Decision-making tools enabling sustainable maintenance strategies of naval systems: a comparison of multi-criteria approaches

The traditional failure mode, effect, and criticality analysis (FMECA) uses risk priority number (RPN) to evaluate the risk level of a failure mode. The RPN index is calculated by multiplication of severity, occurrence and detection factors. The most critically debated disadvantage of this approach is that various combinations of these three factors may produce an identical value of RPN. This paper reviews the drawbacks in traditional FMECA and proposes a new approach to overcome these shortcomings. The proposed approach evaluates risk of failure mode by encouragement-variable-weighted analytic hierarchy process (EVW-AHP) that can prioritize failure modes even if two or more failure modes have same RPN. An example is provided to show the potential applications of the proposed approach and the detailed computational process is presented. The results based on the case study show the proposed new methodology solves the limitations of traditional FMECA approach and is feasible.

This paper proposes a methodology for managing complex sewerage networks based on the concomitant use of two performance evaluation methods, namely, the failure modes, effects, and criticality analysis (FMECA) and the analytical hierarchy process (AHP). The FMECA is used to determine the risks of structural failures making it possible to establish a methodology for managing these failures. The AHP is used to check the relationship consistency between the performance indicators allowing the determination of the overall performance (OP). This proposed methodology was utilized for the urban sewerage network of Oued-Kniss in the city of Algiers, Algeria, as part of the efforts engaged in for sustainable and efficient management.

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

RAMI analysis of the collective Thomson scattering system front-end – Part1 – Failure modes effects and criticality analysis

This study aims to help the management of the Stormwater Drainage System (SDS) of Bejaia City to manage urban flood problems, i.e., to provide them with tools for a better organization of information on SDS combined with a better optimization of its interventions on the network. Our study is based on a multicriteria analysis of the ?SDS-inundation-Impact? system. We used a multicriteria approach and classified the overflow points called Black Points (BPs) using two methods: Analytic Hierarchy Process (AHP) and Failure Mode, Effect and criticality Analysis (FMEA). The criteria and the evaluation scale were defined on the basis of past observations, expert opinions, and feedback experience. The map of the past flooded areas was made and used to calibrate the two models. We mapped the BPs according to intervention priorities (one to four). The outcomes from both models are greatly comparable to the results of the impact assessment of past floods. The proposed approach can also reduce flood risks by integrating some of influencing factors (causing floodings) and the application can be adapted and implemented in other cities too. Both methods are reliable, particularly the AHP for the most overflowing BPs. They could be advantageously combined to improve decision-making.

Failure mode, effects, and criticality analysis (FMECA) is a safety and reliability analysis tool that systematically identifies the consequences of component failure on systems and determines the impact of each failure mode. Thanks to its effectiveness, it becomes the most used tool in risk management. However, many researchers considered that it has some weaknesses. In FMECA analysis, risk factors are difficult to assess in a precise and complete way because of the uncertainties and inaccuracies of the expert’s judgments. They have considered also that the use of only three factors is not adapted for all activities. This article proposes the combination of fault tree analysis (FTA) and a modified factor FMECA. The modification consists on using Security and Pollution as two additional factors. The formula of risk priority number (RPN) is also adapted to the use of five factors, and the analytical hierarchy process (AHP) will be used to obtain their weights. In the end, this method is applied to a real case study to check its applicability.

Failure modes, effects, and criticality analysis (FMECA) is a qualitative risk analysis method widely used in various industrial and service applications. Despite its popularity, the method suffers from several shortcomings analyzed in the literature over the years. The classical approach to obtain the failure modes’ risk level does not consider any relative importance between the risk factors and may not necessarily represent the real risk perception of the FMECA team members, usually expressed by natural language. This paper introduces the application of Type-I fuzzy inference systems (FIS) as an alternative to improve the failure modes’ risk level computation in the classic FMECA analysis and its use in cyber-power grids. Our fuzzy-based FMECA considers first a set of fuzzy variables defined by FMECA experts to embody the uncertainty associated with the human language. Second, the “seven plus or minus two” criterion is used to set the number of fuzzy sets to each variable, forming a rule base consisting of 125 fuzzy rules to represent the risk perception of the experts. In the electrical power systems framework, the new fuzzy-based FMECA is utilized for reliability analysis of cyber-power grid systems, assessing its benefits relative to a classic FMECA. The paper provides the following three key contributions: (1) representing the uncertainty associated with the FMECA experts using fuzzy sets, (2) representing the FMECA experts’ reasoning and risk perception through fuzzy-rule-based reasoning, and (3) applying the proposed fuzzy approach, which is a promissory method to accurately define the prioritization of failure modes in the context of reliability analysis of cyber-power grid systems.

