Failure Mode And Effects Analysis Team Members Research Articles

Evidential FMEA method for human reliability assessment

Evidence theory is a useful tool for modeling and reasoning uncertain information inherent in experts’ evaluations, which is not handled efficiently in traditional failure mode and effects analysis (FMEA). This study proposes an integrated FMEA method that incorporates evidence theory and is applied to human reliability assessment. The human error information of a human-machine system in FMEA is described as a directed graph by a Bayesian network (BN) to assess the dependence among potential human-related failure modes. The BN is extended to propagate the epistemic uncertainty of FMEA team members, where belief mass is applied to model uncertainties in team members’ knowledge and to convert their subjective cognition into varying levels of uncertainty. Risk indexes for occurrence, severity and detection from multiple sources are defined as a special assessment state. The combination of the belief mass of different failure modes is performed using extended Dempster’s rules to avoid the influence of conflicting evidence. Finally, an application in the healthcare system is provided to verify the effectiveness of our model. A comparison with other fuzzy FMEA methods is also conducted, demonstrating the advantages of the proposed model in dealing with decision-makers’ epistemic uncertainty and potential failure mode interdependencies.

A hybrid generalized TODIM based risk prioritization method for failure mode and effect analysis with linguistic Z-numbers

The traditional failure mode and effect analysis (FMEA), as an effective risk analysis technique, has several limitations in the uncertainty modeling and the weights determination of the risk indicators. This paper aims to propose a hybrid risk prioritization method simultaneously considering the characteristics of the reliability associated with the FMEA team members’ evaluation information and their psychological behavior to enhance the performance of the traditional FMEA model. The hybrid risk prioritization method is developed based on the generalized TODIM method and the weighted entropy measure with the linguistic Z-numbers (LZNs). First, the LZNs are adopted to depict the FMEA team members’ cognition information and the reliability of these information. Second, a weighted entropy measure based on the fuzzy entropy and the LZNs is developed to obtain the risk indicators’ weights. Finally, the generalized TODIM method with the LZNs is constructed to obtain the risk priority orders of failure modes, which can effectively simulate the FMEA team members’ psychological character. The applicability and effectiveness of the proposed risk prioritization method is validated through an illustrative example of an integrated steel plant. The results of sensitivity analysis and comparative analysis indicate that the proposed hybrid risk prioritization method is effective and valid, and can get more accurate and practical risk ranking results to help enterprises formulate accurate risk prevention and control plans.

A comparative assessment of Conventional and Rough-Based Multi-Criteria methods for failure mode and effects analysis of Root canal treatment

This paper presents failure mode and effects analysis (FMEA) of a dentistry procedure i.e. root canal treatment (RCT) The aims of the present study are as follows: (i) to identify the potential failure modes (FMs) associated with RCT and (ii) to determine the ranks of the identified FMs to represent their degree of severity for the failure of the RCT procedure. Sixteen potential FMs of RCT are identified with the help of five members of the FMEA team. The FMs are evaluated with respect to three risk factors (RFs) i.e. severity (S), occurrence (O), and detection (D). Opinions regarding the importance of RFs on a scale of 1-9 and importance of each RF for each FM on a scale of 1-10 are gathered from FMEA team members. Subsequently, the obtained information is transformed into rough numbers in order to take care of the vagueness and inconsistency present in the human judgement. Further, weights of the RFs, in terms of rough numbers, are determined. Subsequently, four rough-based multi-criteria decision making (MCDM) methods i.e. rough weighted aggregated sum product assessment (R-WASPAS), rough additive ratio assessment (R-ARAS), rough TOPSIS (R-TOPSIS), and rough VIKOR (R-VIKOR) are used to rank the FMs. The final ranking of the FMs is derived using the grade average integrated ranking method. The results reveal that “Missed canal” and “Root canal under fillings” are the most and the least critical FMs, respectively. Further, the top 20%, the next 30%, and the last 50% FMs are categorized into highly, fairly, and the least critical groups. A few important suggestions are proposed to reduce the risks of the top 20% FMs. The results of the study may be useful for the professionals involved in the RCT and may help them in properly planning and successfully executing the RCT procedure.

