Risk Priorities Of Failure Modes Research Articles

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.

Possibilistic chance-constrained data envelopment analysis framework for failure modes and effects analysis

Failure modes and effects analysis (FMEA) is an intensively used risk analysis technique for prioritizing failure modes of products, systems, and services. Traditionally, FMEA assumes deterministic and precise values for risk factors. In engineering scenarios, the observed values of decision-makers on risk factors are, however, potentially stochastic and imprecise in nature due to the lack of adequate knowledge and experience. On the other hand, the structural correlation between risks (or failure modes) and induced factors (e.g., the loss cost and recovery time of products, systems, and services) is rarely reported in FMEA. To this end, we develop a possibilistic chance-constrained data envelopment analysis (DEA) framework to overcome the above deficiencies. In this framework, three differentiated categories of DEA approaches (namely, series, parallel, and network structure DEA) are devised to characterize such structural correlation. More specially, the Me measure is introduced to handle imprecise parameters associated with risk factors. Simultaneously, decision-makers are endowed to express flexible attitudes on these imprecise parameters. Moreover, in order to reveal the mechanism of risk priority of failure modes, the proposed framework enables decision-makers to offer optimistic and pessimistic attitudes on risk input and propagation, respectively. A case study of a production system, along with some comparisons and discussions, is given to demonstrate the effectiveness and merits of the proposed framework. As indicated from this case study, no matter which DEA approach is conducted, the risk priority of failure modes is stable in a stochastic environment.

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.

Risk assessment of bolt-gasket-flange connection (BGFC) failures at hydrogen transfer stations based on improved FMEA

Failure Modes and Effects Analysis (FMEA) is an engineering technique widely used to identify, evaluate, and eliminate known or potential failures, problems, and errors and inform risk management decisions. Traditional FMEA uses the risk priority number (RPN) to identify potential failure modes and their levels. However, it displays shortcomings in determining risk factor weights, ranking risk priorities of failure modes, and dealing with uncertainties during the risk assessment process. To address these issues, this paper proposed an improved FMEA risk assessment method by integrating FMEA, the analytic hierarchy process (AHP), the technique for order preference by similarity to an ideal solution (TOPSIS), and the cloud model (CM) to determine a more reasonable failure mode level. First, the CM was used to improve the FMEA framework structure to comprehensively assess ambiguity and randomness during the evaluation process. Second, the cloud weights of the risk factor severity (S), occurrence (O), and detectability (D) were calculated using AHP and CM to determine the relative risk factor importance. Then, the cloud distance measurement algorithm and TOPSIS were combined into the TOPSIS-CM risk ranking method to objectively identify the failure modes requiring improvement. Finally, a case study of a flange connection system at a hydrogen blending transmission site in northern China was conducted to verify the adaptability of the proposed method in failure mode assessment. Compared with traditional FMEA methods, the decision scores obtained using the improved FMEA were more discriminating and distinguished the risk of each failure mode, providing more accurate information to decision makers (DMs).

Decision-making model for failure modes and effect analysis based on rough fuzzy integrated clouds

Risk assessment is one of the key topical subjects to identify and eradicate potential failures from system, design and structure at the early stage of production process. As different kinds of uncertainties such as diversity, randomness, vagueness coexist, single uncertainty based decision models are not sufficient to evaluate risk priority of failure modes. However, the existing mathematical approaches usually utilize single models ignoring the coexistence of intrapersonal and interpersonal uncertainties in experts’ judgements. The weights computed from relative importance of risk factors are essential information to capture the relation of various factors in decision system. Most of the current references do not consider the objective and subjective weights simultaneously which effects the rationality of results. In the present study, a novel linguistic assessment model , namely rough fuzzy integrated clouds (RFICs), is developed to handle uncertain information by integrating rough fuzzy numbers with cloud model theory. Then a significant decision support model for the analysis of failure modes is constructed based on RFICs, hybrid weighting method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The positive and negative ideal solutions are determined using statistical deviations and arithmetic operations of clouds to rank the failure modes. The proposed model is implemented on a real-world application to detect the failures of steam valve system in a power generation plant. The simulation and sensitivity of the proposed model is also analyzed for different variations of control parameters. The proposed approach is compared with four existing approaches to show its effectiveness, out-performance and improved accuracy.

