Failure Mode And Effects Analysis Model Research Articles

ANFIS method to enhance FMEA water operation model of Indonesia drinking water distribution system

Many problems are found in water treatment and distribution in water operations. Those problems range from low to critical risk. All critical risks must be addressed immediately. The Failure Mode and Effects Analysis (FMEA) prioritizes problems based on Occurrence, Severity, and Detection values to identify critical risks. However, this method is also having problems. With the same risk priority number (RPN) calculation, FMEA would be a ranking problem with the same RPN value; hence, we have a priority problem that is not critical but based on the highest value. To solve this problem, we propose additional methods, such as the ANFIS, to give weight based on risk level classification. From the results of data processing carried out by the ANFIS method, it is proven that it can perform re-ranking, for example, in the L2, R5, S8, and U3 code, which has an FMEA RPN value of 12. However, in FMEA-ANFIS, the RPN value becomes L2 2.05, R5 1.52, S8 1.32, and U3 2.52. Furthermore, with these results, it can be concluded that the ANFIS method can enhance the FMEA model in water operations.

A novel failure mode and effect analysis model based on extended interval-valued q-rung orthopair fuzzy approach for risk analysis

The failure mode and effect analysis (FMEA) is an essential technique for risk assessment that can not only determine the potential failure modes, causes of failure and effects, but also expunge the potential failure modes in a system. Imprecise evaluation outcomes may be achieved due to the inherent imperfections caused by the traditional FMEA approach. Accordingly, a hybrid FMEA model based upon the interval-valued q-rung orthopair fuzzy (IVq-ROF) set and extended weighted aggregated sum product assessment (WASPAS) method is propounded to provide decision support for risk assessment. The main dedications of this investigation are as below. First, an innovative IVq-ROF score function is presented to compare the IVq-ROF numbers and the related properties of the proposed function are also discussed. Second, the IVq-ROF Aczel–Alsina operation laws are investigated and then some novel IVq-ROF Hamy mean operators are propounded on based on the advanced operations. Third, the IVq-ROF logarithmic percentage change-driven objective weighting (LOPCOW) method is advanced based on the presented score function to determine the objective importance of the risk criteria and the fuzzy full consistency method (FUCOM) is utilized to ascertain the subjective importance of the risk criteria. Fourth, a novel FMEA model based on extended WASPAS method is presented to ascertain the prioritization of failure modes. Lastly, an empirical about risk assessment in the shipbuilding process is implemented to verify the applicability and feasibility of the designed FMEA model. The comparison analysis and sensibility study are executed to demonstrate the significant merits and stability of the developed model. The best advantage of the presented model is that it not only obtains a consistent outcome and takes into account the interrelations among the risk criteria, but also addresses more problems where the weights of the criteria and experts are not determined in advance.

UTILISING FAILURE MODE AND EFFECT ANALYSIS AND THE KANO MODEL FOR INDUCTION STOVE PERFORMANCE IMPROVEMENT DURING INDONESIA’S ENERGY CONVERSION PROGRAM

In 2022, the Indonesian government, through the Indonesia State Electricity Company (PT. PLN), launched a program to shift from gas to electric induction stoves. This study analyzed induction stove failures, using data from 241 participants via WhatsApp Group (WAG) feedback. This research was combining Failure Mode and Effect Analysis (FMEA) and the KANO model to analyzing those failures and proposing the improvement. The failures that be prioritized for improvement were the monitor screen was off, the electricity was suddenly off, the stove went off when using the boiling function and the installation was inconvenient. Improvements were proposed to the program team, the stove producers and the community. This research finding offers insight for researchers and practitioners, namely enriching FMEA and KANO model integration for electric household appliances. These research findings also provide information on the use of induction stoves by the community for product improvements in subsequent programs.

