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The paper presents an efficient methodology that was developed for the reliability prediction and the failure mode effects and criticality analysis (FMECA) of electronic devices using fuzzy logic.

The reliability prediction is based on the general features and characteristics of the MIL‐HDBK‐217FN2 technical document and a derating plan for the system design is developed in order to maintain low components’ failure rates. These failure rates are used in the FMECA, which uses fuzzy sets to represent the respective parameters. A fuzzy failure mode risk index is introduced that gives priority to the criticality of the components for the system operation, while a knowledge base is developed to identify the rules governing the fuzzy inputs and output. The fuzzy inference module is Mamdani type and uses the min‐max implication‐aggregation.

A typical power electronic device such as a switched mode power supply was analyzed and the appropriate reliability indices were estimated using the stress factors of the derating plan. The fuzzy failure mode risk indices were calculated and compared with the respective indices calculated by the conventional FMECA.

Further research efforts are needed for the application of fuzzy modeling techniques in the area of reliability assessment of electronic devices. These research efforts can be concentrated in certain applications that have practical value.

Practical applications can use a fuzzy FMECA modeling instead of the classical FMECA one, in order to obtain a more accurate analysis.

Fuzzy modeling of FMECA is described which can calculate fuzzy failure mode risk indices.

This paper aims to enable the analysts of reliability and safety system to assess the criticality and prioritize failure modes perfectly to prefer actions for controlling the risks of undesirable scenarios.

To resolve the challenge of uncertainty and ambiguous related to the parameters, frequency, non-detection and severity considered in the traditional approach failure mode effect and criticality analysis (FMECA) for risk evaluation, the authors used fuzzy logic where these parameters are shown as members of a fuzzy set, which fuzzified by using appropriate membership functions. The adaptive neuro-fuzzy inference system process is suggested as a dynamic, intelligently chosen model to ameliorate and validate the results obtained by the fuzzy inference system and effectively predict the criticality evaluation of failure modes. A new hybrid model is proposed that combines the grey relational approach and fuzzy analytic hierarchy process to improve the exploitation of the FMECA conventional method.

This research project aims to reflect the real case study of the gas turbine system. Using this analysis allows evaluating the criticality effectively and provides an alternate prioritizing to that obtained by the conventional method. The obtained results show that the integration of two multi-criteria decision methods and incorporating their results enable to instill confidence in decision-makers regarding the criticality prioritizations of failure modes and the shortcoming concerning the lack of established rules of inference system which necessitate a lot of experience and shows the weightage or importance to the three parameters severity, detection and frequency, which are considered to have equal importance in the traditional method.

This paper is providing encouraging results regarding the risk evaluation and prioritizing failures mode and decision-makers guidance to refine the relevance of decision-making to reduce the probability of occurrence and the severity of the undesirable scenarios with handling different forms of ambiguity, uncertainty and divergent judgments of experts.

This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper.

Failure mode and effects analysis (FMEA) is a preventive technique in reliability management field. The successful implementation of FMEA technique can avoid or reduce the probability of system failure and achieve good product quality. The FMEA technique had applied in vest scopes which include aerospace, automatic, electronic, mechanic and service industry. The marking process is one of the back ends testing process that is the final process in semiconductor process. The marking process failure can cause bad final product quality and return although is not a primary process. So, how to improve the quality of marking process is one of important production job for semiconductor testing factory. This research firstly implements FMEA technique in laser marking process improvement on semiconductor testing factory and finds out which subsystem has priority failure risk. Secondly, a CCD position solution for priority failure risk subsystem is provided and evaluated. According analysis result, FMEA and CCD position implementation solution for laser marking process improvement can increase yield rate and reduce production cost. Implementation method of this research can provide semiconductor testing factory for reference in laser marking process improvement.

This paper aims to enable the analysts of reliability and safety systems to evaluate the risk and prioritize failure modes ideally to prefer measures for reducing the risk of undesired events.

To address the constraints considered in the conventional failure mode and effects analysis (FMEA) method for criticality assessment, the authors propose a new hybrid model combining different multi-criteria decision-making (MCDM) methods. The analytical hierarchy process (AHP) is used to construct a criticality matrix and calculate the weights of different criteria based on five criticalities: personnel, equipment, time, cost and quality. In addition, a preference ranking organization method for enrichment evaluation (PROMETHEE) method is used to improve the prioritization of the failure modes. A comparative work in which the robust data envelopment analysis (RDEA)-FMEA approach was used to evaluate the validity and effectiveness of the suggested approach and simplify the comparative analysis.

This work aims to highlight the real case study of the automotive parts industry. Using this analysis enables assessing the risk efficiently and gives an alternative ranking to that acquired by the traditional FMEA method. The obtained findings offer that combining of two multi-criteria decision approaches and integrating their outcomes allow for instilling confidence in decision-makers concerning the risk assessment and the ranking of the different failure modes.

This research gives encouraging outcomes concerning the risk assessment and failure modes ranking in order to reduce the frequency of occurrence and gravity of the undesired events by handling different forms of uncertainty and divergent judgments of experts.

The purpose of this paper is to present a multi criterion failure mode effect and criticality analysis for coal-fired thermal power plants using uncertain data as well as substituting the traditional risk priority number estimation method.

