Failure Mode And Effect Analysis Framework Research Articles

Analyzing the risk of the ammonia storage facility using extended FMEA model based on probabilistic linguistic GLDS method with consensus reaching

Due to the energy crisis, ammonia stands out as an efficient hydrogen energy carrier. However, liquid ammonia's nature of toxicity, corrosiveness, volatility, flammability, etc., make its storage highly risky. It is essential to analyze the risk of the ammonia storage facility for its safety improvement. Failure mode and effect analysis (FMEA) has been a popular risk analysis method due to its ease of implementation. However, traditional FMEA is insufficient to model the uncertainty of risk assessment information, enhance the consensus among experts, aggregate experts' risk evaluation information, and derive reliable risk analysis results. To address these limitations, we introduce a hybrid probabilistic linguistic FMEA framework to analyze failure risk in the ammonia storage facility. First, the probabilistic linguistic terms (PLTs) are introduced to express uncertain risk evaluation information of failure modes. Second, a risk acceptability-based consensus-reaching process is introduced to measure experts' consensus on each failure mode's risk and improve their consensus. Then, a consensus degree-based ordered weighted averaging operator is introduced to fuse experts' risk evaluation information, where experts' weight is determined by their consensus degree. Furthermore, the gained and lost dominance score method is improved on the basis of the expected value of the PLTs to prioritize failure modes' risk. Finally, a case study of the risk analysis for the ammonia storage facility is performed to show the efficiency of the extended FMEA. The case study indicated that condenser cooling water pump failure and failure of level transfer are the top two failure modes and have risk scores of 0.126 and 0.091, respectively.

A new approach to failure mode and effect analysis under linguistic Z-number: A case study of CNC tool holders

Failure Mode and Effects Analysis (FMEA), as the most important component of risk management, has been widely used in the engineering field. Although the method is widely used, the traditional FMEA approach does not take into account the issue of decision maker's mental preference, which seriously affects the accuracy of risk ranking results. To improve these shortcomings, we fuse the language set and Z-number to propose the language Z-number to express the decision information. Meanwhile, we propose a new linguistic Z-number weight Muirhead mean (LZNWMM) operator and use it to aggregate the information. Finally, we propose a new FMEA framework, which uses the proposed operator to aggregate information and calculate attribute weights using the fuzzy best-worst method. Using this method applied to a computer numerical control (CNC) tool holder manufacturer, its usability and validity are verified by comparing it with the traditional FMEA method, LZNWA method and LZNBM method.

An Improved Failure Mode and Effect Analysis Model for Automatic Transmission Risk Assessment Considering the Risk Interaction

Failure mode and effect analysis (FMEA) is a powerful tool for reliability management that has been widely applied in a variety of areas. The interaction relationship exists in the whole process of FMEA risk assessment, and has an important impact on it. However, few scholars consider the interaction among experts, risk factors, and failure modes in the FMEA framework. Therefore, we propose an improved FMEA method, which comprehensively considers the interaction between each part. First, the Choquet integral is used to improve the incomplete part of the social trust network, then the importance of experts is determined. Second, the Shapley-LINMAP linear programming method based on cloud model is proposed to calculate the weight of risk factors. Finally, the improved decision-making trial and evaluation laboratory method based on cloud model is used to rank the failure modes. By applying the method proposed in this article to the case of automatic transmission (AMT) of new energy vehicles, the effectiveness and superiority can be verified from the comparison analysis with other methods, which enriches the theoretical research of the FMEA method and improves the reliability of risk assessment of AMT.

A fully data-driven FMEA framework for risk assessment on manufacturing processes using a hybrid approach

The Failure Mode and Effect Analysis (FMEA) is a widely used method that effectively identifies and prioritizes potential risks in a given system or process. However, traditional and modified versions of FMEA are often criticized for their subjective assessments, inadequate risk prioritization methods, and lack of consideration of the importance level of risk factors. To address these issues, this study introduces a data-driven FMEA approach. Specifically, the proposed approach utilizes data-driven risk factors to determine objective rankings of failure modes. This study uses the frequency and stability of failures, time and product loss cost due to failure as objective and data-driven risk factors. These factors enable a more precise description of the influence of risk factors on failure modes. The Modified Criteria Ranking Importance with Intra-criteria Correlation (M-CRITIC) method is employed to assign weights to the identified risk factors, which indicates their level of importance in analysis. Additionally, the recently proposed Alternative by Alternative Comparison (ABAC) method is used to derive the risk priorities of failure modes. The effectiveness and applicability of the developed approach are demonstrated through a case study focused on manufacturing process risk analysis in the food industry. Furthermore, this study contributes to the growing trend toward objective risk calculations for FMEA and highlights the importance of using data-driven models for risk analysis.

