A directed failure causality network (DFCN) based method for function components risk prioritization under interval type-2 fuzzy environment

The study proposes a comprehensive and systematic approach for multi-criteria and multi-stage facility location selection problem. To handle with high and more uncertainty in the evaluation and selection processes, the problem is solved by using multi-criteria decision making technique with interval Type-2 fuzzy sets. The study contributes the facility location selection literature by introducing the application of fuzzy TOPSIS method with interval Type-2 fuzzy sets. Finally, the suggested approach is applied to a real life region and site selection problem of a cement factory.

Type-2 fuzzy sets are generalisations of ordinary fuzzy sets in which the membership grades are characterised by fuzzy membership functions. Here, we consider the problem of finding the distance between two interval type-2 fuzzy sets (IT2-FSs). We define the formulation of calculating the distance between two IT2-FSs based on a new definition of centroid for an IT2-FS and explain an algorithm for obtaining it. Then, based on our proposed distance formulation, we present a method to deal with fuzzy multiple attributes group decision-making (FMAGDM) problems. We also use two examples to illustrate the FMAGDM process of the proposed method. The method provides us with a useful way to deal with FMAGDM problems based on IT2-FSs.

This paper introduces a new proposal to involve uncertainties in Fuzzy Markov Chains by using Interval Type-2 Fuzzy Sets (IT2 FS). A Type-1 Fuzzy Markov chain is an approach which uses Type-1 Fuzzy Sets (T1 FS) to describe the distributional behavior of a Discrete-Time Markov process, while the IT2 FS approach is an extension of its scope that allows to embed several T1 FS inside its Footprint of Uncertainty. In this paper, a finite state Fuzzy Markov Chain process is defined by an Interval Type-2 Fuzzy environment, finding their limiting properties and its Type-reduced behavior. To do so, two examples are provided.

Failure mode risk prioritization is crucial in aircraft landing systems, where undetected or misjudged failures can lead to catastrophic outcomes. Effective risk analysis enables proactive maintenance and enhances aviation safety in such critical phases of flight. In this study, a novel hybrid decision-making framework is proposed to prioritize failure modes in aircraft landing systems by integrating the Multi-Attributive Border Approximation Area Comparison (MABAC) method with Prospect Theory under a q-Rung Orthopair Hesitant Fuzzy Set (q-ROHFS) environment. Traditional failure modes and effects analysis (FMEA) approaches often suffer from rigid weighting schemes, lack of sensitivity to expert hesitancy, and an inability to incorporate psychological factors such as risk aversion or subjective evaluations—especially in high-risk domains like aviation. To address these limitations, the proposed model incorporates human psychological behaviour and uncertainty in expert assessments. Prospect Theory is employed to capture decision makers’ risk attitudes and reference-dependent evaluations, while q-ROHFSs allow more flexible and comprehensive representation of hesitant and uncertain information. In this approach, Best-Worst Method (BWM) is used to determine the relative importance of risk factors for each decision maker, and their individual weights are obtained using TOPSIS-based similarity measures. A novel generalized q-ROHF Minkowski distance measure is also introduced and implemented to determine the weights of decision makers in the TOPSIS method, as well as to construct the prospect decision matrix and the distance matrix in the MABAC method, thereby enhancing computational precision. The applicability and effectiveness of the proposed method are demonstrated through a real-world case study on aircraft landing systems, and a sensitivity analysis is conducted to validate the robustness of the results. The findings highlight the method’s capability to reflect expert preferences more realistically and improve risk prioritization decisions in complex safety-critical systems.

Preliminary failure modes and effects analysis on Korean HCCR TBS to be tested in ITER

The towing system is one of the most crucial parts of a tug boat. The towing process in tugboats, where a lot of machines and ship equipment are involved in the process, carries many risks and potential failures. Therefore, this operation can cause serious injuries, fatal accidents, equipment damage, disruption of towing operations and loss of time and money. In this study, a comprehensive risk analysis was conducted to increase the reliability of the tugboat towing system. Firstly, the failure modes, causes and results of the towing system were determined. Then, the failure modes and effect analysis method were integrated with the rule-based fuzzy approach and risk prioritization was performed. According to the findings, the three critical failure modes among 23 failure modes were towing system rope failure, insufficient air supply to the towing system from the compressor and cracking or breaking of system components, respectively. Additionally, corrective and preventive actions were suggested for a total of eleven failure modes with high RPN values. This study aims not only to provide a theoretical contribution to the prevention of towing system-related accidents, but also to provide a practical framework that can be used in practice for all maritime stakeholders, especially tugboat owners and crews.

Photovoltaic (PV) power plant projects in East Java often face challenges during the initiation phase due to inadequate risk assessments. This phase is crucial for ensuring the project's long-term success; however, suboptimal risk identification has caused project delays. In the risk assessment process, high-priority risks need to be mitigated, but differences in the methods used can result in varying risk priority outcomes. One of the methods that can be used is Failure Mode and Effect Analysis (FMEA), while according to internal regulations at PT PLN (Persero), the Risk Matrix is applied. By using these two methods, the differences in the resulting risk priority rankings can be identified. Both tools rely on evaluations from experts experienced in decision-making during the initiation phase of PV power plant projects. From the analysis, 12 risks were identified during the initiation phase of the Pagerungan Besar PV power plant project. The highest-priority risks identified include Suboptimal feasibility study (Risk Priority Number/RPN: 74.27), unfavorable outcomes for the organization/company (RPN: 68.96), lack of budget (RPN: 64.00), and stakeholder Intervention (RPN: 53.69). Meanwhile, by plotting the Severity Level and Occurrence Level of each risk on the Risk Matrix according to PT PLN (Persero) regulations, the highest-priority risks identified include Suboptimal feasibility study (Extreme Risk Level, Zone E5), location-related issues (Extreme Risk Level, Zone E5), non-compliance with regulations (Extreme Risk Level, Zone E5), and suboptimal project team (Very High Risk Level, Zone D5). The two risk assessment methods produced different prioritizations, potentially impacting mitigation strategy decisions. This variation highlights the need for further analysis to ensure accurate and reliable risk prioritization, which is critical for project success and efficient resource allocation. Future studies should focus on evaluating prioritization methods to support effective decision-making and ensure timely project implementation in line with the targeted Commercial Operation Date (COD).