Failure mode and effects analysis based on rough cloud model and MULTIMOORA method: Application to single-point mooring system

The failure mode and effects analysis (FMEA) is an important tool for risk assessment, which can identify and eliminate potential failure modes in a system. However, due to the inherent drawbacks of the traditional FMEA method, the risk assessment results may be inaccurate. In this study, an FMEA approach based on the rough cloud model and MULTIMOORA (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form) method is developed to evaluate the risk of the single-point mooring system. The main contributions of this study are as follows. First, an interval asymmetric rough cloud (IARC) model is proposed by combining cloud model and rough set theory, which can deal with the randomness of expert individual evaluation and the uncertainty of expert group evaluation. Second, an IARC power weighted average operator is proposed to eliminate the influence of extreme expert opinions when aggregating expert opinions. Third, according to the feature of single-point mooring system failure, a two-level risk factor hierarchical structure is established, and the subjective and objective weights are integrated to determine the risk factor weights. Fourth, an extended MULTIMOORA method based on the rough cloud model is developed to determine the risk priority of failure modes. To illustrate the implementation process of the approach, we have applied it to a single-point mooring system of an FPSO (Floating Production Storage and Offloading) in the South China Sea. The comparison analysis with the existing methods is also presented to validate the feasibility and effectiveness of this approach.

A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis techniques due to its outstanding capabilities in identifying, assessing, and eliminating potential failure modes in a wide range of industrial applications. It provides a comprehensive view for investigating potential failures, causes, and effects in designs, products, and processes. However, traditional FMEA is extensively criticized for its defects in determining the criteria weights, identifying the risk priority of failure modes, and handling the uncertainty during the risk evaluation. To resolve these problems, this study proposes a novel fuzzy rough number extended multi-criteria group decision-making (FR-MCGDM) strategy to determine a more rational rank of failure modes by integrating the fuzzy rough number, AHP (analytic hierarchy process), and VIKOR (Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje). Above all, a fuzzy rough number is introduced to characterize experts’ judgment, aggregate group risk assessments, and tackle the uncertainty and subjectivity in the risk evaluation. Then a fuzzy rough number enhanced AHP is presented to determine the criteria weights. A fuzzy rough number enhanced VIKOR is proposed to rank the failure modes. A practical case study of the check valve is provided to validate the applicability of the proposed FMEA. Comparative studies demonstrate the efficacy of the proposed FR-MCGDM, with remarkable advantages in handling the uncertainty and subjectivity during failure modes evaluation.

Risk assessment of submarine pipelines using modified FMEA approach based on cloud model and extended VIKOR method

The development of offshore oil and gas resources is inevitable for a submarine pipe network. Submarine pipeline leakage can easily escalate into a catastrophic event, causing enormous loss of life and property and environmental pollution. Therefore, it is imperative to conduct risk assessments of submarine pipelines. A failure mode and effects analysis (FMEA) is a significant method in risk analysis. However, due to the uncertainty in the risk analysis process, the assessment results may not be sufficiently accurate. In this paper, to evaluate the risk of submarine pipeline with enhanced reliability, an improved FMEA method based on cloud model and extended vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) is proposed. The main contributions of this method are as follows. First, to minimize the associated linguistic uncertainties during the evaluation process, the cloud model theory is used, enabling the fuzziness and randomness to be comprehensively considered. Second, an improved synthetic dynamic weight algorithm, considering the personal status of experts as well as the agreement degree and confidence level of expert comments is proposed to strengthen the knowledge of the experts to minimize incompleteness. Third, an extended two-level risk factor hierarchy of submarine pipeline failure is established to improve the comprehensiveness of risk assessment, meanwhile, an integrated weighting method considering both subjective and objective aspects is utilized to obtain the risk factor weights, which can comprehensively reveal the risk factors’ relative importance. Fourth, the VIKOR method is extended with the cloud model to determine the risk priority of failure modes, which can offer a compromise solution in the context of uncertainty. Furthermore, a case study on a submarine pipeline of the Chengbei oilfield in China is performed to illustrate the applicability of proposed approach. Sensitivity analysis are carried out to observe the robustness of the proposed method. Finally, the comparison between the obtained results and that from pre-existing methods shows that the proposed method is a more accurate and effective method for the risk assessment of a submarine pipeline.