A developed gained and lost dominance score method for risk prioritization in FMEA with Fermatean fuzzy information

Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation.

An integrated FMEA framework considering expert reliability for classification and its application in aircraft power supply system

The risk assessment of the integrated drive generator (IDG) is a significant problem in flight safety that directly affects the operation of the aircraft power system However, the current IDG risk assessment methods still have many limitations, leading to risk identification and classification that is not accurate and scientific. Therefore, this paper constructs a failure mode and effect analysis (FMEA) framework for IDG risk identification and classification considering expert reliability in unbalanced hesitant fuzzy linguistic term sets (UHFLTSs) environment. First, since the UHFLTSs can effectively describe uncertain and imprecise linguistic information of decision makers (DMs) and adapt it to practical applications, it is specially chosen to depict the evaluation of DMs. Second, the importance of experts is fully assessed by combining expert reliability and expert weight since the variation in expert reliabilities may have a substantial impact on the final sorting outcomes. In addition, the group best–worst method (GBWM) is combined with the entropy method to determine the risk factor weight. Third, to build up the accuracy and stability of classification, a new integrated TOPSIS-ELECTRE TRI decision framework is proposed to classify failure modes more rationally. The sensitivity analysis of parameters demonstrates the robustness of the proposed method. Meanwhile, the effectiveness and superiority of the method are verified both qualitatively and quantitatively. By comparing with several typical sorting methods, the proposed method has more realistic and stable classification results.

Three-way failure mode and effect analysis approach for reliability management in multigranular unbalanced linguistic contexts

Failure mode and effect analysis (FMEA) is one of the most influential reliability analysis techniques and has been widely used in a variety of fields. Due to differences in knowledge and experiences, the subjective assessment information provided by experts may be multigranular unbalanced linguistic terms. Furthermore, existing FMEA methods built in multi-criteria decision-making paradigm ignore the failure cost of each failure mode under different decision-making actions. Based on three-way decisions with decision-theoretic rough sets, this paper constructs a novel three-way FMEA framework in multi-granular unbalanced linguistic contexts. Firstly, we transform multigranular unbalanced linguistic information into uniform and balanced linguistic information, and propose the 2-tuple probabilistic linguistic term sets to integrate linguistic assessments of all experts. Secondly, an optimization model is designed to determine the weight of risk factors, the TOPSIS method is used to calculate the conditional probability of each failure mode. Thirdly, the concept of failure-loss function matrices has been introduced to derive three-way decision rules of FMEA. Finally, an illustrative reliability management of offshore wind turbine system is used to demonstrate the application and effectiveness of the proposed methodology. The three-way FMEA framework can provide decision makers with targeted reliability management strategies.

An evidence theory-based large group FMEA framework incorporating bounded confidence and its application in supercritical water gasification system

Supercritical water gasification (SCWG) is an advanced technology for sewage sludge treatment, which can effectively remove hazardous substances in it and realize the resource utilization of sludge. Given the harsh operating environment and complex operating process, it is particularly critical for SCWG system to adopt failure mode and effects analysis (FMEA) to ensure its reliability and security. In particular, the risk management process of SCWG system needs to include two considerations: the evaluation process requires the participation of a large number of team members (TMs) with different professional knowledge and operational skills, and the cross-correspondence between the factors within the system cannot be ignored in the analysis process. Thus, an FMEA framework is established to meet practical needs, which can model TMs’ personality traits and reflect the interdependencies between factors. First, assessments in the form of probability linguistic term sets (PLTSs) are converted into mass functions due to the excellence of evidence theory in information aggregation. Second, a bounded confidence-based clustering method is developed to consider TMs’ willingness to interact during clustering. Additionally, evidence conflicts are managed using a new discounting method that captures TMs’ stubbornness. Besides, the cross-correspondence between factors is analyzed by an evidence theory-based DEMATEL method. Further considering TMs’ bounded rationality, regret theory (RT) is used to distinguish and prioritize failure modes (FMs). Finally, a case study of an SCWG system is successfully solved by applying the proposed framework. The effectiveness and superiority are subsequently demonstrated by a series of analyses and discussions.