Argon Purification Unit is a processing unit to purify the crude argon using hydrogen gas through an automatic machinery process. Based on the hazardous material and its automatic machinery process, the argon purification unit needs to be assessed for risk control consideration and business performance. This research proposed risk assessment of argon purification unit based on the failure modes using Failure Modes, Effects and Criticality Analysis (FMECA) with Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) approach to minimize the risks and losses. In this research, FMECA is used to identify the potential failure modes, failure mechanism (causes), potential failure effects for each unit component and evaluate the risk by determining risk priority number (RPN). The RPN is the product of severity, occurrence, and detection variables. Then, Fuzzy-AHP is used to determine the weight of each variable based on its hierarchy. The fuzzy-AHP approach aims to increase validity and decrease expert judgment subjectivity in the risk assessment process for each failure mode by considering variables’ weight. The result of RPN is gained by multiplying each failure mode’s variables by considering the importance of variables. This research results weight of severity is 0.43, which is the highest of all variables. The highest RPN is 8.76, shown by the leaked joint of the argon compressor. This research indicates that the application of the fuzzy-AHP approach in FMECA can identify and evaluate the potential risk of the Argon Purification Unit validly and objectively, which provides the different weight of RPN variables.

Criticality analysis is a technique for the assessment of criticality rating for every constitutive part. Failure mode effect and criticality analysis (FMECA) are broadly utilized for characterizing, distinguishing and dispensing with potential failures from system, design, or process for the criticality analysis. The determination of the critical ranking of failure modes for criticality analysis is a vital issue of FMECA. The traditional method of FMECA determines the critical ranking of failure modes using the risk priority numbers, which is the product of evaluation criteria like the occurrence, severity and detection of each failure mode but it may not be realistic in some applications. The practical applications reveal that the criticality analysis using traditional FMECA has been considerably criticized for several reasons. In this paper, first, a detailed FMEA to find out the various failure modes of each component of a conventional lathe machine is performed and thereafter, the Fuzzy FMECA approach is used to perform the criticality analysis. A comparative analysis of fuzzy FMECA with traditional FMECA is also done to find out the most superior approach for the criticality analysis. It was concluded that the fuzzy FMECA approach is the most superior approach for the criticality analysis of a system.

The high probability of failure of concrete bridges due to their deterioration over time necessitates the implementation of risk assessment tools. One risk assessment tool that has been applied extensively and proven to be effective is the failure mode, effects, and criticality analysis (FMECA). The purpose of FMECA is to determine and assess all possible failure modes and analyze their causes and effects in order to eliminate them before they can occur. The application of FMECA in construction and infrastructure industries, in general, is limited. Therefore, the aim of this paper is to build a framework for implementing FMECA in bridge risk assessment in combination with the VIKOR (VlseKriterijumska Optimizcija I Kaompromisno Resenje in Serbian) method to avoid the problems associated with the traditional FMECA method. The aim of the proposed model is to serve as a guide for ranking and prioritizing failure modes of concrete bridges under a fuzzy environment.

The ELECTRE I method to support the FMECA

The objective of this contribution is to provide a review and suggest possible extensions of the Failure Mode Effects Analysis (FMEA), Hazard Risk Assessment (HRA) [2] and to demonstrate the importance of these tools to general probabilistic design for reliability (PDfR) [8]. FMEA was first introduced in the 1960s by the U.S. National Aeronautics and Space Administration (NASA) and is currently used extensively across many industries. FMEA is useful in understanding the failure modes of various products, qualifying the effects of failure and aiding in the development of mitigation strategies. It is a useful tool in improving quality, reliability, and the maintainability of designs, and is a critical component in risk management strategies and evaluations. This is, actually, the approach of the prognostics and health monitoring/management (PHM) engineering. Failure mode effects and criticality analysis (FMECA) [1] is an extension of (FMEA). While FMEA is a bottom-up, inductive analytical method which may be performed at either the functional or piece-part level, FMECA extends FMEA by including a criticality analysis that is aimed, like PDfR is, at charting the probability of failure modes against the severity of their consequences. The result highlights failure modes with relatively high probability and severity of consequences, allowing remedial effort to be directed where it will produce the greatest value. FMECA tends to be preferred over FMEA in space and North Atlantic Treaty Organization (NATO) military applications, while various forms of FMEA predominate in other industries. Being extensions of the FMEAs, FMECAs add severity and probability ranking aspects to the problems of interest. This is accomplished through an appropriate HRA - an engineering process of where the risk of an event is quantified by examining the chain of the preceding events, starting with, e.g., the failure mode, then stepping through to the end effects. The approach allows quantification of risk through the use of probabilistic risk analysis (PRA) and is addressed and discussed in detail. Failure oriented accelerated testing (FOAT) [9] could and should be viewed as an important constituent part of the effort. It is shown that care must be taken to establish the appropriate probabilities, to identify the statistical independence of the random variables of importance, as well as to assess the trustworthiness of the available or obtained data. It is indicated that an important drawback of the FMEA is the lack of pure operational (field) failure data. These data are frequently utilized from the computerized maintenance management system (CMMS) software, which does not always provide a true snapshot of the Mean Time Between Failures (MTBF) or other critical characteristics of the product. This results in the situation that personal judgment plays a large part in the development of the FMEA. Several papers have been published recently on development of Fuzzy FMEA methodologies (see, e.g., [7]). This application of fuzzy logic to Hazard Risk Analysis will allow additional uncertainty and inaccuracy to be modeled throughout FMECA development, leading to a more robust decision making with consideration of various uncertainties.