A new approach for risk assessment of failure modes considering risk interaction and propagation effects

Failure Mode and Effect Analysis (FMEA) is an efficient method for risk prevention and control, which has been applied to identify the potential risks and improve system reliability in various fields. Nevertheless, few attempts in previous research have been made to examine the impacts of the interactions among the failure modes on risk priority, which will influence the risk priority accuracy. To solve this problem, we propose a novel method to improve the conventional FMEA approaches. The proposed method not only considers the positive and negative influences of failure modes, but also the attenuation effect of such influences in the system. In addition, different from the previous FMEA approaches assuming that all failure modes play equally important roles in risk propagation, the new FMEA method considers the initial strength effect of failure mode on the risk propagation to ensure the accuracy of the risk priority. The final risk priority of a failure mode is computed recursively by the PageRank algorithm, which considers the effect of risk priority of other failure modes associated with this failure mode. Finally, to verify the effectiveness of the proposed method, we apply it to a case study of failure mode effect analysis for smart bracelets.

Improved FMEA based on IVF and fuzzy VIKOR method: A case study of workpiece box system of CNC gear milling machine

AbstractFailure mode and effect analysis (FMEA) is a tool used to define, identify, and prevent known or unknown potential risks. An improved FMEA based on interval triangular fuzzy numbers (IVF) and fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is proposed in this study to solve problems of expression and processing of uncertain information, weights of risk factors, and ranking of failure modes in traditional FMEA. Linguistic variables are used to evaluate failure modes level and relative importance of risk factors and are expressed via interval‐valued triangular fuzzy number. Determining the subjective weights of risk factors using fuzzy AHP, calculating the objective weights of risk factors using the extended VIKOR method, and obtaining the comprehensive weights of risk factors via ICWGT are proposed for solving the weight problem of risk factors. Finally, the fuzzy VIKOR method is used to rank risk priority of failure modes. The proposed method is used to evaluate workpiece box system of CNC gear milling machine and the results are compared with the findings of other methods to verify effectiveness and rationality of the proposed method.

A Fuzzy-FMEA Risk Assessment Approach for Offshore Wind Turbines

Failure Mode and Effects Analysis (FMEA) has been extensively used by wind turbine assembly manufacturers for risk and reliability analysis. However, several limitations are associated with its implementation in offshore wind farms: (i) the failure data gathered from SCADA system is often missing or unreliable, and hence, the assessment information of the three risk factors (i.e., severity, occurrence, and fault detection) are mainly based on experts’ knowledge; (ii) it is rather difficult for experts to precisely evaluate the risk factors; (iii) the relative importance among the risk factors is not taken into consideration, and hence, the results may not necessarily represent the true risk priorities; and etc. To overcome these drawbacks and improve the effectiveness of the traditional FMEA, we develop a fuzzy-FMEA approach for risk and failure mode analysis in offshore wind turbine systems. The information obtained from the experts is expressed using fuzzy linguistics terms, and a grey theory analysis is proposed to incorporate the relative importance of the risk factors into the determination of risk priority of failure modes. The proposed approach is applied to an offshore wind turbine system with sixteen mechanical, electrical and auxiliary assemblies, and the results are compared with the traditional FMEA.