A New Model for Failure Mode and Effects Analysis Based on k-Means Clustering Within Hesitant Linguistic Environment

Failure mode and effects analysis (FMEA) is a systematic and proactive risk prevention and management tool used to improve the reliability and safety of a system, process or service. However, many defects around the traditional FMEA affect the effectiveness of its practical applications. When dealing with the FMEA problems, the first and the most important step is to collect the risk assessment information of experts regarding failure modes. Moreover, clustering failure modes is more adaptable to actual needs, especially under a tight resource constraint. In this article, a new FMEA model using double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering is developed to evaluate and cluster the risk of failure modes. First, the DHHFLTSs are utilized to describe the complex linguistic assessments on failure modes given by FMEA team members. Next, an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means algorithm is proposed and used to cluster failure modes into priority classes. Finally, a case study concerning the risk evaluation over floating offshore wind turbine is provided to illustrate the effectiveness and usefulness of our proposed FMEA. The results show that the new risk classification approach is more reasonable and practical for risk management decision making.

A modified method to improve failure analysis

Failure mode and effects analysis (FMEA) is an effective method that is widely used to manage failures and prevent risks, and, thanks to its simplicity and its versatility, the most important and renowned quality norms request it use. However, in literature, several works have criticized this tool and claims that it had short-comes. The FMEA team members are considered with the same importance and failures values are calculated meaning their judgement and without any differentiation. Moreover, failures modes are supposed to be independent and the effect of their relationship is ignored in the risk analysis. The accuracy of the FMEA analysis is also reduced due to the fact that risk factors are supposed to have the same importance. In this article, we propose to combine FMEA with other methods in order to overcame it limitedness. Firstly, a criteria selection will be proposed in order to select the most relevant factors for risk analysis. Their weights will be calculated using the best worst method (BWM). Secondly, a weight associated to every team member will be calculated and used to obtain failures values. Thirdly, the design structure method (DSM) will be used to identify the relationship between failures and to judge their importance, we propose to use the fuzzy technique for order of preference by similarity method (FTOPSIS). In order to test the applicability and the effectiveness of this method, a case study will be proposed in the end.

Selection of product recycling channels based on extended TODIM method

The evaluation and selection of product recovery channels is a key decision issue in closed-loop supply chain management. Although existing research does not take into account the presence of inaccurate information and the influence of the decision maker’s psychological state during the selection of recycling channels, we study the issue of recycling channel selection under the consideration of inaccurate information and the influence of the decision maker’s psychological state. (1) First, we establish a new evaluation index system for recycling channels for policy makers. (2) We propose a TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method based on fuzzy evaluation and Shapley index for decision makers to imitate the psychological behavior characteristics of FMEA (Failure mode and effect analysis) team members. It can also be used to estimate the risk priority of several failure modes. The interval type 2 fuzzy is used to describe the uncertainty in the risk assessment process. (3) Finally, we obtain the practical significance of the method proposed in this paper through case studies, sensitivity analysis and comparative analysis of traditional methods. We also find the key impact factors in the choice of recycling channels.

A New Integrated Approach for Risk Evaluation and Classification With Dynamic Expert Weights

Failure mode and effect analysis (FMEA), as a proactive reliability management technique, has been widely employed to reduce the risk of systems and assure the quality of products in various industries. Nevertheless, the traditional FMEA method shows many weaknesses when used in real-life scenarios. This causes the dilemma for practitioners that it is not great as expected. Many alternative risk priority models have been developed to enhance the capacity of FMEA, but the majority of them focus on how to acquire a complete ranking of failure modes. In this article, we propose a novel FMEA approach using hesitant uncertain linguistic Z numbers (HULZNs) and density-based spatial clustering of applications with noise (DBSCAN) algorithm to assess and cluster the risk of failure modes. The HULZNs are adopted to represent the uncertain and hesitant risk evaluation information of FMEA team members. The normal DBSCAN algorithm is improved to cluster the recognized failure modes into different risk classes. Moreover, the weights of FMEA experts are dynamically obtained with a weight adjustment method. Finally, a practical case of a geothermal power plant is given to demonstrate the effectiveness and validity of our introduced FMEA approach.

An improved reliability model for FMEA using probabilistic linguistic term sets and TODIM method

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model.