Grey-complex proportional assessment (COPRAS-G) method, a multi criteria decision making tool is applied to evaluate the criticalities of the failure modes (alternatives). In this model the criteria (criticality factor) against each alternative are expressed in grey number instead of crisp values.

Rupture failure of the straight tube of economizer (ECO) due to erosion is the highest critical failure mode whereas rupture failure of the stub of ECO due to welding defect is the lowest critical failure mode.

This paper incorporates human and environmental factors as additional factors which also influence the failure modes significantly. The COPRAS-G method is modified according this problem. Uncertainty in the scoring of criticality factors against each failure mode by various maintenance personnel is expressed in grey numbers.

This study aims at introducing a method based on the failure mode, effects and criticality analysis (FMECA) to aid in selecting the most suitable formwork system with the minimum overall cost.

The research includes a review of the literature around formwork selection and analysis of data collected from the building construction industry to understand material failure modes. An FMECA-based model that estimates the total cost of a formwork system is developed by conducting a two-phased semi-structured interview and regression and statistical analyses. The model comprises material, manpower and failure mode costs. A case study of fifteen buildings is analysed using data collected from construction projects in the UAE to validate the model.

Results obtained indicate an average accuracy of 89% in predicting the total formwork cost using the proposed method. Moreover, results show that the costs incurred by failure modes account for 11% of the total cost on average.

The analysis is limited to direct costs and costs associated with risks; other costs and risk factors are excluded. The proposed framework serves as a guide to construction project managers to enhance decision-making by addressing the indirect cost of failure modes.

The research proposes a novel formwork system selection method that improves upon the subjective conventional selection process by incorporating the risks and uncertainties associated with the failure modes of formwork systems into the decision-making process.

The purpose of this paper is to propose a practical method of performing maintenance in the offshore industry where engineers have to manage problems such as the high cost of operations, assuring an high availability of the plant, safety on board and environmental protection. Indeed an efficient maintenance method it is necessary in order to offer methods and criteria to select the rights maintenance strategies keeping in to account the environmental, safety and production constrains.

The paper provides an overview of reliability centered maintenance (RCM) and reliability, availability, maintainability methodologies and an integration of the two methodologies in a particular case study in the oil and gas sector.

This paper suggests an improvement of the well-established RCM methodology applicable to industries with high priority level. It is proposed an integration between a reliability analysis and an availability analysis and an application on the offshore oil and gas industry.

The methodology provides an excellent tool that can be utilized in industries, where safety, regulations and the availability of the plant play a fundamental role.

The proposed methodology provides a practical method for selecting the best maintenance strategy considering the equipment redundancy and sparing, the asset’s performance over long time scales, and the system uptime, downtime and slowdowns.

To permit the system safety and reliability analysts to evaluate the criticality or risk associated with item failure modes.

The factors considered in traditional failure mode and effect analysis (FMEA) for risk assessment are frequency of occurrence (S_f), severity (S) and detectability (S_d) of an item failure mode. Because of the subjective and qualitative nature of the information and to make the analysis more consistent and logical, an approach using fuzzy logic is proposed. In the proposed approach, these parameters are represented as members of a fuzzy set fuzzified by using appropriate membership functions and are evaluated in fuzzy inference engine, which makes use of well‐defined rule base and fuzzy logic operations to determine the criticality/riskiness level of the failure. The fuzzy conclusion is then defuzzified to get risk priority number. The higher the value of RPN, the greater will be the risk and lower the value of RPN, and the lesser will be the risk. The fuzzy linguistic assessment model was developed using toolbox platform of MATLAB 6.5 R.13.

The applicability of the proposed approach is investigated with the help of an illustrative case study from the paper industry. Fuzzy risk assessment is carried out for prioritizing failure causes of the hydraulic system, a primary element of the feeding system. The results provide an alternate ranking to that obtained by the traditional method. It is concluded from the study that the fuzzy logic‐based approach not only resolves the limitations associated with traditional methodology for RPN evaluation but also permits the experts to combine probability of occurrence (S_f), severity (S) and detectability (S_d) of failure modes in a more flexible and realistic manner by using their judgement, experience and expertise.

The paper integrates the use of fuzzy logic and expert database with FMEA and may prove helpful to system safety and reliability analysts while conducting failure mode and effect analysis to prioritize failures for taking corrective or remedial actions.

Failure mode and effect analysis (FMEA) is an analysis technique for identifying and eliminating known and/or potential failures and problems from products, processes or systems. Notwithstanding its diffusion, traditional FMEA has several limitations. Lately, scientific research has been focused on improving said limitations, yet the process is still ongoing. The purpose of this paper is to support developments in this area.

The paper improves the conventional FMEA by using the method of pairwise comparison to establish the relative importance of the input factors in risk priority number calculation, and Markov chains to calculate risk distributions in the long term.

The functioning and usefulness of the proposed methodology is demonstrated through an application to the construction industry, one of the world’s biggest industrial sectors, dogged by a high rate of work-related injuries and casualties.

Having demonstrated the applicability of the novel methodology to a real domain, the paper contributes to the process of overcoming traditional FMEA limitations.