Research on Sustainable Supplier Evaluation Index System in Architectural Design Industry

Sustainable development has been popularized and emphasized in many industries. Great attention has been paid to the sustainability performance of the whole supply chain, with evaluating suppliers’ sustainability being particularly critical. However, research on sustainable development in the consulting service industry remains to be discovered, and few academic studies have analyzed the supplier selection indices derived from this industry. This paper conducted a case study in a design institute using the Delphi method to investigate the concept and characteristics of sustainable suppliers. Keywords derived from surveys and literature were collected, sorted, and established into a comprehensive hierarchy index system for sustainability evaluation. Finally, based on the improved failure mode and effects analysis (FMEA) and analytic hierarchy process (AHP), the failure risk of the indices was analyzed with weighted risk factors to obtain the high-risk key evaluation indices, which provided a simplified scheme for enterprises to streamline the evaluation process on sustainable suppliers. The results show the reliability of the improved FMEA model by differentiating the risk value and ranking. Suggestions for sustainable improvement are provided.

Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process. The FMEA approach, however, requires domain experts to manually analyse the FMEA model to derive risk-reducing actions that should be applied. In this paper, we provide a formal framework to allow for automatic planning and acting in FMEA models. More specifically, we cast the FMEA model into a Markov decision process which can then be solved by existing solvers. We show that the FMEA approach can not only be used to support medical experts during the modelling process but also to automatically derive optimal therapies for the treatment of patients.

Reliability analysis of intelligent manufacturing systems based on improved FMEA combined with machine learning

Along with the booming of intelligent manufacturing, the reliability management of intelligent manufacturing systems appears to be becoming more significant. Failure mode and effects analysis (FMEA) is a prospective reliability management instrument extensively utilized to manage failure modes of systems, products, processes, and services in various industries. However, the conventional FMEA method has been criticized for its inherent limitations. Machine learning can handle large amounts of data and has merits in reliability analysis and prediction, which can help in failure mode classification and risk management under limited resources. Therefore, this paper devises a method for complex systems based on an improved FMEA model combined with machine learning and applies it to the reliability management of intelligent manufacturing systems. First, the structured network of failure modes is constructed based on the knowledge graph for intelligent manufacturing systems. Then, the grey relation analysis (GRA) is applied to determine the risk prioritization of failure modes. Hereafter, the k-means algorithm in unsupervised machine learning is employed to cluster failure modes into priority classes. Finally, a case study and further comparative analysis are implemented. The results demonstrate that failure modes in system security, production quality, and information integration are high-risk and require more resources for prevention. In addition, recommendations for risk prevention and monitoring of intelligent manufacturing systems were given based on the clustering results. In comparison to the conventional FMEA method, the proposed method can more precisely capture the coupling relationship between the failure modes compared with. This research provides significant support for the reliability and risk management of complex systems such as intelligent manufacturing systems.

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.

Failure mode and effect analysis using VIKOR method based on interval-valued linguistic Z-numbers

Failure mode and effect analysis (FMEA) is an effective quality management tool used to improve product quality and reliability. However, with the application of FMEA, its shortcomings are exposed regarding risk assessment, weight determination, and failure mode risk prioritization. This paper proposes a new FMEA model using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method based on the Interval-valued linguistic Z-numbers (IVLZNs). Specifically, IVLZNs and the Interval-valued linguistic Z-numbers weighted arithmetic averaging (IVLZNWAA) operator are used to evaluate and aggregate risk information of failure modes; the maximum deviation method is used to determine the weight of risk factors; the IVLZNs-VIKOR method is used to determine the risk priority of failure modes. Then, a numerical example is given to verify the effectiveness of the proposed model. Finally, a comparative analysis is made to demonstrate the feasibility and rationality of the proposed method.

Failure mode and effect analysis with ORESTE method under large group probabilistic free double hierarchy hesitant linguistic environment

Failure Mode and Effect Analysis (FMEA) is a prospective and systematic analytical tool widely used to improve the reliability and safety of various industries. However, the existing FMEA methods have received criticism for their inherent deficiencies that limit their flexibility and effectiveness. To address these issues, we propose a novel FMEA model that combines Probabilistic Free Double Hierarchy Hesitant Linguistic Term Set (PFDHHLTS), Extended Grey Relation Analysis (EGRA), and ORESTE to determine the priorities of Failure Modes (FMs). Firstly, the PFDHHLTSs are proposed to represent experts’ assessments, improving the flexibility and accuracy of experts’ expressions. Secondly, the PFDHHLTS-EGRA is proposed to determine the evaluation matrix and the weights of risk factors in a large group environment. Furthermore, the ORESTE method is extended to the PFDHHLTS environment. Lastly, we verify the effectiveness of our method through a case study on the aluminum electrolysis process. The results demonstrate the effectiveness and flexibility of our method in expressing experts’ assessments and obtaining more accurate and reliable risk priorities.