Risk analysis of health, safety and environment in chemical industry integrating linguistic FMEA, fuzzy inference system and fuzzy DEA

Organizations are continuously endeavoring to provide a healthy work environment without any incident, by Health, Safety, and Environment (HSE) management. As most of the activities and processes in the organizations have risk-taking nature, identification and evaluation of risks can be useful to decrease their negative effects on the system. Although Failure Mode and Effect Analysis (FMEA) technique is used widely for risk assessment, the traditional Risk Priority Number (RPN) score has shortcomings like do not considering different weights and the inherent uncertainty of risk factors as well as do not regarding all viewpoints of the experts in decision making. The aim of this study is presenting a hybrid approach based on the Linguistic FMEA, Fuzzy Inference System (FIS) and Fuzzy Data Envelopment Analysis (DEA) model to calculate a novel score for covering some RPN shortcomings and the prioritization of HSE risks. First, after identifying potential risks and assigning values to the RPN determinant factors by linguistic FMEA team members due to the differentiation of these values, FIS is used to reach a consensus opinion about these factors. Then, the outputs of FIS are used by the fuzzy DEA and its supper efficiency model to risk prioritization which can contribute to full prioritization. In addition to considering uncertainty and decreasing dependence on the team’s opinions, in this phase weights of triple factors are calculated based on mathematical programming. To show the ability of the proposed approach in terms of HSE risks prioritization, it has been implemented in an active company in the chemical industry. After identifying risks having high priority based on the proposed score, preventive/corrective actions are presented in accordance with the case study, and for more analysis of results, the self-organizing map has been applied in this study.

Risk assessment based on hybrid FMEA framework by considering decision maker’s psychological behavior character

As a proactive risk analysis technique, the failure mode and effect analysis (FMEA) has been widely utilized to assure the reliability and safety of various fields. However, the conventional FMEA model possess several drawbacks such as overlooking FMEA team member’s psychological behavior, uncertainty and interaction relationships among risk indicators. In this paper, we develop a hybrid FMEA framework integrating TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) approach and Choquet integral method to contribute to these research gaps. In this framework, the generalized trapezoidal fuzzy numbers are adopted to depict the uncertainty in risk evaluation. Then, an improved GTrFN-WAA (weighted arithmetic averaging operator of GTrFN) operator based on similarity measure is constructed to aggregate risk evaluation information for FMEA team member. This aggregation method can takes into account different types of risk evaluation information. Next, the extended TODIM method with Choquet integral is developed to determine risk priority of each failure mode. This method can simulate the psychological behavior of FMEA team members and model interaction relationships among risk indicators. Finally, a practical example of FMEA problem is presented to demonstrate the application and feasibility of the proposed hybrid FMEA framework, and comparison and sensitivity studies are also conducted to validate the effectiveness of the improved risk assessment approach.

Failure Mode and Effects Analysis Using Two-Dimensional Uncertain Linguistic Variables and Alternative Queuing Method

This study develops an improved failure mode and effects analysis (FMEA) method using two-dimensional uncertain linguistic variables (2DULVs) and alternative queuing method (AQM). The 2DULVs are employed to represent the evaluations provided by FMEA team members on the weights of risk factors and the risk of failure modes in the form of pairwise comparisons. The two-dimensional uncertain linguistic best worst method (2DUL-BWM) is used to derive the weights of risk factors. The two-dimensional uncertain linguistic AQM (2DUL-AQM) is proposed for determining the risk ranking of the identified failure modes. Finally, the maintenance of a water treatment plant is presented as an example to demonstrate the applicability and effectiveness of the proposed FMEA method. By comparing its performance with those of other existing methods, the proposed FMEA is shown to be more advantageous in ranking the risk of failure modes.

An extended generalized TODIM for risk evaluation and prioritization of failure modes considering risk indicators interaction

Failure Mode and Effect Analysis (FMEA) is considered as a proactive risk prevention and control technique that has been widely applied to identify, assess and eliminate the risk of failure modes in various fields. Nevertheless, the interactions between risk indicators and decision maker’s psychological behavior characteristics are seldom considered simultaneously in the current FMEA method. In this article, we develop a hybrid FMEA framework integrating generalized TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method, Choquet integral and Shapley index to remedy this gap. In the proposed FMEA framework, fuzzy measures and Shapley index are used to model the interaction relationships among risk indicators and to determine the weights of these indicators. The extended generalized TODIM method with fuzzy measure and Shapley index is presented to simulate the psychological behavior characteristics of FMEA team members. It is also applied to calculate the risk priority of each failure mode. Trapezoidal Fuzzy Numbers (TrFNs) are adopted to depict the uncertainty in the risk evaluation process. Furthermore, a new risk evaluation information fusion with TrFNs-WAIA (weighted arithmetic interaction averaging operator of the trapezoidal fuzzy numbers) operator based on Shapley Choquet is developed to aggregate individual risk evaluation information of FMEA team member into a group risk evaluation matrix, which considers the potential correlations among these members. Finally, a practical example of FMEA problem is presented to demonstrate the application and feasibility of the proposed hybrid FMEA framework, and comparison and sensitivity studies are also conducted to validate the effectiveness of the improved FMEA approach.

