An extended FMEA approach based on the Z-MOORA and fuzzy BWM for prioritization of failures

Identification and prioritization of failure modes in a system and planning for corrective actions are among the most important components of risk management in any organization. Meanwhile, conventional Failure Mode and Effects Analysis (FMEA) is one of the most commonly used methods for prioritization of the failures. Despite the widespread applications of this method in various industries, FMEA is associated with some shortcomings that can lead to unrealistic results. In this study, a proposed approach is presented in three phases to cover some of the shortcomings of the FMEA technique. In the first phase, FMEA is used to identify the failure modes and assign values to the Risk Priority Number (RPN) determinant factors. In the second phase, the Fuzzy Best-Worst Method (FBWM) based on the experts’ opinions is used to measure the weights of these factors. In the third phase, the outputs of the previous phases are used as a basis to prioritize the failures using the proposed Multi-Objective Optimization by Ratio Analysis based on the Z-number theory (Z-MOORA). In addition to assigning different weights to the RPN determinant factors and considering uncertainties of them, the Z-number theory is used in this approach to cover reliability in different failure modes. The proposed approach was implemented in the automotive spare parts industry, and the results indicate a full prioritization of the failures in comparison with other conventional methods such as FMEA and fuzzy MOORA.     

A novel approach for failure mode and effects analysis using combination weighting and fuzzy VIKOR method

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis tools for identifying, assessing and eliminating potential failure modes in a wide range of industries. In general, failure modes in FMEA are evaluated and ranked through the risk priority number (RPN), which is obtained by the multiplication of crisp values of the risk factors, such as the occurrence (O), severity (S), and detection (D) of each failure mode. However, the conventional RPN method has been considerably criticized for various reasons. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in risk evaluation process, this paper presents a novel approach for FMEA based on combination weighting and fuzzy VIKOR method. Integration of fuzzy analytic hierarchy process (AHP) and entropy method is applied for risk factor weighting in this proposed approach. The risk priorities of the identified failure modes are obtained through next steps based on fuzzy VIKOR method. To demonstrate its potential applications, the new fuzzy FMEA is used for analyzing the risk of general anesthesia process. Finally, a sensitivity analysis is carried out to verify the robustness of the risk ranking and a comparison analysis is conducted to show the advantages of the proposed FMEA approach.     

Failure mode and effects analysis based on a novel fuzzy evidential method

Failure mode and effect analysis (FMEA) has been widely applied to examine potential failures in systems, designs, and products. The risk priority number (RPN) is the key criteria to determine the risk priorities of the failure modes. Traditionally, the determination of RPN is based on the risk factors like occurrence (O), severity (S) and detection (D), which require to be precisely evaluated. However, this method has many irrationalities and needs to be improved for more applications. To overcome the shortcomings of the traditional FMEA and better model and process uncertainties, we propose a FMEA model based on a novel fuzzy evidential method. The risks of the risk factors are evaluated by fuzzy membership degree. As a result, a comprehensive way to rank the risk of failure modes is proposed by fusing the feature information of O, S and D with Dempster–Shafer (D–S) evidence theory. The advantages of the proposed method are that it can not only cover the diversity and uncertainty of the risk assessment, but also improve the reliability of the RPN by data fusion. To validate the proposed method, a case study of a micro-electro-mechanical system (MEMS) is performed. The experimental results show that this method is reasonable and effective for real applications.     

基于模糊逻辑的安全苛求系统失效模式与影响分析研究

针对传统RPN(风险优先数)方法在安全苛求系统FEMA(失效模式与影响分析)过程中的不足,提出一种基于FRPN(模糊风险优先数)的新方案,即运用模糊逻辑理论进行定量的FMEA。通过运用模糊加权几何平均计算失效模式的FRPN,据此对失效模式的风险程度排序,寻找影响系统安全性的主要因素,并将其作为改善对象。最后将该方法应用到高速铁路列控系统的FMEA过程中,结果表明,所提出的FRPN方法比传统RPN更加科学严谨,更能紧密联系应用环境,具有较高的科学性和实用价值。     

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.     

