Failure Mode And Effects Analysis Team Research Articles

Failure Mode and Effects Analysis Prior to the Introduction of AI Generated GTVs for Brain Metastases in the Clinical Workflow

AI autosegmentation of organs-at-risk (OARs) is common practice at many radiotherapy clinics. Despite the abundance of gross tumor volume (GTV) autosegmentation algorithms, adoption in clinical care has been slow due to the high risk associated with errors in GTV delineation. Here we present a failure mode and effects analysis (FMEA) to evaluate the risk associated with introducing AI derived GTVs in patients treated with stereotactic radiosurgery (SRS). An AI GTV autosegmentation algorithm for brain metastases was developed in-house based on a V-Net 3D CNN. Registered CT and MR images and a contour of the brain are input into the software and all identified lesions are returned in a DICOM-RT structure set. Following algorithm evaluation, a workflow was developed to enable AI GTV autosegmentation to be introduced clinically for every SRS patient. The following steps were added to existing procedures: 1) workflow to send CT/MR and brain structure to external server, 2) autosegmentation run on the server, 3) AI GTV structures with a standard nomenclature added to existing OAR structure set, and 4) MD review, editing, and approval of AI GTVs. After successfully completing the physics evaluation testing of the new process, we formed a team of 10 faculty and staff including physicists, residents, physicians, and planners to perform the FMEA prior to clinical implementation. The team met to map the process, identify potential failure modes, and score their frequency of occurrence, severity, and detectability. A 3-point scale (1, 3, or 5) was used to simplify the scoring process. Occurrence was defined as rare, sometimes, or often; severity as low, medium, or high; and detectability as obvious, possible, or challenging. The risk probability numbers (RPNs) were calculated and the steps in the process with the highest RPNs were flagged for further discussion. The FMEA team completed their process map and analysis primarily in 4 meetings. The process map began with acquisition of the patients CT simulation scan and ended with physician approval of final volumes for treatment planning. We identified 17 process steps and 72 possible failure modes, of which 26 were associated with the new workflow. Eighteen failure modes had an RPN greater than 30 (highest risk score in at least one category) and were flagged to assess mitigation strategies. Five were unique to the new AI GTV workflow and mitigation strategies will be designed prior to clinical use. Those involved risks related to inaccurate AI GTV contours, false positives, and an incomplete review stemming from over-reliance by team members on AI. AI is increasingly being employed at every step of radiotherapy to automate and streamline processes. The FMEA analysis resulted in the identification of the riskiest parts of using AI GTV autosegmentation. This can be an effective tool in the development of checks to ensure that GTV autosegmentation methods can be safely introduced in support of patient care.

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.

Assessment of barriers to IoT-enabled circular economy using an extended decision- making-based FMEA model under uncertain environment

The Circular Economy (CE) is an industrial system that is repaired or revived with intent and design. The concept of reuse in the CE has also significantly increased the operational capabilities and intricacy of the network. In the same way, The IoT is an emerging technology designed to control challenges of the concept of circular economy. This article aims to identify and rank barriers to IoT-enabled CE in the concept of Industry 4.0. In this research, new integrated approach based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Multi-Objective Optimization on the basis of the Ratio Analysis (MOORA) in the field of Pythagorean fuzzy sets (PFS) has been produced to cover some disadvantages of Failure Mode and Effects Analysis (FMEA) method include lack of complete prioritization and inability to decide the different significance of risk factors in an uncertain environment. In the proposed method, the first Risk Priority Number (RPN) is ascertained by the FMEA team. Then risk factors are weighed using the SWARA method and barriers are prioritized using the MOORA method in PFS. The computational approach of the suggested method is also implemented through a case study of the problem of ranking barriers to IoT-enabled CE, which certifies the efficiency and validity of the presented method. In addition, this sensitivity analysis study was carried out to exhibit permanence of the proposed method and to make a comparison for the efficacy analysis of achieved results and the results show that produced method is compared with other developed methods, it is more efficient and usable in the field of PFS.

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.