Improving Risk Evaluation in FMEA With Cloud Model and Hierarchical TOPSIS Method

Failure mode and effect analysis (FMEA) is a prospective reliability analysis technique used in a wide range of industries for enhancing the safety and reliability of systems, products, processes, and services. However, the conventional FMEA method has been criticized for inherent drawbacks that limit effectiveness and applications. In this paper, a novel integrated FMEA model based on cloud model theory and hierarchical technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to assess and rank the risk of failure modes. First, individual linguistic assessments of failure modes are converted into normal clouds. Then, FMEA team members’ weights are calculated based on the subjective weighting information. Finally, the risk priority of failure modes is determined by using the cloud hierarchical TOPSIS. The newly proposed FMEA method combines the advantages of the cloud model in coping with fuzziness and randomness of linguistic assessments and the merits of hierarchical TOPSIS in solving complex decision making problems. Two empirical examples to illustrate the feasibility and effectiveness of the proposed FMEA are presented together with a comparison to existing methods.

A Novel Failure Mode and Effect Analysis Approach Integrating Probabilistic Linguistic Term Sets and Fuzzy Petri Nets

Failure mode and effect analysis (FMEA) is an effective quality management technique widely used in various industries to improve the reliability and safety of systems, products, processes, and services. In traditional FMEA, the ranking of failure modes is carried out by the risk priority number (RPN), which is calculated by the product of severity (S), occurrence (O), and detection (D). Nevertheless, the normal FMEA has many inherent defects in assessing and ranking failure modes. Therefore, in this paper, we present a new FMEA model, which integrates probabilistic linguistic term sets (PLTSs) and fuzzy Petri nets (FPNs) for the risk assessment and prioritization of failure modes. Specifically, the PLTSs are used to capture the uncertainty of FMEA team members’ subjective judgments, and the FPNs are established to acquire the risk priority of the identified failure modes. Besides, a technique for order preference by similarity to an ideal solution (TOPSIS)-based weighting method is proposed to determine the objective weight of each team member. Finally, a marine-ship system risk assessment example is provided to illustrate the suggested FMEA and a comparative analysis is conducted to assess its effectiveness and usefulness. The results show that the new FMEA approach can produce more reliable and reasonable risk ranking result of failure modes.

Failure modes and effects analysis for CO2 transmission pipelines using a hesitant fuzzy VIKOR method

Carbon capture, utilization, and storage (CCUS) is now considered to be one of the main technological options to reduce greenhouse gas emissions. It seems that pipeline transmission is an effective way to transport the large amount of CO2 from the capture site to the storage site over long distance for most CCUS projects. Therefore, risk assessment of CO2 transmission pipelines has been an important aspect for successful development of the CCUS industry in the future. Failure mode and effects analysis (FMEA) is a group-based activity to prioritize risk. A group multi-criteria decision-making approach based on combined weights and the hesitant fuzzy VIKOR (vlsekriterijumska optimizacija i kompromisno resenje in Serbian, meaning “multi-criteria optimization and compromise solution”) method is proposed to evaluate the risk of CO2 transmission pipelines. First, a pair-wise comparison matrix for risk factors is constructed according to the evaluation information given by the FMEA team members, and a fuzzy analytic hierarchy process is used to determine the subjective weight vector of these risk factors. The maximizing deviation method is used to determine the objective weights of risk factors. Then, to select the most serious failure modes for CO2 transmission pipelines, the hesitant fuzzy VIKOR method is used to prioritize the potential failure modes that have been identified by the FMEA team members. Finally, a numerical example for risk assessment is given to illustrate the effectiveness and rationality of the proposed method.