An integrated framework for mechatronics based product development in a fuzzy environment

New product development (NPD) is a term used to describe the complete process of bringing a new concept to a state of market readiness. Mechatronics based product requires a multidisciplinary approach for its modeling, design, development and implementation. An integrated and concurrent approach focusing on integrating the mechanical structure with basic three components namely sensors, controllers and actuators is required. This paper aims at developing a framework for a new Mechatronics product development. For conceptual design of Mechatronics system, various tools like Fuzzy Delphi Method (FDM), Fuzzy Interpretive Structural Modeling (FISM), Fuzzy Analytical Network Process (FANP) and Fuzzy Quality Function Deployment (FQFD) are used. Based on the prioritized design requirements, the functional specifications of the required components are developed. Then, Computer Aided Design and control system software are used to develop the detailed system design. Then, a prototype model is developed based on the integration of mechanical system with Sensor, Controller and Electrical units. Performance of the prototype model is monitored and Fuzzy failure mode and effect analysis (FMEA) is then used to rank the potential failures. Based on the results of fuzzy FMEA, the developed model is redesigned. The proposed framework is illustrated with a case study related to developing automatic power loom reed cleaning machine.     

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.     

A new fuzzy methodology-based structured framework for RAM and risk analysis

The aim of this paper is to propose a new hybridized framework for analyzing the performance issues of a chemical process plant by utilizing uncertain, imprecise and vague information. In the proposed framework, Fuzzy Lambda–Tau (FLT) approach has been used for computing reliability, availability and maintainability (RAM) parameters of the considered system. Further, for enhancing the RAM characteristics of the system, improved Fuzzy Failure Mode Effect Analysis (FMEA) approach is adopted. Under improved Fuzzy FMEA approach, defined Fuzzy linguistic rating values in the form of triangular and trapezoidal Fuzzy numbers have been assigned by the experts to each risk factor of the listed failure causes. The proposed framework is demonstrated with an industrial application in a chlorine production plant of a chemical process industry. The results show decreasing trend for system availability and deposition of solid Nacl, mechanical failure, corrosion due to wet chlorine, scanty lubrication, improper adsorption and valve malfunctioning are identified as the most critical failure causes for the considered system. A comparative performance analysis between the proposed framework, Fuzzy technique for order of preference by similarity to ideal solution (Fuzzy TOPSIS), Fuzzy evaluation based on distance from average solution (Fuzzy EDAS) and Fuzzy Vlse Kriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR) are then carried out to show the competence of the proposed framework. It is expected that the analytical results would be highly useful in formulating an optimal maintenance policy for such complex systems and may also be used for improving performance of similar plants.     

Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach

Failure mode and effects analysis (FMEA) is a widely used engineering technique for identifying and eliminating known and potential failures from systems, designs, products, processes or services. However, the conventional risk priority number method has been extensively criticized in the literature for a lot of reasons such as ignoring relative importance of risk factors, questionable multiplication procedure, and imprecisely evaluation. In this article, a new FMEA model based on fuzzy digraph and matrix approach is developed to solve the problems and improve the effectiveness of the traditional FMEA. All the information about risk factors like occurrence (O), severity (S) and detection (D) and their relative weights are expressed in linguistic terms, represented by fuzzy numbers. By considering the risk factors and their relative importance, a risk factors fuzzy digraph is developed for the optimum representation of interrelations. Then, corresponding fuzzy risk matrixes are formed for all the identified failure modes in FMEA and risk priority indexes are computed for determining the risk priorities of the failure modes. Finally, a case study of steam valve system is included to illustrate the proposed fuzzy FMEA and the advantages are highlighted by comparing with the listed methods.     

An intutionistic approach based on failure mode and effect analysis for prioritizing corrective and preventive strategies

Although Failure Mode and Effect Analysis (FMEA) is a prominent approach that has been used with convenience as the most popular risk definition and evaluation tool related to a system, a product, or a service, it has several deficiencies. This study addresses these deficiencies and proposes a new intuitionistic approach, which combines FMEA and Weighted Aggregated Sum Product Assessment (WASPAS) by implementing a new intuitionistic scale. Intuitionistic FMEA‐WASPAS can handle uncertainty, vagueness, and hesitancy of the risk evaluation process and provides flexibility for risk assessment. In this study, rankings of corrective‐preventive strategies for failure modes (FMs) are obtained by the proposed approach. To compute Intuitionistic Fuzzy Risk Priority Numbers, occurrence, severity, detection, cost, duration of exposure, and system safety factors are used. A numerical example is also illustrated to present the practicality and effectiveness of the Intuitionistic FMEA‐WASPAS approach.     