Application of Monte Carlo Simulation to Study the Probability of Confidence Level under the PFMEA’s Action Priority

Failure mode and effects analysis (FMEA) is the most commonly used risk evaluation tool in industry and academia. After four revisions, the US Automotive Industry Action Groups (AIAG) and German Association of the Automotive Industry (VDA) issued the latest FMEA manual, called AIAG and VDA FMEA Handbook Edition 1, in June 2019. Risk priority number (RPN) in the old-edition FMEA is replaced with action priority (AP), where the numerical evaluation of severity (S), occurrence (O), and detection (D) are referred to in the AP form for judging high (H), medium (M), and low (L) priority in order to ensure appropriate actions for improving prevention or detection control. When evaluating design (D) or process (P) in FMEA, the FMEA team has to refer to the evaluation criteria for S, O, and D, so as to reduce the difference in the evaluation reference and fairness. Since the criteria evaluation form is the qualitative rating standard with semantic judgment, evaluation errors are likely to occur when the team judges S, O, and D. The FMEA cases in this study are preceded by the confidence level (CL) of the S, O, and D evaluation standards and the setting of a confidence interval (CI) for the actual evaluation events. With discrete nonuniform distribution as the simulation setting, Monte Carlo simulation is applied several times to evaluate the probability before and after the evaluation, which is compared with the AP form to confirm the probability values of high, medium, and low priority. It provides reference for the FMEA cross-functional team, improving the originally non-AP events. Finally, the AP calculated in the simulation is compared and analyzed with the RPN sequence to verify the judgment of better actions with AP.

Modified FMEA hazard identification for cross-country petroleum pipeline using Fuzzy Rule Base and approximate reasoning

The pipeline industry's existing and new safety challenges require flexible and powerful techniques for performing a risk-based analysis of cross-country petroleum product pipeline systems. One of the traditional tools for the prediction of pipeline failure is the Failure Mode and Effects Analysis (FMEA) technique due to its ease of application. However, many limitations have been identified in its application especially for cross-country pipelines in developing countries. Firstly, failure data is often either unavailable or unreliable, therefore identification of the risk priority numbers for the three failure factors (i.e. probability of failure, severity and detection) relies on experts' elicitation. Secondly, domain experts often provide diverse opinions and knowledge, which could produce different assessment rankings and it is often difficult to harmonise due to the multidisciplinary nature of the FMEA team. Thirdly, there is a lack of a systematic way of accounting for the relative importance of individual failure factors, which carries the risk of the assessment results not representing the true risk picture of the assessed system. Consequently, this paper proposes a new approach, called the modified FMEA, by integrating the noted benefits of hybrid FMEA with Fuzzy Rule Base (FRB) and FMEA with Grey Relations Theory (GRT) in order to overcome the identified drawbacks. The study utilises both the fuzzy and the grey theory to include experts' diverse opinions and to assign a relative weighting to each assessment factor in the risk assessment. The results of the risk assessment are then used to determine the risk priority and rank the failure modes under different types of conditions. A case study of Nigeria's petroleum product pipeline system 2 B is conducted to examine the applicability and validity of the new approach. The results show the practical application of the methodology in this new domain. The new approach offers a more effective method for identifying product pipeline system hazards and risk analysis in geographies with limited or unreliable data. The application of this new methodology in the oil and gas cross-country pipeline domain will aid decision making under uncertainty for pipeline inspection and maintenance.

Risk Reduction of Healthcare Workers’ Exposure to COVID-19 using Failure Mode and Effect Analysis

Background: The exposure to COVID-19 by staff has a major impact on healthcare system. Objective: identify potential failures related to the exposure of HCWs to COVID-19, evaluate the potential causes and effects, and the actions to mitigate the risk of exposure. Methods: Members of the infection control department, quality department, nursing department, and medical administration were selected as team members to conduct the Failure Mode and Effect Analysis (FMEA). The identification of potential failure modes, causes and effects was conducted in consecutive meetings. Accordingly, were identified actions to reduce the staff exposure to COVID-19. Results: The description of the complex process was conducted including the potential in-hospital and hospital-community interaction for transmission of infection to staff. In eight areas were identified 20 potential failure modes: Hand hygiene, personal protective equipment, detection of sick staff, exposure in common areas, hiring new staff, staff living conditions, and staff knowledge, skill, and perceptions about all other infection control practices. The highest ranked priorities were identified including improper PPE use (556 points), late detection of sick staff (520 points), and poor compliance with infection control practices in common areas (436 points) respectively. The mitigation strategies focused on a wide range of actions to improve the staff education, improve practices and procedures, monitor practices and feedback to staff in a continuous quality improvement cycle. Conclusion: Data presented provides a comprehensive evaluation of the risks and mitigation measures to prevent the staff exposure to COVID-19 conducted in a high-risk environment by a qualified FMEA team. Keywords: failure modes and effect analysis; quality management; risk mitigation; staff exposure; COVID-19; Qatar;