Risk evaluation by FMEA of supercritical water gasification system using multi-granular linguistic distribution assessment

The sustainability challenge is increasingly driving the adoption of supercritical water gasification (SCWG) technology to ensure the elimination and recovery of pollution produced by sewage sludge treatment (SST). Risk evaluation by failure mode and effects analysis (FMEA) plays a crucial role in guaranteeing the reliability and safety of SCWG systems. However, some limitations in existing FMEA methods need to be ameliorated. Multiple risk factors are involved in prioritizing risk levels for failure modes in SCWG systems, it is essential a multiple criteria decision making (MCDM) process, in which overall assessments of failure modes should be provided according to their performances from several points during a system operation period. Due to differences in knowledge backgrounds and experiences, FMEA team members prefer to utilize multi-granular linguistic term sets to express their assessments of system risk. A hybrid risk evaluation model by FMEA is exploited with multi-granular linguistic distribution assessments to suit practical case. Best-worst and maximizing derivation methods are adopted to determine subjective and objective combined weights for distinguishing the importance of risk factors. Complex proportional assessment method is used to prioritize failure modes for explicitly and effectively reflecting the risk level of each failure mode. The proposed model is applied in a practical case of an SCWG system used in SST. Results derived from comparative and sensitivity analyses fully demonstrate the reliability and validity of the model.

An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods

In general, analysis of failure modes and their effects requires a group of experts to tackle substantial uncertainties associated with the risk evaluation process. To date, to overcome one or more of the uncertainty-related issues, an increasing number of failure mode and effects analysis (FMEA) models based on multi-criteria decision-making (MCDM) methods have been developed. However, most of the improvements have not cautiously considered the process of assigning importance weights to risk factors and FMEA team members during FMEA. This study aims to enhance the performance of the classic FMEA and to propose an integrated fuzzy MCDM approach for FMEA. First, a fuzzy best-worst method is used to obtain the weights of risk factors. Second, an integrated structure based on fuzzy proximity and fuzzy similarity entropy weights is developed to obtain the weights of FMEA team members with respect to different risk factors. Finally, a fuzzy VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje) approach is employed to obtain the risk priorities of failure modes. The applicability and effectiveness of the proposed approach is validated through an illustrative example concerning risk analysis of a grinding wheel system. The results of sensitivity and comparative analyses show that the proposed approach is valid and can provide valuable and effective information in assisting risk management decision-making.

Failure Mode and Effects Analysis by Using the House of Reliability-Based Rough VIKOR Approach

Failure mode and effects analysis (FMEA) is a widely used reliability analysis tool for identifying and eliminating known or potential failures in system, design, and process. In traditional FMEA, failure modes are evaluated by FMEA team members with respect to three risk factors: severity (S), occurrence (O), and detectability (D), and ranked via their risk priority number (RPN), which is obtained by multiplying the crisp values of S, O, and D. However, traditional RPN has been considerably criticized due to the following shortcomings: not considering the different weights of risk factors; the identical value of RPN for different combinations of S, O, and D; the diversity and uncertainty of evaluation information given by FMEA team members and without considering the dependence among different failure modes. Although significant efforts have been made in FMEA literatures to overcome these shortcomings, there are still some deficiencies. In this paper, a new risk priority model is presented for FMEA by using the house of reliability (HoR)-based rough VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) approach. In the proposed model, the HoR is introduced to identify the dependence among different failure modes and the link between failure modes and the risk factors of O, D and the subcriteria of S. Rough number is introduced to manipulate the subjectivity and vagueness in decision making and VIKOR approach is used to determine the risk priority order of failure modes in a comprehensive way. Finally, an illustrative case in transmission system of a vertical machining center has demonstrated the effectiveness and practicality of the proposed model.

Failure Mode and Effect Analysis Using Cloud Model Theory and PROMETHEE Method

Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members’ weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.

New approach for failure mode and effect analysis using linguistic distribution assessments and TODIM method

As a proactive risk management instrument, failure mode and effect analysis (FMEA) has been broadly utilized to recognize, evaluate and eliminate failure modes of products, processes, systems and services. Nevertheless, the conventional FMEA method suffers from many important deficiencies when used in the real world. First, crisp numbers are adopted to describe the risk of failure modes; but, in many practical situations, it is difficult to obtain exact assessment values due to inherent vagueness in the human judgments. Second, the priority ranking of failure modes is determined based on the risk priority number (RPN), which is questionable and strongly sensitive to the variation of risk factor ratings. Therefore, this paper applies linguistic distribution assessments to represent FMEA team members’ risk evaluation information and employs an improved TODIM (an acronym in Portuguese of interactive and multicriteria decision making) method to determine the risk priority of failure modes. Furthermore, both subjective weights and objective weights of risk factors are taken into account while conducting the risk analysis process. Finally, an empirical case concerning the risk evaluation of a grinding wheel system is presented to demonstrate the practicality and effectiveness of the proposed new FMEA model.