A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis techniques due to its outstanding capabilities in identifying, assessing, and eliminating potential failure modes in a wide range of industrial applications. It provides a comprehensive view for investigating potential failures, causes, and effects in designs, products, and processes. However, traditional FMEA is extensively criticized for its defects in determining the criteria weights, identifying the risk priority of failure modes, and handling the uncertainty during the risk evaluation. To resolve these problems, this study proposes a novel fuzzy rough number extended multi-criteria group decision-making (FR-MCGDM) strategy to determine a more rational rank of failure modes by integrating the fuzzy rough number, AHP (analytic hierarchy process), and VIKOR (Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje). Above all, a fuzzy rough number is introduced to characterize experts’ judgment, aggregate group risk assessments, and tackle the uncertainty and subjectivity in the risk evaluation. Then a fuzzy rough number enhanced AHP is presented to determine the criteria weights. A fuzzy rough number enhanced VIKOR is proposed to rank the failure modes. A practical case study of the check valve is provided to validate the applicability of the proposed FMEA. Comparative studies demonstrate the efficacy of the proposed FR-MCGDM, with remarkable advantages in handling the uncertainty and subjectivity during failure modes evaluation.     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.     

A systematic decision making approach for product conceptual design based on fuzzy morphological matrix

Conceptual design plays an important role in development of new products and redesign of existing products. Morphological matrix is a popular tool for conceptual design. Although the morphological-matrix based conceptual design approaches are effective for generation of conceptual schemes, quantitative evaluation to each of the function solution principle is seldom considered, thus leading to the difficulty to identify the optimal conceptual design by combining these function solution principles. In addition, the uncertainties due to the subjective evaluations from engineers and customers in early design stage are not considered in these morphological-matrix based conceptual design approaches. To solve these problems, a systematic decision making approach is developed in this research for product conceptual design based on fuzzy morphological matrix to quantitatively evaluate function solution principles using knowledge and preferences of engineers and customers with subjective uncertainties. In this research, the morphological matrix is quantified by associating the properties of function solution principles with the information of customer preferences and product failures. Customer preferences for different function solution principles are obtained from multiple customers using fuzzy pairwise comparison (FPC). The fuzzy customer preference degree of each solution principle is then calculated by fuzzy logarithmic least square method (FLLSM). In addition, the product failure data are used to improve product reliability through fuzzy failure mode effects analysis (FMEA). Unlike the traditional FMEA, the causality relationships among failure modes of solution principles are analyzed to use failure information more effectively through constructing a directed failure causality relationship diagram (DFCRD). A fuzzy multi-objective optimization model is also developed to solve the conceptual design problem. The effectiveness of this new approach is demonstrated using a real-world application for conceptual design of a horizontal directional drilling machine (HDDM).     

结合OWA算子和模糊DEMATEL的风险评估方法

针对传统故障模式与影响分析(FMEA)方法在实际应用中的不足,提出一种基于有序加权平均(OWA)算子和决策试行与评价实验法(DEMATEL)的风险排序方法。FMEA专家对故障模式的3个风险因子给出模糊评价信息,应用OWA算子对评估信息进行集结,得到各故障原因对故障模式的影响强度。采用模糊DEMATEL法构建FMEA系统要素间的初始直接影响矩阵,经过运算可得综合影响矩阵,并计算各故障原因的原因度,据此进行产品或系统的失效风险评估。运用该方法对地铁车门系统的基础部件进行安全性分析,并将所得结果与传统RPN方法的结果做对比,验证了该方法的可行性和有效性。     