RISK PRIORITY EVALUATION OF POWER TRANSFORMER PARTS BASED ON HYBRID FMEA FRAMEWORK UNDER HESITANT FUZZY ENVIRONMENT

The power transformer is one of the most critical facilities in the power system, and its running status directly impacts the power system's security. It is essential to research the risk priority evaluation of the power transformer parts. Failure mode and effects analysis (FMEA) is a methodology for analyzing the potential failure modes (FMs) within a system in various industrial devices. This study puts forward a hybrid FMEA framework integrating novel hesitant fuzzy aggregation tools and CRITIC (Criteria Importance Through Inter-criteria Correlation) method. In this framework, the hesitant fuzzy sets (HFSs) are used to depict the uncertainty in risk evaluation. Then, an improved HFWA (hesitant fuzzy weighted averaging) operator is adopted to fuse risk evaluation for FMEA experts. This aggregation manner can consider different lengths of HFSs and the support degrees among the FMEA experts. Next, the novel HFWGA (hesitant fuzzy weighted geometric averaging) operator with CRITIC weights is developed to determine the risk priority of each FM. This method can satisfy the multiplicative characteristic of the RPN (risk priority number) method of the conventional FMEA model and reflect the correlations between risk indicators. Finally, a real example of the risk priority evaluation of power transformer parts is given to show the applicability and feasibility of the proposed hybrid FMEA framework. Comparison and sensitivity studies are also offered to verify the effectiveness of the improved risk assessment approach.

A Calibrated Individual Semantic Based Failure Mode and Effect Analysis and Its Application in Industrial Internet Platform

This article proposes a calibrated individual semantic (CIS)-based failure mode and effect analysis (FMEA) to deal with the risk evaluation of industrial internet platforms (IIP) from four perspectives: network security, data processing capability, equipment performance, and openness. The novelty of the CIS model is based on the deviation between linguistic terms and numerical values to calibrate linguistic scales of decision-makers (DMs). Not only can it handle situations in which different DMs have different understandings of the same term, but it is also suitable for multiple attributes decision-making with uncertainty. In addition, this new FMEA framework considers the consensus-reaching process as a way to eliminate the disagreement among DMs from different departments. Finally, a comparison between the proposed and traditional method is presented to illustrate the advantages of new method.

Application of an adapted FMEA framework for robot-inclusivity of built environments

Mobile robots are deployed in the built environment at increasing rates. However, lack of considerations for a robot-inclusive planning has led to physical spaces that would potentially pose hazards to robots, and contribute to an overall productivity decline for mobile service robots. This research proposes the use of an adapted Failure Mode and Effects Analysis (FMEA) as a structured tool to evaluate a building’s level of robot-inclusivity and safety for service robot deployments. This Robot-Inclusive FMEA (RIFMEA) framework, is used to identify failures in the built environment that compromise the workflow of service robots, assess their effects and causes, and provide recommended actions to alleviate these problems. The method was supported with a case study of deploying telepresence robots in a university campus. The study concluded that common failures were related to poor furniture design, a lack of clearance and hazard indicators, and sub-optimal interior planning.

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning

Nowadays, the availability of data from the manufacturing environment, such as process and operation related data or past maintenance activities enable new possibilities for advanced data analytics like prediction of failure behavior. Possible predictions could consider faults of specific components or even the current product and component properties.The paper presents a data-driven Failure Mode and Effect Analysis (FMEA) methodology by using deep learning models on historical and operational data from the use stage of industrial investment goods. The developed methodology is supposed to support the maintenance planning for industrial investment goods by enhancing transparency and providing decision support.The developed framework is applied to and validated by a case study from the aviation sector. The results show that the accuracy of the fault prediction is around 95 %. By integrating these results into a data-driven FMEA framework, risk and failure occurrence estimations are no longer subjective. Especially the estimation of failure probabilities no longer solely depends on the experience and knowledge from employees.

