Hu-Chen Liu

Review: Risk evaluation approaches in failure mode and effects analysis: A literature review

Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.

Risk evaluation in failure mode and effects analysis with extended VIKOR method under fuzzy environment

Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying, and eliminating potential failures or problems in products, process, designs, and services. In traditional FMEA, the risk priorities of failure modes are determined by using risk priority numbers (RPNs), which can be obtained by multiplying the scores of risk factors like occurrence (O), severity (S), and detection (D). However, the crisp RPN method has been criticized to have several deficiencies. In this paper, linguistic variables, expressed in trapezoidal or triangular fuzzy numbers, are used to assess the ratings and weights for the risk factors O, S, and D. For selecting the most serious failure modes, the extended VIKOR method is used to determine risk priorities of the failure modes that have been identified. As a result, a fuzzy FMEA based on fuzzy set theory and VIKOR method is proposed for prioritization of failure modes, specifically intended to address some limitations of the traditional FMEA. A case study, which assesses the risk of general anesthesia process, is presented to demonstrate the application of the proposed model under fuzzy environment.

Failure mode and effects analysis using D numbers and grey relational projection method

Abstract Failure mode and effects analysis (FMEA) is a widely used risk assessment tool for defining, identifying and eliminating potential failures or problems in products, process, designs and services. Two critical issues of FMEA are the representation and handling of various types of assessments and the determination of risk priorities of failure modes. Many different approaches have been suggested to enhance the performance of traditional FMEA; however, deficiencies exist in these approaches. In this paper, based on a more effective representation of uncertain information, called D numbers, and an improved grey relational analysis method, grey relational projection (GRP), a new risk priority model is proposed for the risk evaluation in FMEA. In the proposed model, the assessment results of risk factors given by FMEA team members are expressed and modeled by D numbers. The GRP method is used to determine the risk priority order of the failure modes that have been identified. Finally, an illustrative case is provided to demonstrate the effectiveness and practicality of the proposed model.

Fuzzy Failure Mode and Effects Analysis Using Fuzzy Evidential Reasoning and Belief Rule-Based Methodology

The main objective of this paper is to propose a new risk priority model for prioritizing failures in failure mode and effects analysis (FMEA) on the basis of fuzzy evidential reasoning (FER) and belief rule-based (BRB) methodology. The technique is particularly intended to resolve some of the shortcomings in fuzzy FMEA (i.e., fuzzy rule-based) approaches. In the proposed approach, risk factors like occurrence (O), severity (S), and detection (D), along with their relative importance weights, are described using fuzzy belief structures. The FER approach is used to capture and aggregate the diversified, uncertain assessment information given by the FMEA team members; the BRB methodology is used to model the uncertainty, and nonlinear relationships between risk factors and corresponding risk level; and the inference of the rule-based system is implemented using the weighted average-maximum composition algorithm. The Dempster rule of combination is then used to aggregate all relevant rules for assessing and prioritizing the failure modes that have been identified in FMEA. A case study concerning an ocean going fishing vessel in a marine industry is provided and conducted using the proposed model to illustrate its potential applications and benefits.

Evaluating the risk of failure modes with extended MULTIMOORA method under fuzzy environment

Failure mode and effects analysis (FMEA) is a prospective risk assessment tool which has been widely used within various industries, particularly the aerospace, automotive and healthcare industries. However, the conventional risk priority number (RPN) method has been criticized much for its deficiencies in risk factor weights, computation of RPN, evaluation of failure modes and so on. Therefore ranking of failure modes based on their related risk factors is necessary seeking to overcome the shortcomings and enhance the assessment capability of the traditional FMEA. In this paper, we treat the risk factors and their weights as fuzzy variables and evaluate them using fuzzy linguistic terms. As a result, a new risk priority model is proposed for evaluating the risk of failure modes based on fuzzy set theory and MULTIMOORA method. An empirical case of preventing infant abduction is provided to illustrate the potential applications and benefits of the proposed fuzzy FMEA. The main findings of this article are related with the proposed technique for failure modes assessment and ranking, and application of this technique for the prevention of infant abduction, which is a devastating problem for a healthcare facility. A new FMEA model is developed by using fuzzy set theory and MULTIMOORA method.Risk factors and their weights are evaluated using fuzzy linguistic variables.Extended MULTIMOORA is used to determine the risk priority of failure modes.The proposed fuzzy FMEA can be a useful tool for failure modes assessment and ranking.