Fuzzy failure modes and effects analysis by using fuzzy TOPSIS-based fuzzy AHP

Failure mode and effects analysis (FMEA) is a widely used engineering technique for designing, identifying and eliminating known and/or potential failures, problems, errors and so on from system, design, process, and/or service before they reach the customer (Stamatis, 1995). In a typical FMEA, for each failure modes, three risk factors; severity (S), occurrence (O), and detectability (D) are evaluated and a risk priority number (RPN) is obtained by multiplying these factors. There are significant efforts which have been made in FMEA literature to overcome the shortcomings of the crisp RPN calculation. In this study a fuzzy approach, allowing experts to use linguistic variables for determining S, O, and D, is considered for FMEA by applying fuzzy ‘technique for order preference by similarity to ideal solution’ (TOPSIS) integrated with fuzzy ‘analytical hierarchy process’ (AHP). The hypothetical case study demonstrated the applicability of the model in FMEA under fuzzy environment.     

Failure Mode and Effects Analysis Based on Z-numbers

The main objective of this paper is to propose a new method for failure mode and effects analysis (FMEA) based on Z-numbers. In the proposed method, firstly, Z-numbers are used to perform the valuations (Z-valuation) of the risk factors like occurrence (O), severity (S) and detection (D). Secondly, the Z-valuations of the risk factors are integrated by fuzzy weighted mean method. A new risk priority number named as ZRPN is calculated to prioritize failure modes based on a modified method of ranking fuzzy numbers. Finally, a case study for the rotor blades of an aircraft turbine is performed to demonstrate the feasibility of the proposed method.     

Fuzzy modeling of system behavior for risk and reliability analysis

The main objective of the article is to permit the reliability analyst's/engineers/managers/practitioners to analyze the failure behavior of a system in a more consistent and logical manner. To this effect, the authors propose a methodological and structured framework, which makes use of both qualitative and quantitative techniques for risk and reliability analysis of the system. The framework has been applied to model and analyze a complex industrial system from a paper mill. In the quantitative framework, after developing the Petrinet model of the system, the fuzzy synthesis of failure and repair data (using fuzzy arithmetic operations) has been done. Various system parameters of managerial importance such as repair time, failure rate, mean time between failures, availability, and expected number of failures are computed to quantify the behavior in terms of fuzzy, crisp and defuzzified values. Further, to improve upon the reliability and maintainability characteristics of the system, in depth qualitative analysis of systems is carried out using failure mode and effect analysis (FMEA) by listing out all possible failure modes, their causes and effect on system performance. To address the limitations of traditional FMEA method based on risky priority number score, a risk ranking approach based on fuzzy and Grey relational analysis is proposed to prioritize failure causes.     

Evaluating the risk of failure using the fuzzy OWA and DEMATEL method

Most current risk assessment methods use the risk priority number (RPN) value to evaluate the risk of failure. However, traditional RPN methodology has been criticized to have several shortcomings. Therefore, an efficient, simplified algorithm to evaluate the orderings of risk for failure problems is proposed in this paper, which utilizes fuzzy ordered weighted averaging (OWA) and the decision making trial and evaluation laboratory (DEMATEL) approach to rank the risk of failure. The proposed approach resolves some of the shortcomings of the traditional RPN method. In numerical verification, a failure mode and effects analysis (FMEA) of the thin film transistor liquid crystal display (TFT-LCD) product is presented to further illustrate the proposed approach. The results show that the proposed approach can reduce duplicated RPN numbers and get a more accurate, reasonable risk assessment. As a result, the stability of product and process can be assured.     

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

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.     

基于战略对应的IT项目组合选择模型

把信息技术项目当作组合来管理可以通过平衡风险和收益来促进企业目标和IT应用的结合,但由于决策信息的不确定性和IT项目目标与企业战略的难以对应,企业面临IT项目组合选择的挑战。构建基于战略对应的IT项目组合选择模型,其中模糊集和模糊层次分析法用来刻画不确定信息和评估IT项目风险、成本及收益,关键成功因素法用来提高IT项目与企业战略的对应,并建立模糊0－1整数规划。利用定性可能性理论把模糊组合选择模型转化为一般可求解的整数规划形式,最后用一个案例说明模型的用法。