Diagnosing and routing electronic service quality improvement of academic libraries with the FMEA approach in an intuitionistic fuzzy environment

PurposeIn this way, the aim of this study is to expand and evelop the application of this technique in FMEA to rank failure modes of ESQ of academic libraries in an intuitionistic fuzzy environment. Assessment of electronic service quality (ESQ) of libraries is significantly important according to their major roles. It should be noted that the ESQ has a significant impact on customer satisfaction, which improves organizational performance. Accordingly, low ESQ means waste of organizational resources and poor user satisfaction. So, there is a dire need to reflect reasons inducing failure modes in academic library ESQ. Thus, investigation of failure modes affecting academic library ESQ is highly important. One solution in this area is utilization of the intuitionistic fuzzy (IF) failure mode and effects analysis (FMEA) as one of the widely used methods for prediction and identification of failure modes.Design/methodology/approachThe present study in terms of objective is applied and in terms of the type of method is descriptive-analytical. The research sample included four experts of Yazd academic Libraries (Iran). To collect data, three types of questionnaires were distributed among experts. The purpose of the first questionnaire was to identify and reach an agreement on e-library failure modes. Type II questionnaire was used to determine the importance of identified risk factors and Type III questionnaire was used to prioritize the factors.FindingsResults indicate that the difficulty of using websites, lack of provided information feedback to users and lack of links on the website to users' are the main priorities for improving ESQ in the studied academic libraries.Originality/valueIn this approach, the Intuitionistic fuzzy Elimination Et Choix Traduisant la REalité and technique for order of preference by similarity to ideal solution method were used to rank failure modes in academic library ESQ within the FMEA framework.]

A novel approach to emergency risk assessment using FMEA with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment

PurposeThe application of the traditional failure mode and effects analysis (FMEA) technique has been widely questioned in evaluation information, risk factor weights and robustness of results. This paper develops a novel FMEA framework with extended MULTIMOORA method under interval-valued Pythagorean fuzzy environment to solve these problems.Design/methodology/approachThis paper introduces innovatively interval-value Pythagorean fuzzy weighted averaging (IVPFWA) operator, Tchebycheff metric distance and interval-value Pythagorean fuzzy weighted geometric (IVPFWG) operator into the MULTIMOORA submethods to obtain the risk ranking order for emergencies. Finally, an illustrative case is provided to demonstrate the practicality and feasibility of the novel fuzzy FMEA framework.FindingsThe feasibility and validity of the proposed method are verified by comparing with the existing methods. The calculation results indicate that the proposed method is more consistent with the actual situation of project and has more reference value.Practical implicationsThe research results can provide supporting information for risk management decisions and offer decision-making basis for formulation of the follow-up emergency control and disposal scheme, which has certain guiding significance for the practical popularization and application of risk management strategies in the infrastructure projects.Originality/valueA novel approach using FMEA with extended MULTIMOORA method is developed under IVPF environment, which considers weights of risk factors and experts. The method proposed has significantly improved the integrity of information in expert evaluation and the robustness of results.

The consistency analysis of failure mode and effect analysis (FMEA) in information technology risk assessment

FMEA is as a method for assessing IT risks. This research aimed to examine the consistency of both traditional FMEA and improved FMEA in IT risk assessment. Improved FMEA is the result of a synthesis framework to minimize consistency in traditional FMEA. Two sets of action research cycles (plan, act, observe, reflect) were applied in this research. Action Research 1 was used to examine and prove the consistency of traditional FMEA. On the other hand, Action Research 2 was applied to examine the consistency of improved FMEA. Tests were carried out by two different teams in the same case study. The consistency was observed in the gap of the RPN results in both teams, and the differences result in both action research cycles. Action Research 1 proved that traditional FMEA was not consistent. The gap in the amount of risk at a very high level was four risks. However, Action research 2 had the same amount of risk at a very high level. Based on the correlation test, the consistency of action research 1 was 0.848 (very large correlation), and the action research 2 was 0.937 (near-perfect correlation). The consistency of improved FMEA proved to be more consistent than traditional FMEA. The limitation of this study was memory issues because both action research cycles were carried out by the same team and with similar case studies. Further research is expected to compare traditional FMEA and improved FMEA in different case studies. The theoretical contribution was the improved FMEA synthesis based on limitations of traditional FMEA. The FMEA team may use Improved FMEA Framework.