Failure mode and effects analysis using intuitionistic fuzzy hybrid weighted Euclidean distance operator

Failure mode and effects analysis (FMEA) has shown its effectiveness in examining potential failures in products, process, designs or services and has been extensively used for safety and reliability analysis in a wide range of industries. However, its approach to prioritise failure modes through a crisp risk priority number (RPN) has been criticised as having several shortcomings. The aim of this paper is to develop an efficient and comprehensive risk assessment methodology using intuitionistic fuzzy hybrid weighted Euclidean distance (IFHWED) operator to overcome the limitations and improve the effectiveness of the traditional FMEA. The diversified and uncertain assessments given by FMEA team members are treated as linguistic terms expressed in intuitionistic fuzzy numbers (IFNs). Intuitionistic fuzzy weighted averaging (IFWA) operator is used to aggregate the FMEA team members’ individual assessments into a group assessment. IFHWED operator is applied thereafter to the prioritisation and selection of failure modes. Particularly, both subjective and objective weights of risk factors are considered during the risk evaluation process. A numerical example for risk assessment is given to illustrate the proposed method finally.

Site selection in waste management by the VIKOR method using linguistic assessment

Abstract Site selection is an important issue in municipal solid waste (MSW) management. Selection of the appropriate solid waste site is an extensive evaluation process that requires consideration of multiple alternative solutions and evaluation criteria. In reality, it is easier for decision makers to express their judgments on the alternatives by using linguistic terms, and there usually exists uncertain and incomplete assessment information. Moreover, decision makers may have different risk attitudes in the siting process because of their different backgrounds and personalities. Therefore, an attitudinal-based interval 2-tuple linguistic VIKOR (ITL-VIKOR) method is proposed in this paper to select the best disposal site for MSW. The feasibility and practicability of the proposed method are further demonstrated through an example of refuse-derived fuel (RDF) combustion plant location. Results show that the new approach is more suitable and effective to handle the MSW site selection problems by considering the decision makers attitudinal character and incorporating the uncertain and incomplete assessment information.

A Novel Approach for FMEA: Combination of Interval 2‐Tuple Linguistic Variables and Gray Relational Analysis

Failure mode and effect analysis (FMEA) is a powerful tool for defining, identifying, and eliminating potential failures from the system, design, process, or service before they reach the customer. Since its appearance, FMEA has been extensively used in a wide range of industries. However, the conventional risk priority number (RPN) method has been criticized for having a number of drawbacks. In addition, FMEA is a group decision behavior and generally performed by a cross-functional team. Multiple experts tend to express their judgments on the failure modes by using multigranularity linguistic term sets, and there usually exists uncertain and incomplete assessment information. In this paper, we present a novel FMEA approach combining interval 2-tuple linguistic variables with gray relational analysis to capture FMEA team members’ diversity opinions and improve the effectiveness of the traditional FMEA. An empirical example of a C-arm X-ray machine is given to illustrate the potential applications and benefits of the proposed approach. Copyright

Failure mode and effects analysis using intuitionistic fuzzy hybrid TOPSIS approach

Failure mode and effects analysis (FMEA) is an effective reliability analysis technique used to identify and evaluate potential failures in systems, products, processes, and/or designs. In traditional FMEA, prioritization of failure modes is carried out by utilizing risk priority numbers (RPNs), which can be acquired by the multiplication of three risk factors: occurrence (O), severity (S) and detection (D). However, there are some inherent deficiencies in the conventional RPN method, which affect its effectiveness and thus limit its applications. In response, this paper introduces a new modified TOPSIS method, named intuitionistic fuzzy hybrid TOPSIS approach, to determine the risk priorities of failure modes identified in FMEA. Moreover, both the subjective and objective weights of risk factors are taken into consideration in the process of risk and failure analysis. A product example of the color super twisted nematic is presented at last to demonstrate the potential applications of the proposed approach, and the merits are highlighted by comparing with some existing methods.

Application of interval 2-tuple linguistic MULTIMOORA method for health-care waste treatment technology evaluation and selection.

The management of health-care waste (HCW) is a major challenge for municipalities, particularly in the cities of developing countries. Selection of the best treatment technology for HCW can be viewed as a complicated multi-criteria decision making (MCDM) problem which requires consideration of a number of alternatives and conflicting evaluation criteria. Additionally, decision makers often use different linguistic term sets to express their assessments because of their different backgrounds and preferences, some of which may be imprecise, uncertain and incomplete. In response, this paper proposes a modified MULTIMOORA method based on interval 2-tuple linguistic variables (named ITL-MULTIMOORA) for evaluating and selecting HCW treatment technologies. In particular, both subjective and objective importance coefficients of criteria are taken into consideration in the developed approach in order to conduct a more effective analysis. Finally, an empirical case study in Shanghai, the most crowded metropolis of China, is presented to demonstrate the proposed method, and results show that the proposed ITL-MULTIMOORA can solve the HCW treatment technology selection problem effectively under uncertain and incomplete information environment.