Reactor reliability modeling and reliable life analysis method for multi-state space reactor systems based on DBN and interval estimation

Space nuclear reactor power (space reactor) is of the highest energy density and the most promising long-term self-sustaining and high-power level space power source. Achieving high reliability during the years-long maintenance-free service process is one of the bottlenecks that constrain the application of space reactors, which brings a challenging task on the quantitative evaluation of reliability indicators (life and reliability) of space reactors during the design phase. On the one hand, traditional binary reliability theory cannot model the system functional structure accurately, due to diverse failure modes and complex propagation paths; on the other hand, the lack of reliability data introduces large epistemic uncertainty, resulting from high costs and time-consuming ground reliability tests. To tackle these problems, this paper takes the Stirling integrated space reactor (ACMIR) as a specific research object and proposes a general reactor reliability modeling and reliable life analysis method for space reactors with large prior epistemic uncertainty which is robust to manipulation and can obtain system reliability indicators with small confidence intervals for engineering decisions. First, based on experts’ experience, the functional logic relationship between components and system is sorted out by the failure mode and effect analysis (FMEA) and the prior failure rates of components are evaluated according to the failure grade of the industrial standard. Then, the FMEA model is mapped to a discrete-time Bayesian network (DBN) to model the system reliable life, based on which system reliability indicators with low epistemic uncertainty are analyzed by interval estimation. Finally, the practical and engineering value of the proposed method is demonstrated with the example of ACMIR in two application cases.

Critical risk factors of public building green retrofit projects- an empirical study in Chongqing, China

ABSTRACT In order to discuss the critical risk factors of green retrofit project in existing public buildings, this study identifies the risk factors from the whole life cycle firstly, and the experts are invited to assess the probability of the occurrence, the degree of the severity, and the follow-up effect of the risk occurrence, respectively. In addition, a Choquet integral-based FMEA (Failure mode and effect analysis) model is introduced to assess and analyze the risks in which the descriptive language results are transformed into triangle fuzzy number to analyze the risk assessment results quantitatively, and the relative preference relations are used to rank the risk factors, empirical study is conducted focusing on Chongqing. Based on the proposed risk assessment model, the top 10 high-risk factors in green retrofit projects of public buildings are extracted, the possible potential causes are analyzed, and the corresponding risk response measures are proposed, which provide references for the improvement of risk management in urban renewal.

Failure mode and effects analysis using an improved pignistic probability transformation function and grey relational projection method

Failure mode and effects analysis (FMEA) is an important risk analysis tool that has been widely used in diverse areas to manage risk factors. However, how to manage the uncertainty in FMEA assessments is still an open issue. In this paper, a novel FMEA model based on the improved pignistic probability transformation function in Dempster–Shafer evidence theory (DST) and grey relational projection method (GRPM) is proposed to improve the accuracy and reliability in risk analysis with FMEA. The basic probability assignment (BPA) function in DST is used to model the assessments of experts with respect to each risk factor. Dempster’s rule of combination is adopted for fusion of assessment information from different experts. The improved pignistic probability function is proposed and used to transform the fusion result of BPA into probability function for getting more accurate decision-making result in risk analysis with FMEA. GRPM is adopted to determine the risk priority order of all the failure modes to overcome the shortcoming in traditional risk priority number in FMEA. Applications in aircraft turbine rotor blades and steel production process are presented to show the rationality and generality of the proposed method.