An improved failure mode and effects analysis method based on uncertainty measure in the evidence theory

Abstractmode and effects analysis (FMEA) is an effective tool to assess the risk of a system or process under uncertain environment. However, how to handle the uncertainty in the subjective assessment is an open issue. In this paper, a novel method to deal with the uncertainty coming from subjective assessments of FMEA experts is proposed in the framework of Dempster–Shafer evidence theory. First, the uncertain degree of the assessment is measured by the ambiguity measure. Then, the uncertainty is transformed to the reliability of each FMEA expert and the relative importance of each risk factor. After that, the assessments from FMEA team will be fused with a discounting‐based combination rule to address the potential conflict. Moreover, to avoid the situation that different risk priorities of failure modes may have the same ranking based on classical risk priority number method, the gray relational projection method (GRPM) is adopted for ranking risk priorities of failure modes. Finally, an application of the improved FMEA model in sheet steel production process verifies the reliability and validity of the proposed method.

An extended MULTIMOORA method based on OWGA operator and Choquet integral for risk prioritization identification of failure modes

Failure mode and effects analysis (FMEA) is one of the important methods for risk analysis, and has been used in various fields to improve the reliability of systems. However, in the fuzzy environment, the aggregation, weight calculation, risk evaluation and prioritization of evaluation information limit its wide application. Therefore, this paper proposes an extended multi-objective optimization by ratio analysis plus full multiplicative form (MULTIMOORA) method based on the ordered weighted geometric averaging (OWGA) operator and Choquet integral for FMEA. Firstly, trapezoidal fuzzy numbers (TrFNs) are used for describing the fuzzy ratings of failure modes. Secondly, considering the uncertainty preferences of decision-makers, the OWGA operator is adopted for aggregating the evaluation information given by multiple decision-makers. Thirdly, the TrFNs ranking method based on the relative preference relation is combined with Choquet integral to model the interactions among risk factors, thus capturing their importance weights. On this basis, an extended MULTIMOORA method is proposed to determine the risk priority of failure modes. Finally, two case studies are provided to illustrate the effectiveness and practicability of the proposed method.

An integrated fuzzy-genetic failure mode and effect analysis for aircraft wing reliability

The purpose of this paper is to propose a model for assessing the hazard system based on the failure structure of aircraft wing. The model considers reliability maintenance and repair factors. Failure Mode and Effect Analysis (FMEA) is one of the well-known methods of quality management being used for continuous improvement in product or process plans. One serious issue of FMEA is the definition of the risk priorities of failure modes. This paper proposes an analytical method based on failure modes and failure analysis in criticality and suggests a fuzzy evaluation method for evaluating the risk level of the wing of aircraft. The analytical technique includes qualitatively analyzing the operation, failure modes, failure cause, failure rate, and severity through FMEA and quantitatively assessing the safety by using the fuzzy evaluation method. Fuzzy logic and Genetic Algorithms are integrated using a risk-cost model based on FMEA and comparisons with Simulated Annealing algorithms.

Failure mode and effect analysis using regret theory and PROMETHEE under linguistic neutrosophic context

Failure mode and effect analysis (FMEA), which aims to identify and assess potential failure modes in a system, has been widely utilized in diverse areas for improving and enhancing the performance of systems due to it is a powerful and useful risk and reliability assessment instrument. However, the conventional FMEA approach has been suffered several criticisms for it has some shortcomings, such as unable to handle ambiguous and uncertain information, neglect the relative weights of risk criteria, and without considering the psychological behaviors of decision-makers. To ameliorate these limitations, this paper aims at establishing a hybrid risk ranking model of FMEA via combing linguistic neutrosophic numbers, regret theory, and PROMETHEE (Preference ranking organization method for enrichment evaluation) approach. In the presented model, linguistic neutrosophic numbers are adopted to capture decision-makers’ evaluation regarding the failure modes on each risk criterion. A modified PROMETHEE approach based on regret theory is presented to obtain the risk priority of failure modes considering the psychological behaviors of decision-makers. Moreover, a maximizing deviation model and TOPSIS (Technique for order preference similar to ideal solution) are separately applied to derive the weights of risk criteria and decision-makers. Finally, a numerical example relating to the supercritical water gasification system is employed to implement the presented method, and the effectiveness and feasibility of the proposed model are validated by the results derived from a sensitivity and comparison analysis.

