An Alternative FMEA Method for Simple and Accurate Ranking of Failure Modes

Bradley, James R.; Guerrero, Hector

doi:10.1111/j.1540-5915.2011.00329.x

Cited by 34 publications

(39 citation statements)

References 19 publications

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“…It is a straight forward step-by-step technique almost universally accepted as a method for systematically determining the ways in which failure can occur and also the effects (from minor to catastrophic) that each such failure can have on overall functionality The objective of FMECA is to anticipate failures and prevent them from occurring A good FMECA will: identify known and potential failure modes, identify cause and effect of each failure mode and provide for problem follow-up and corrective action, and prioritises the identified failure modes according to the RPN Bradley and Guerrero (2011) have acknowledged the wide use of FMECA in both product design and industry. However, they have identified two prominent criticisms of its traditional application viz.…”

Section: Failure Modes Effects and Criticality Analysismentioning

confidence: 99%

A hybrid model for learning from failures: The Hurricane Katrina disaster

Labib

2015

Expert Systems with Applications

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a b s t r a c tThere is a need to facilitate learning from failures in the context of natural and man-made disasters. This paper investigates the multi-faceted nature of research in disasters and the aspect of hybrid approaches in modelling within this domain. The paper applies a framework of reliability and multiple criteria decision analysis techniques to the case of the Hurricane Katrina disaster of 2005. It is shown how this hybrid model can be used through an integrative approach to perform a systematic analysis that can lead to learning from failures.The proposed framework incorporates and integrates Fault Tree Analysis (FTA), Reliability Block Diagram (RBD) analysis and the Risk Priority Number (RPN) concept, together with the Analytic Hierarchy Process (AHP) which is used as a simulation model for decision support. It is shown how the proposed integrated framework can contribute to our understanding of failures and enhances the ability to extract lessons from failures or disasters. Such lessons are then mapped into specific decisions for prevention, and resource allocations, to help avoid a repeat disaster.

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Section: Failure Modes Effects and Criticality Analysismentioning

confidence: 99%

A hybrid model for learning from failures: The Hurricane Katrina disaster

Labib

2015

Expert Systems with Applications

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“…Wang et al [55] reported an FMEA method by using the house of reliability-based rough VIKOR approach. Other studies regarding prioritization of FMs can be found in [5], [12], [13], [18], [23], and [31].…”

Section: Prioritization Of Fms In Fmeamentioning

confidence: 99%

“…R ELIABILITY engineering addresses the estimation, prevention, and management of high levels of "lifetime" engineering uncertainty and risks of failure, which has received wide attention in various areas [16], [65]. Failure mode and effects analysis (FMEA) is a highly structured, and systematic reliability-management instrument, which is very useful for evaluating and eliminating potential failure modes (FMs) of products, processes, systems, and services [5], [39], [52].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Bowles and Peláez [4] initially presented a fuzzy logic-based FMEA method for dealing with some of the drawbacks in the traditional methods based on strictly numerical evaluations. Bradley and Guerrero [5] developed a method to rank FMs using a data-elicitation technique. Liu et al [39] proposed an integrated FMEA approach for accurate risk assessment in an uncertain setting.…”

Section: Introductionmentioning

confidence: 99%

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Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach

et al. 2019

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Failure mode and effect analysis (FMEA) is an effective risk-management tool, which has been extensively utilized to manage failure modes (FMs) of products, processes, systems, and services. Almost all FMEA models are concerned with how to get a complete risk order of FMs from highest to lowest risk. However, in many situations, it may be sufficient to classify the FMs into several ordinal risk classes. Meanwhile, generating a consensual decision is crucial for the FMEA problem because 1) reaching consensus will enhance the connections among FMEA participants, and 2) a highly accepted group solution to the FMEA problem can be generated. Thus, this study proposes a consensus-based group decision-making framework for FMEA with the aim of classifying FMs into several ordinal risk classes in which we assumed that FMEA participants provide their preferences in a linguistic way using possibilistic hesitant fuzzy linguistic information. In the FMEA framework, a consensus-driven methodology is presented to generate the weights of risk factors. Following this, an optimizationbased consensus rule guided by a minimum adjustment distance policy is devised, and an interactive model for reaching consensus is developed to generate consensual FM risk classes. In order to justify its validity of the proposal, our framework is applied for the risk evaluation of proton beam radiotherapy.Index Terms-Consensus, failure mode and effect analysis (FMEA), failure mode classification, multiattribute group decisionmaking, reliability management. NOMENCLATURE DDetection. FM Failure mode.

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“…This situation exposes projects to a number of diverse and interdependent risks, which implies that identifying and analyzing their causes and effects is an important aspect. For instance, Failure Modes and Effects Analysis (FMEA) consists in a qualitative analysis of dysfunction modes and their effects [14]. Initially developed for product-related risks, it has been expanded to process-related and project-related risks, where the focus changes, but the principle is the same, consisting in identifying direct causes and effects of a potential failure.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Risk Response Planning Under Resource Constraints in Large Engineering Projects

Fang

Marle²,

Xie³

et al. 2013

IEEE Trans. Eng. Manage.

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Engineering project managers often face a challenge to allocate tight resources for managing interdependent risks. In this paper a quantitative framework of analysis for supporting decision-making in project risk response planning is developed and studied. The design structure matrix representation is used to capture risk interactions and build a risk propagation model for predicting the global mitigation effects of risk response actions. For exemplification, a genetic algorithm is used as tool for choosing response actions and allocating budget reserves. An application to a real transportation construction project is also presented. Comparison with a Sequential Forward Selection greedy algorithm shows the superiority of the genetic algorithm search for optimal solutions, and its flexibility for balancing mitigation effects and required budget. Managerial relevance statementThe aim of this paper is to provide managers of engineering projects with an integrated five-step framework to guide the risk response planning process, which is to determine and implement preventive and corrective actions to avoid, reduce or transfer project risks. A -2 -series of quantitative methods have also been presented for practical use, e.g., for modeling risks and risk interactions, predicting global mitigation effects of response actions, and optimizing the allocation of constrained budget to candidate response actions. Thus, the framework supports project managers" decision-making process in coping with the complexity of project risks and resource constraints. An example of application to a real industrial project of implementing a tramway system in a medium-sized city in Europe is also provided. The proposed approach is expected applicable to a wide set of engineering projects for risk management.

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An Alternative FMEA Method for Simple and Accurate Ranking of Failure Modes

Cited by 34 publications

References 19 publications

A hybrid model for learning from failures: The Hurricane Katrina disaster

A hybrid model for learning from failures: The Hurricane Katrina disaster

Failure Mode and Effect Analysis in a Linguistic Context: A Consensus-Based Multiattribute Group Decision-Making Approach

An Integrated Framework for Risk Response Planning Under Resource Constraints in Large Engineering Projects

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