such techniques are based on the evaluation of technical, operational and organizational factors. The methodology allows supporting periodic update of QRA by collecting and aggregating a set of indicators. However, their effectiveness relies on continuous monitoring activity and realtime data capturing. For this reason, this contribution focuses on the coupling of such methods with sensors of different nature located in or around and offshore O&G system. The inheritance from the Centre for Integrated Operations in the Petroleum Industries represents the basis of such study. Such approach may be beneficial for several cases in which (quasi) real-time risk evaluation may support critical operations. Two representative cases have been described: i) erosion and corrosion issues due to sand production; and ii) oil production in environmental sensitive areas. In both the cases, dynamic risk analysis may employ real-time data provided by sand, corrosion and leak detectors. A simulation of dynamic risk analysis has demonstrated how the variation of such data can affect the overall risk picture. In fact, this risk assessment approach has not only the capability to continuously iterate and outline improved system risk pictures, but it can also compare its results with sensor-measured data and allow for calibration. This can potentially guarantee progressive improvement of the method reliability for appropriate support to safety-critical decisions. Direct causes of such events often differed, but...

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An Efficient Approximate Markov Chain Method in Dynamic Fault Tree Analysis

Cited by 56 publications

References 14 publications

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

Fuzzy smart failure modes and effects analysis to improve safety performance of system: Case study of an aircraft landing system

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

Real-Time Data for Risk Assessment in the Offshore Oil and Gas Industry

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