Advanced FMEA method based on interval 2-tuple linguistic variables and TOPSIS

Failure mode and effects analysis (FMEA) is a widely used technique for identifying, evaluating, and eliminating potential failures in production, system, and process. The traditional FMEA ranks the failure modes according to risk priority numbers (RPN), which are obtained by the multiplications of the crisp values of risk factors, such as occurrence (O), severity (S), and detection (D). However, the traditional FMEA is criticized for mishandling uncertain information and calculating RPN unreasonably. To overcome the above deficiencies, this study presents an advanced FMEA method combined with interval 2-tuple linguistic variables (ITLV) and technique for order preference by similarity to ideal solution (TOPSIS). In the proposed method, the evaluations given by different FMEA members based on their different linguistic term sets are represented by ITLVs, which are feasible and valid variables to effectively deal with uncertain information. The TOPSIS method is used to rank the risk priorities of failure modes by comprehensively considering all of risk factors. Finally, an application case is provided to illustrate the validity and robustness of the proposed method.

Failure mode and effects analysis: an integrated approach based on rough set theory and prospect theory

Failure mode and effect analysis (FMEA), a bottom-up method, is one of risk assessment tools to eliminate or reduce failures in design and process. It has been applied to many industries due to its flexibility and effectiveness. However, the conventional FMEA considers less about the subjectivity and vagueness in the process of risk assessment and assumes that three risk factors’ importance is the same. Although a lot of approaches based on fuzzy logic are proposed to deal with vague information in previous literature, they need priori assumptions leading to fixed intervals to express vagueness. In addition, most of the previous methods suppose that decision makers are totally rational without considering their psychological factors. To solve the problems, an extended technique for order performance by similarity to ideal solution (TOPSIS) is developed to improve FMEA approach, which combines the advantage of variable precision rough number in dealing with vague information and the strength of prospect theory (PT) in considering decision maker’s bounded rationality. The proposed method consists of two stages: one is the determination of risk factors’ weight function values; and the other is ranking risk priority of failure modes with the PT-based TOPSIS. Finally, a case study of a steam valve system is used to demonstrate the effectiveness and efficiency of the proposed method.

Risk Assessment for Failure Mode and Effects Analysis Using the Bonferroni Mean and TODIM Method

As a safety and reliability analysis technique, failure mode and effects analysis (FMEA) has been used extensively in several industries for the identification and elimination of known and potential failures. However, some shortcomings associated with the FMEA method have limited its applicability. This study aims at presenting a comprehensive FMEA model that could efficiently handle the preference interdependence and psychological behavior of experts in the process of failure modes ranking. In this model, a linguistic variable expressed by the interval-valued Pythagorean fuzzy number (IVPFN) is utilized by experts to provide preference information with regard to failure modes’ evaluation and risk factors’ weight. Then, to depict the interdependent relationships between experts’ preferences, the Bonferroni mean operator is extended to IVPFN to aggregate the experts’ preference. Subsequently, an extended TODIM approach in which the dominance degree of failure modes is calculated by grey relational analysis is utilized to determine the risk priority of failure modes. Finally, a practical example concerning the risk assessment of a nuclear reheat valve system is provided to demonstrate the effectiveness and feasibility of the presented method. In addition, a sensitivity analysis and comparison analysis are conducted, and the results show that the preference interdependence and psychological behavior of experts have an important effect on the risk priority of failure modes.

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

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

A new failure mode and effects analysis model using Dempster–Shafer evidence theory and grey relational projection method

Failure mode and effects analysis (FMEA) is an important analytical tool in reliability engineering to identify the critical potential failure modes. In this paper, a new FMEA model using Dempster–Shafer evidence theory (DSET) and grey relational projection method (GRPM) is proposed, which mainly manages two critical issues of FMEA: the presentation and handling of various types of uncertainty and the ranking of risk priorities of failure modes. DSET has a good advantage to express and model the assessment results of risk factors. GRPM is used to determine the risk priority order of the identified failure modes, where the double reference points (the positive/negative ideal alternative) are applied. Two illustrative cases are provided to demonstrate the effectiveness and practicality of the proposed method.