A novel failure mode and effect analysis model using personalized linguistic evaluations and the rule-based Bayesian network

Failure mode and effect analysis (FMEA) is a useful reliability-management instrument and has provided an effective risk assessment tool for engineering applications. With the advancement of technology, the engineering field has encountered an increasing number of failure modes. However, there is a lack of literature focused on evaluating large-scale failure modes. Additionally, the conversion of expert evaluations into numerical forms is primarily conducted by researchers, resulting in potential misunderstandings and information loss. Moreover, most FMEA methods rely only on the risk rankings, lacking further references to provide management recommendations. To address these research gaps, we propose a novel FMEA model. First, we employ a staged evaluation approach to accurately assess a large number of failure modes, which minimizes decision errors caused by evaluator fatigue. Second, to reduce the misinterpretation of expert evaluations and information loss, we improve the personalized individual semantics method to increase expert involvement, and introduce the inverse distance weighting method to minimize information loss. Third, we utilize the rule-based Bayesian network method to provide additional risk indicators. In addition to risk rankings, the output risk distribution offers valuable insights for engineering management. Finally, we apply the proposed model to a risk assessment project, providing suggestions on the safe operation of the floating production storage and offloading facility and vessels. Sensitivity and comparison analyses are conducted to demonstrate the validity and advantages of the proposed model.

APPLICATION OF STATISTICAL DATA AND METHODS TO ESTABLISH RPN RATINGS OF FMEA METHOD FOR CONSTRUCTION PROJECTS

The Failure Mode and Effects Analysis (FMEA) is paramount for analytical skills of reliability design in dynamic prevention. The FMEA model is a significant method which can simultaneously reduce the operating errors or delays as well as improve the construction quality. In particular, the Risk Priority Number (RPN) in the FMEA model is a vital tool which helps construction managers prioritize problem-solving. As the Internet of Things and big data analytical skills have become progressively widespread and mature, among the three risk indicators of RPN, the number of operating errors or delays per unit time can be estimated by the data collected from the analysis of statistical methods and regarded as the basis of 10-level classification. In addition, when the loss is larger, then the severity is higher. This paper proposed three evaluation criteria, including Occurrence, Severity, and Detection of RPN in construction engineering, and a 10-level classification model. To assist the construction managers, priority for construction improvement can be identified based on RPN calculations.

A social network analysis-based model for failure mode and effect analysis under linguistic preference relation environment

Failure mode and effect analysis (FMEA) is a proactive risk analysis technique widely used to improve the reliability and safety of complex systems in various industries. Generally, FMEA is a multidisciplinary team activity in which the trust relationships among experts have an influence on the risk assessment process and the risk priority of failure items. Furthermore, it is significant to obtain the risk ranking result of failure items with a high consistency degree in the group FMEA. Therefore, in this study, we aim to develop a new FMEA model based on double hierarchy hesitant linguistic preference relations to derive the risk priority of failure items considering experts' social network. First, the risk assessments of experts are described by using the double hierarchy hesitant linguistic preference relations via pairwise comparisons of failure items. Second, the relative weights of FMEA experts are derived based on their trust relationships in social network and the consistency of failure risk evaluations. Third, consistency checking and repairing algorithm are performed to address experts’ self-contradictory opinions, and the risk priority of failure items is determined in line with their risk degrees. Finally, a practical risk evaluation case is provided to demonstrate the applicability of the proposed FMEA model, and a comparative analysis is conducted to illustrate its effectiveness and advantages.

Risk prioritization using a modified FMEA analysis in industry 4.0

To assess the reliability of production systems, a Failure Mode and Effects Analysis (FMEA) is usually performed to determine how reliable a system is in its entirety. In order to ensure smooth production operations and timely delivery of orders, it is necessary to ensure continuity of operations without failure and interruption. Industry 4.0 (I4.0) can provide so many benefits, but it also introduces new challenges because of its technical components such as sensors and actuators. In this paper, we propose an FMEA model and structure quantitative risk assessment metrics for a yogurt-filling system in an I4.0 environment. We use the risk priority number (RPN), which is usually a product of three factors: severity (S), occurrence (O), and detection (D) ratings. In this study, we use also a fourth factor, namely, dependency (D2). We compare RPN scores for traditional components and I4.0 components. The application method of the FMEA model was presented in detail, and its practicality was verified and demonstrated. To get valid results, a team of experts was utilized. Then Pareto method was used together with the FEMA approach to prioritize the risks that need more attention. After that, risk prevention steps were introduced. Results showed that four components are the most important ones. Results also showed that some of the components of I4.0, such as proximity sensors and color detection sensors were found with the largest priority. A better performance was achieved following the enhancements suggested by the experts' team. A reduction in RPN of 55% has been achieved by implementing the upgrades, indicating an increase in system reliability.