Failure mode effects and criticality analysis (FMECA) is a commonly adopted approach to defining, assessing, and reducing possible failures in designs, systems, processes, products, and services. Traditional FMECA ranks the failure modes of products based on a risk priority number (RPN), which is obtained by multiplying the risk elements. Conventional FMECA has the shortcomings of badly handling unknown information and unreasonably assessing RPNs. To deal with these issues, an advanced FMECA method based on intuitionistic 2-tuple linguistic variables (I2LVs) and the triangular fuzzy analytic hierarchy process (TFAHP) is proposed. In this method, the fuzzy evaluation of risk elements given by different FMECA members is represented by I2LVs, which can efficiently handle unknown information. The TFAHP method is adopted to assess the weights of risky elements and rank the risk priorities of different failure modes. Finally, an application case of an insulated-gate bipolar transistor is used to verify the effectiveness and robustness of the proposed method.

This paper presents the potential causes of component failures in wind turbine systems that affect their reliable and efficient operation. Component failures in wind turbines can lead to complete system failure, resulting in downtime, reduced reliability, and increased costs. To fully utilize wind energy, minimizing the risk of component failures is essential. By applying the FMECA method (Failure Mode Effects and Criticality Analysis – FMECA), critical components of wind turbine systems have been identified, providing the opportunity to prioritize problem-solving. The results emphasize the importance of maintenance and design optimization to reduce the risk of failures and maximize the utilization of wind energy. Introduction/purpose: The reliability of wind turbine systems plays a crucial role in ensuring a stable and efficient electricity supply from renewable sources. The failure of any component can lead to system downtime, reduced reliability, and increased operational costs. In this context, it is essential to identify and analyze potential failures in order to improve overall system reliability. The aim of this paper is to analyze the reliability of a wind turbine system using the FMECA method. The focus is on identifying the most critical components and understanding the causes and consequences of their failures, thereby contributing to the improvement of system design, maintenance, and operation. Methods: This paper applies the FMECA (Failure Modes, Effects and Criticality Analysis) method, which enables a detailed assessment of potential system failures, their causes and effects, and the identification of the most critical system points based on quantitative parameters. The methodology includes the following steps: identification of key components of the wind turbine, including the rotor, gearbox, generator, control system, and other subsystems; definition of possible failure modes for each component, with corresponding mechanisms that may lead to failure (e.g., wear, overheating, mechanical damage, etc.); evaluation of the consequences of failures, both on the specific component and on the overall operation of the wind turbine; quantitative risk assessment through the assignment of values for: the probability of potential failure occurrence (R1), the severity of the potential failure (R2), and the probability of detecting the failure and preventing its manifestation (R3); calculation of the criticality level (R) using the expression: 𝑅𝑅 = 𝑅𝑅1 ∙ 𝑅𝑅2 ∙ 𝑅𝑅3; ranking of components based on R values to identify those that pose the greatest threat to system reliability and require prioritized monitoring or optimization. Results: The results of the FMECA analysis indicate that the most critical components of the wind turbine system are: gearbox – the highest criticality level (R value), as gearbox failure can lead to complete system shutdown and costly repairs; generator – high severity of failure and moderate likelihood of failure detection; wind turbine control system – although failures are less frequent, the consequences can be severe due to the loss of control over the turbine. Based on the analysis, components have been classified according to maintenance and monitoring priorities to enable timely detection of potential failures and prevent major breakdowns. Conclusion: The FMECA method is an effective tool for identifying and ranking potentially critical components of wind turbine systems. The results indicate that the gearbox, generator, and control system are the most sensitive points in the system. Their preventive maintenance, along with the implementation of condition monitoring systems and design improvements, can significantly enhance reliability and reduce operational costs. The analysis can serve as a foundation for improving maintenance strategies and increasing wind farms operational efficiency.

The electromechanical servo is the preferred aviation servo actuator system now. EMA (electromechanical actuators), especially EMA of airplanes, will inevitably occur a variety of faults. FMECA (Failure Modes, Effect and Criticality Analysis) is commonly used to analyze the failure mode of the product. However, traditional FMECA is easily affected by subjective factors, and previous studies on FMECA have not focused on the EMA of airplanes. Therefore, this paper was carried out to provide a method of EMA failure modes analysis to find out the most vulnerable part of EMA. Firstly, we analyzed the PMSM using traditional FMECA to obtain a preliminary result for further examination. Then, we used fuzzy comprehensive evaluation to quantify qualitative evaluation indicators and build a fuzzy FMECA model based on fuzzy comprehensive evaluation, which can directly obtain the risk ranking of each failure mode. At the same time, the model was used on PMSM, which is an important part of EMA, to give an example of using this method. Finally, two results were compared to verify the accuracy of the improvement method. The main contribution of the article was to propose a model that can rank the risk levels of all components in EMA on airplanes based on fuzzy FMECA.