Fuzzy Based Failure Mode and Effect Analysis Towards to Risks of Autonomous Maintenance Activities: As a TPM Implementation

Manufacturing companies attach importance to Total Productive Maintenance (TPM) applications to extend equipment life and increase efficiency. The recommended applications with TPM are carried out under the pillar activities. Autonomous Maintenance (AM) pillar manage the assignments of the operators to undertake routine maintenance work on machine maintenance. Cleaning, lubrication and control activities are one of the steps of AM pillar and operators need to do them daily. However, there are a number of occupational health and safety risks that operators may face during these activities. Safety (S) pillar which serve as another TPM pillar, deal with occupational accidents and possible situations during TPM applications. AM and S pillars work together to assess the risks that may occur during AM applications. Failure Mode and Effect Analysis (FMEA) is also frequently utilized for risk assessment, but this is criticized in terms of the difficulty in reaching the common point of decision-makers’ risk assessment and equal weighting of risk factors. Therefore, it is appropriate to support with the fuzzy logic approach. In this study, the entropy-weighted fuzzy based FMEA method was utilized for identify and prioritize potential risks that may be encountered during the AM activities. Potential risks were revealed and evaluated with the FMEA team. Eleven potential risks were identified in the study. The risk factors of the assessment were weighted by the entropy method. The risk of hand injuring during cleaning the oil below material cutting saw has the highest risk priority.

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.

An Extended FMEA Model Based on Cumulative Prospect Theory and Type-2 Intuitionistic Fuzzy VIKOR for the Railway Train Risk Prioritization.

This paper aims toward the improvement of the limitations of traditional failure mode and effect analysis (FMEA) and examines the crucial failure modes and components for railway train operation. In order to overcome the drawbacks of current FMEA, this paper proposes a novel risk prioritization method based on cumulative prospect theory and type-2 intuitionistic fuzzy VIKOR approach. Type-2 intuitionistic VIKOR handles the combination of the risk factors with their entropy weight. Triangular fuzzy number intuitionistic fuzzy numbers (TFNIFNs) applied as type-2 intuitionistic fuzzy numbers (Type-2 IFNs) are adopted to depict the uncertainty in the risk analysis. Then, cumulative prospect theory is employed to deal with the FMEA team member’s risk sensitiveness and decision-making psychological behavior. Finally, a numerical example of the railway train bogie system is selected to illustrate the application and feasibility of the proposed extended FMEA model in this paper, and a comparison study is also performed to validate the practicability and effectiveness of the novel FMEA model. On this basis, this study can provide guidance for the risk prioritization of railway trains and indicate a direction for further research of risk management of rail traffic.

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.

MRI safety management in patients with cardiac implantable electronic devices: Utilizing failure mode and effects analysis for risk optimization.

Cardiac implantable electronic devices (CIEDs) are increasing in prevalence. Exposing patients with CIEDs to magnetic resonance imaging (MRI) can lead to adverse outcomes. This has led certain radiology departments to not accept MRI referrals related to patients with CIEDs. Patients with MR-conditional CIEDs can be safely scanned under specific conditions. Our institution has accepted such referrals since 2014. The aim of this study was to systematically identify and reduce risk in our CIED-MRI protocol using failure mode and effects analysis (FMEA). A multidisciplinary FMEA team was assembled and included senior stakeholders from the CIED-MRI protocol. A process map was constructed followed by risk analysis and scoring. Targeted interventions were formulated and implemented; high-risk failure modes were prioritized. A new process map and protocol were drafted and repeat risk analysis was performed. Monitoring and re-evaluation of the CIED-MRI pathway were instigated at departmental quality assurance (QA) meetings. Interventions included direct CIED characterization using wireless technology pre-MRI, CIED programming and reprogramming in the MRI suite before and immediately after MRI reducing device downtime and continuous patient monitoring during MRI by a cardiac physiologist. The cumulative risk priority number (RPN) decreased from 1190 pre-FMEA to 492 post-FMEA. Despite the risk of exposing CIEDs to the MR environment, patients with MR-conditional CIEDs can be safely scanned with an appropriate multidisciplinary support. We found FMEA an indispensable tool in identifying and minimizing risk with no adverse events recorded since FMEA recommendations were implemented.

