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.     

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.     

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.     

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。     

Multi‐criteria group decision‐making method based on TODIM with probabilistic interval‐valued hesitant fuzzy information

Probabilistic interval‐valued hesitant fuzzy sets (PIV‐HFSs) are suitable for aggregating information from different groups because the probabilistic information of all the groups can be included by using interval values. Moreover, decision makers (DMs) prefer to use interval values to provide evaluation information. Furthermore, the traditional multi‐criteria group decision‐making (MCGDM) approach has some limitations, such as obtaining the DMs' weights with inappropriate methods and neglecting the interactions amongst the criteria and the psychological characteristics of DMs. Motivated by these research background, the main contents of this study are as follows. First, PIV‐HFSs are proposed, and the convex combination operation is extended into PIV‐HFSs. Second, a hybrid MCGDM approach with PIV‐HFSs is suggested that is based on the maximizing deviation method, fuzzy analytic network process (FANP) and TODIM (an acronym in Portuguese for interactive and multi‐criteria decision‐making model). Third, an evaluation case of health management centres based on the service‐specific failure mode and effect analysis (FMEA) is considered. The results show that the most crucial secondary factor is frequency (0.35775) and that the most serious failure mode is the inaccurate check‐in. The results demonstrate that the proposed model can evaluate service quality effectively and that it performs better than other methods.     

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 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.     

Predicting uncertain behavior of industrial system using FM—A practical case

As such the reliability of system is affected by many factors such as design, manufacturing, installation, commissioning, operation and maintenance. Consequently it may be extremely difficult if not impossible to model, analyze and predict the failure behavior of system.To this effect, the authors presented a structured framework which makes use of fuzzy methodology (FM), an approximate reasoning tool to deal with the imprecise, uncertain and subjective information related to system performance. The component related objective events are modeled with the help of the Petri net model of the system. Various 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 uncertain behavior of system. Further, to improve upon the reliability characteristics of the system, in-depth qualitative analysis of unit is carried out using failure mode and effect analysis (FMEA) by listing all possible failure modes and their causes. A decision support system based on fuzzy set theory is developed to counter the limitations of traditional FMEA. The framework has been applied to model and analyze a real complex industrial system from paper mill.     

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).     

A model for failure mode and effects analysis based on intuitionistic fuzzy approach

Failure mode and effects analysis (FMEA) is one of the most powerful methods in the field of risk management and has been widely used for improving process reliability in manufacturing and service sector. High applicability of FMEA has contributed to its applications in many research domains and practical fields pertaining risk assessment and system safety enhancement. However, the method has also been criticized by experts due to several weaknesses and limitations. The current study proposed a novel model for failure mode and effects analysis based on intuitionistic fuzzy approach. This approach offers some advantages over earlier models as it accounts for degrees of uncertainty in relationships among various criteria or options, specifically when relations cannot be expressed in definite numbers. The proposed model provides a tool to evaluate the failure modes, while dealing with vague concepts and insufficient data. The proposed model was tested in a case study examining the failure modes for quality of internet banking services.     

Exploiting 2-tuple linguistic representational model for constructing HOQ-based failure modes and effects analysis

Failure modes and effects analysis (FMEA) is a widely recognized tool used to identify potential failure risks for improving the reliability and safety of new products. A major function of FMEA is to evaluate, analyze, and determine the risk priority number (RPN) of each potential failure mode based on three risk assessment indices: severity, occurrence, and detectability in order to eliminate the potential risk. Unlike in conventional practice, where these indices are measured using discrete ordinal scales, this study adopts a 2-tuple linguistic computational approach to treat the assessments of the three risk indices and develop an FMEA assessment model to overcome the drawbacks of conventional RPN calculation. The house of quality (HOQ), a central tool of quality function deployment, is adopted to determine the more reasonable evaluations of the occurrence and detectability indices. The three risk indices are considered with different weights in the proposed model to obtain RPNs. Compared to conventional and fuzzy FMEA, the proposed FMEA model is more useful for determining practical RPNs for risk analysis and management in new product development (NPD). An example is used to demonstrate the applicability of the proposed model.     

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.     