Risk evaluation of photovoltaic power systems: An improved failure mode and effect analysis under uncertainty

Photovoltaic (PV) power systems are confronted with many failure risks threatening operational security and leading to adverse impacts on the sustainable development. This paper identified the potential PV system failures and extended the traditional failure mode and effects analysis (FMEA) that has been questioned due to its inadequacy in industrial applications. First, the risk factor evaluation system was expanded from the original 3 indicators to 7 indicators associated with PV system failures, including personal safety, power loss, property damage, frequency of failure, visualization, maintenance expense, and technical difficulty. Second, a modified FMEA approach combining rough set theory, cloud model theory and multiplicative Multi-objective Optimization by Ratio Analysis (MULTIMOORA) method was developed. The proposed approach could not only settle various uncertainties (i.e., intrapersonal uncertainty, interpersonal uncertainty, and randomness) by integrating cloud model and rough sets, but also introduce the triple subordinate method MULTIMOORA to obtain a final risk ranking of PV system failures. Finally, the improved FMEA model was adopted to a PV system failure risk assessment case, verifying its effectiveness and superiority. It revealed that hot spots and disconnection of underground cables were the riskiest potential failures in PV systems. Essential risk mitigation measures should be implemented in advance to maintain the safety and promote the sustainable development of PV systems.

Failure Mode and Effect Analysis Using T-Spherical Fuzzy Maximizing Deviation and Combined Comparison Solution Methods

Failure mode and effect analysis (FMEA) is a potent risk analytical instrument extensively utilized for enhancing systems’ quality. Because the classical FMEA model has some deficiencies, numerous fuzzy set-based enhanced FMEA techniques have been developed to improve risk evaluation results’ reasonability. However, most of them require experts to follow certain associated constraints when expressing preferences; otherwise, their preferences will be invalid, which limits experts’ flexibility. In addition, the previous methods rarely consider the reliability of weight allocation results and the stabilization of risk ranking results. Many previous methods usually emphasize the local difference between assessments of failure modes in calculating objective weights and merely depend on one compromise solution in ranking failure modes, both of which may affect the precision of their results. To overcome these limitations, this study applies T-spherical fuzzy sets, the recent generalization of fuzzy sets without strict constraints, to flexibly characterize experts’ preferences. Subsequently, a divergence-based maximizing deviation method is presented to determine the weights of experts and risk factors. A new consensus feedback mechanism is also introduced to achieve consensus among experts. Furthermore, a T-spherical fuzzy combined compromise solution method is presented to rank failure modes stably. Finally, a case study, sensitivity analysis, and comparisons show that the proposed model is effective and practically suitable.

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 probabilistic uncertain linguistic FMEA model based on the extended ORESTE and regret theory

As a means of identifying, preventing, and controlling potential system failures, failure mode and effect analysis (FMEA) has been extensively applied in many industries. However, the classical FMEA method has several defects in actual situations, such as being unable to express uncertain information, overlooking the weights of FMEA experts and risk factors (RFs), and without taking into account the experts’ psychological behaviors. To break through these constraints, a novel FMEA model based on probabilistic uncertain linguistic term sets (PULTSs), Organísation, rangement et Synthèse de données relarionnelles (in French) (ORESTE) and regret theory (RT) is proposed in this paper. First, PULTSs are introduced to express FMEA experts’ assessments under uncertainty. Then, the maximizing consensus model with PULTSs context (PUL-MCM) is constructed to obtain the weights of FMEA experts. Moreover, direct assessment weighting and maximizing deviation method (MDM) are utilized to determine the subjective and objective weights of RFs respectively, so as to reflect the importance of RFs comprehensively. Afterward, the extended ORESTE and RT are combined to prioritize FMs, which not only considers the psychological behaviors of experts and the detailed relations between failure modes (FMs) but avoids the information loss caused by the defuzzification of subjective weights of RFs. Finally, an example of the pasting process failures in lead-acid battery production is presented to illustrate the feasibility and rationality of the novel FMEA model, while sensitivity and comparative analyses are also performed to further demonstrate the flexibility and superiority of the novel FMEA model.