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.

Integrated Decision-Making Approach Based on SWARA and GRA Methods for the Prioritization of Failures in Solar Panel Systems under Z-Information

Encountering a problem or error in the final stages of providing products or services increases costs and delays scheduling. The key task is to ensure quality and reliability in the early stages of the production process and prevent errors from occurring from the beginning. Failure mode and effect analysis (FMEA) is one of the tools for identifying potential problems and their impact on products and services. The conventional FMEA technique has been criticized extensively due to its disadvantages. In this study, the concepts of uncertainty and reliability are considered simultaneously. The processes of weighting risk factors, prioritizing failures by using the stepwise weight assessment ratio analysis (SWARA)–gray relational analysis (GRA) integrated method based on Ζ-number theory and complete prioritization of failures are implemented. Crucial management indices, such as cost and time, are considered in addition to severity, occurrence and detection factors along with assigning symmetric form of the weights to them. This, in turn, increases the interpretability of results and reduces the decision-maker’s subjectivity in risk prioritization. The developed model is implemented on solar panel data with 19 failure modes determined by the FMEA team. Results show that the proposed approach provides a more complete and realistic prioritization of failures than conventional FMEA and fuzzy GRA methods do.

Comparison and Verification of Reliability Assessment Techniques for Fuel Cell-Based Hybrid Power System for Ships

In order to secure the safe operation of the ship, it is crucial to closely examine the suitability from the design stage of the ship, and to set up a preliminary review and countermeasures for failures and defects that may occur during the construction process. In shipyards, the failure mode and effects analysis (FMEA) evaluation method using risk priority number (RPN) is used in the shipbuilding process. In the case of the conventional RPN method, evaluation items and criteria are ambiguous, and subjective factors such as evaluator’s experience and understanding of the system operate a lot on the same contents, resulting in differences in evaluation results. Therefore, this study aims to evaluate the safety and reliability for ship application of the reliability-enhanced fuel cell-based hybrid power system by applying the re-established FMEA technique. Experts formed an FMEA team to redefine reliable assessment criteria for the RPN assessment factors severity (S), occurrence (O), and detection (D). Analyze potential failures of each function of the molten-carbonate fuel cell (MCFC) system, battery system, and diesel engine components of the fuel cell-based hybrid power system set as evaluation targets to redefine the evaluation criteria, and the evaluation criteria were derived by identifying the effects of potential failures. In order to confirm the reliability of the derived criteria, the reliability of individual evaluation items was verified by using the significance probability used in statistics and the coincidence coefficient of Kendall. The evaluation was conducted to the external evaluators using the reestablished evaluation criteria. As a result of analyzing the correspondence according to the results of the evaluation items, the severity was 0.906, the incidence 0.844, and the detection degree 0.861. Improved agreement was obtained, which is a significant result to confirm the reliability of the reestablished evaluation 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.

A Novel Failure Mode and Effects Analysis Model Using Triangular Distribution-Based Basic Probability Assignment in the Evidence Theory

Failure mode and effects analysis (FMEA) is a typical risk assessment and prevention technology, in which different experts provide different assessments on the target system to identify the risk grades of its components. Sometimes, the assessments contain conflict information. How to manage and fuse the conflict assessment information is an open issue. We propose a triangular distribution-based basic probability assignment (TDBPA) method to model and fuse the conflict risk level coming from different experts' assessments in the frame of Dempster-Shafer evidence theory. First, the subjective assessments of risk analysis from domain experts are modeled with belief structure in Dempster-Shafer evidence theory. Then, the assessments are transformed as the TDBPA function. Thirdly, the conflict risk assessments from the FMEA team for failure analysis can be fused with Dempster rule of combination. After that, the modified risk priority number (RPN) model based on fused assessment can be calculated for ranking of failure modes. Finally, recommended actions should be taken for prevention of potential risk items. We verify the rationality and efficiency of the proposed method with a case study in the blades of an aircraft turbine. In short, the presented FMEA methodology procedure in this paper is well organized so that we can apply it in a more simple and understandable way. Utilizing the character of triangular distribution, taking adjacent values into account, TDBPA method can smooth the conflict assessment for information fusion. In addition, the shortcoming of repeating values in classical RPN is eliminated in the proposed method, which improves the ability for risk assessment of FMEA.

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.