Failure Mode and Effect Analysis for Machine Tool Risk Analysis Using Extended Gained and Lost Dominance Score Method

Enhancing the reliability of machine tools and preventing the potential failures in the manufacturing process is one of the most important tasks for the development of industry. The failure mode and effects analysis (FMEA) is the well-known and widely utilized approach to identify and evaluate potential failures for preventing risk in various enterprises. It is a group-oriented technique ordinarily conducted by a group of experts from related fields. Obviously, an effective approach should be developed, which is used to integrate risk evaluation information from multiexperts by considering group and individual risk attitudes. This article proposes a hybrid FMEA framework for addressing machine tool risk analysis problem by integrating cloud model, Choquet integral, and gained and lost dominance score (GLDS) method. In this framework, an improved Shapley cloud-Choquet weighting averaging operator is defined to fuse random and uncertain risk information by considering the correlations among experts. An extended GLDS method with developed Choquet integral based on distance measure of normal clouds is presented to prioritize the risk priority of each failures, in which the group and individual risk attitude and risk interactions are considered simultaneously. Finally, a real risk analysis of machine tool in a machine tool industry is introduced to illustrate the application and feasibility of the proposed approach, and comparison and sensitivity studies are also conducted to validate the effectiveness of the hybrid framework.

Failure Mode and Effect Analysis (FMEA) with Extended MULTIMOORA Method Based on Interval-Valued Intuitionistic Fuzzy Set: Application in Operational Risk Evaluation for Infrastructure

Failure Mode and Effect Analysis (FMEA) is a useful risk assessment tool used to identify, evaluate, and eliminate potential failure modes in numerous fields to improve security and reliability. Risk evaluation is a crucial step in FMEA and the Risk Priority Number (RPN) is a classical method for risk evaluation. However, the traditional RPN method has deficiencies in evaluation information, risk factor weights, robustness of results, etc. To overcome these shortcomings, this paper aims to develop a new risk evaluation in FMEA method. First, this paper converts linguistic evaluation information into corresponding interval-valued intuitionistic fuzzy numbers (IVIFNs) to effectively address the uncertainty and vagueness of the information. Next, different priorities are assigned to experts using the interval-valued intuitionistic fuzzy priority weight average (IVIFPWA) operator to solve the problem of expert weight. Then, the weights of risk factors are subjectively and objectively determined using the expert evaluation method and the deviation maximization model method. Finally, the paper innovatively introduces the interval-valued intuitionistic fuzzy weighted averaging (IVIFWA) operator, Tchebycheff Metric distance, and the interval-valued intuitionistic fuzzy weighted geometric (IVIFWG) operator into the ratio system, the reference point method, and the full multiplication form of MULTIMOORA sub-methods to optimize the information aggregation process of FMEA. The extended IVIF-MULTIMOORA method is proposed to obtain the risk ranking order of failure modes, which will help in obtaining more reasonable and practical results and in improving the robustness of results. The case of the Middle Route of the South-to-North Water Diversion Project’s operation risk is used to demonstrate the application and effectiveness of the proposed FMEA framework.

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.

Integrating a consensus-reaching mechanism with bounded confidences into failure mode and effect analysis under incomplete context

Failure mode and effect analysis (FMEA) is one of the most powerful pre-accident risk management techniques and has been extensively used in various fields. Nevertheless, consensus issues and incomplete assessment information are rarely investigated in the current FMEA method. This paper integrates a consensus-reaching mechanism into the FMEA framework and adopts incomplete linguistic distribution assessments to represent risk assessment information. In the constructed FMEA framework, a deviation minimum-based optimization model is presented to manage incomplete assessment information, and this model aims to minimize the opinion deviation among FMEA members. Then, a consensus-reaching model with a novel feedback adjustment mechanism is proposed to assist FMEA members to gain a consensus. The novel feedback adjustment mechanism is based on a natural assumption that individuals have bounded confidences when updating opinions and that they only take into account the adjustment suggestions that are similar to their own. A two-stage optimization model with bounded confidences is built to support the novel feedback adjustment mechanism. In the first stage, a consensus maximum-based optimization model is proposed to judge whether the consensus among the FMEA members can be achieved under the specific bounded confidences. If it can be achieved, then in the second stage, an adjustment minimum-based optimization model with bounded confidences is designed to generate feedback adjustment suggestions; otherwise, the feedback adjustment suggestions generated from the first stage are used for supporting modifying opinions. Two case studies show that the proposal has a good application value, and the simulation experiment shows that our proposal can effectively improve the consensus success ratio and accelerate the speed to achieve a consensus compared with the existing baseline approach.