Experience with fault injection experiments for FMEA

Failure Modes and Effects Analysis (FMEA) is a widely used system and software safety analysis technique that systematically identifies failure modes of system components and explores whether these failure modes might lead to potential hazards. In practice, FMEA is typically a labor‐intensive team‐based exercise, with little tool support. This article presents our experience with automating parts of the FMEA process, using a model checker to automate the search for system‐level consequences of component failures. The idea is to inject runtime faults into a model based on the system specification and check if the resulting model violates safety requirements, specified as temporal logical formulas. This enables the safety engineer to identify if a component failure, or combination of multiple failures, can lead to a specified hazard condition. If so, the model checker produces an example of the events leading up to the hazard occurrence which the analyst can use to identify the relevant failure propagation pathways and co‐effectors. The process is applied on three medium‐sized case studies modeled with Behavior Trees. Performance metrics for SAL model checking are presented. Copyright © 2011 John Wiley & Sons, Ltd.     

Nonlinear fractional‐order power system stabilizer for multi‐machine power systems based on sliding mode technique

This paper presents two novel nonlinear fractional‐order sliding mode controllers for power angle response improvement of multi‐machine power systems. First, a nonlinear block control is used to handle nonlinearities of the interconnected power system. In the second step, a decentralized fractional‐order sliding mode controller with a nonlinear sliding manifold is designed. Practical stability is achieved under the assumption that the upper bound of the fractional derivative of perturbations and interactions are known. However, when an unknown transient perturbation occurs in the system, it makes the evaluation of perturbation and interconnection upper bound troublesome. In the next step, an adaptive‐fuzzy approximator is applied to fix the mentioned problem. The fuzzy approximator uses adjacent generators relative speed as own inputs, which is known as semi‐decentralized control strategy. For both cases, the stability of the closed‐loop system is analyzed by the fractional‐order stability theorems. Simulation results for a three‐machine power system with two types of faults are illustrated to show the performance of the proposed robust controllers versus the conventional sliding mode. Additionally, the fractional parameter effects on the system transient response and the excitation voltage amplitude and chattering are demonstrated in the absence of the fuzzy approximator. Finally, the suggested controller is combined with a simple voltage regulator in order to keep the system synchronism and restrain the terminal voltage variations at the same time. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive fuzzy semi‐decentralized control for a class of large‐scale nonlinear systems with unknown interconnections

In this paper, a general method is developed to generate a stable adaptive fuzzy semi‐decentralized control for a class of large‐scale interconnected nonlinear systems with unknown nonlinear subsystems and unknown nonlinear interconnections. In the developed control algorithms, fuzzy logic systems, using fuzzy basis functions (FBF), are employed to approximate the unknown subsystems and interconnection functions without imposing any constraints or assumptions about the interconnections. The proposed controller consists of primary and auxiliary parts, where both direct and indirect adaptive approaches for the primary control part are aiming to maintain the closed‐loop stability, whereas the auxiliary control part is designed to attenuate the fuzzy approximation errors. By using Lyapunov stability method, the proposed semi‐decentralized adaptive fuzzy control system is proved to be globally stable, with converging tracking errors to a desired performance. Simulation examples are presented to illustrate the effectiveness of the proposed controller. Copyright © 2006 John Wiley & Sons, Ltd.     

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。     

Grammatical and context‐sensitive error correction using a statistical machine translation framework

Producing electronic rather than paper documents has considerable benefits such as easier organizing and data management. Therefore, existence of automatic writing assistance tools such as spell and grammar checker/correctors can increase the quality of electronic texts by removing noise and correcting the erroneous sentences. Different kinds of errors in a text can be categorized into spelling, grammatical and real‐word errors. In this article, we present a language‐independent approach based on a statistical machine translation framework to develop a proofreading tool, which detects grammatical errors as well as context‐sensitive spelling mistakes (real‐word errors). A hybrid model for grammar checking is suggested by combining the mentioned approach with an existing rule‐based grammar checker. Experimental results on both English and Persian languages indicate that the proposed statistical method and the rule‐based grammar checker are complementary in detecting and correcting syntactic errors. The results of the hybrid grammar checker, applied to some English texts, show an improvement of about 24% with respect to the recall metric with almost similar value for precision. Experiments on real‐world data set show that state‐of‐the‐art results are achieved for grammar checking and context‐sensitive spell checking for Persian language. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

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.