Modern microgrids are networked systems comprising physical and cyber components for networking, computation, and monitoring. These cyber components make microgrids more reliable but increase the system complexity. Therefore, risk assessment methods are an imperative technology for cyber-physical systems to ensure safe and secure operations. The authors propose a reliability approach that utilizes the failure modes of power and cyber network key components to perform the risk analysis in microgrids. In this work, the authors have used the Failure Mode and Effect Analysis (FMEA) approach for risk assessment of microgrid systems and determine the influence of various failure modes on their performance. FMEA is one of the most effective methods to assess the risk involving the cyber components. It is the method of examining components, modules, and subsystems to determine failure modes in a system and their causes and consequences. A novel approach is proposed to calculate risk priority numbers based on factors like severity, occurrence, and detection. A risk matrix is calculated from the proposed FMEA worksheet, which acts as a graphical representation for evaluating components risk based on risk priority number or criticality metrics.

Safety Instrumented Systems (SIS) protect major hazard facilities against catastrophic accidents. A SIS consists of hardware components and a software part - the program. Failure Mode Reasoning (FMR) is a novel abstraction technique for identifying and quantifying failure modes of SIS hardware components based on an analysis of the SIS program. In FMR, the program is divided into smaller segments, for each of which a local analysis of failure modes is carried out to identify input failure modes based on the function of the segment and a given failure mode at its output. The results of local analyses are then combined and simplified into a short list of global input failure modes. This paper explains the concepts of FMR and demonstrates its application through a real-life example. A comparison with other methods, such as Fault Tree Analysis is given in order to highlight the role of FMR in enhancing safety analysis of SIS.

An ELICIT information-based ORESTE method for failure mode and effect analysis considering risk correlation with GRA-DEMATEL

Supply chain management is a major challenge for hospital pharmacists, particularly in minimising stock-outs and their impact on patient care. This study aimed to map the risks associated with the supply chain of sterile medical devices in the operating room using a patient-centred approach. Risk analysis, conducted using the 'failure modes and effects analysis' approach, took place in 2024. It focused on the sterile medical devices supply chain within an operating room pharmaceutical unit. A multidisciplinary working group developed a risk map for stock discrepancies in four stages: identification of risks, risk rating based on occurrence and severity, listing of control measures and prioritisation of risks. The risk analysis identified 52 risks across seven subprocesses, with 10% of these risks classified as priority. These priority risks mainly involved the emergency dispensing of equipment in the operating room, the scheduled provision of medical devices through the preparation of case carts, and the storage of products in the pharmaceutical unit. Key areas for improvement included staff training, enhancing software expertise and strengthening quality assurance. This study presents the first risk map of stock errors for health products in a hospital setting. This approach is applicable to other supply chains in healthcare. Effective risk management depends on a collective effort and the engagement of all stakeholders. Optimising stock management is critical in enhancing the quality and safety of care provided to patients.

PurposeThis paper proposes an integrated IT2F-FMEA model under a group decision-making setting. In risk assessment models, experts' evaluations are often aggregated beforehand, and necessary computations are performed, which in turn, may cause a loss of information and valuable individual opinions. The proposed integrated IT2F-FMEA model aims to calculate risk priority numbers from the experts' evaluations and then fuse experts' judgments using a novel integrated model.Design/methodology/approachThis paper presents a novel failure mode and effect analysis (FMEA) model by integrating the fuzzy inference system, best-worst method (BWM) and weighted aggregated sum-product assessment (WASPAS) methods under interval type-2 fuzzy (IT2F) environment. The proposed FMEA approach utilizes the Mamdani-type IT2F inference system to calculate risk priority numbers. The individual FMEA results are combined by using integrated IT2F-BWM and IT2F-WASPAS methods.FindingsThe proposed model is implemented in a real-life case study in the furniture industry. According to the case study, fifteen failure modes are considered, and the proposed integrated method is used to prioritize the failure modes.Originality/valueMamdani-type singleton IT2F inference model is employed in the FMEA. Additionally, the proposed model allows experts to construct their membership functions and fuzzy rules to capitalize on the experience and knowledge of the experts. The proposed group FMEA model aggregates experts' judgments by using IT2F-BWM and IT2F-WASPAS methods. The proposed model is implemented in a real-life case study in the furniture company.

PT. DALWA ANUGRAHHASANIAH, a company that produces Packaged Drinking Water (AMDK) using ozone technology, this company also produces various sizes of glasses, bottles, and uses two types of raw materials - mineral water and demineralized water. This study aims to ensure that the AMDK production process does not experience defects. In an effort to reduce product defects, the Failure Mode and Effect Analysis (FMEA) method is used. FMEA is a proactive risk management method used to identify potential failures in a system, design, process, or service before it reaches the consumer. The goal is to prevent failure, maintain product quality, and reduce associated costs. The Failure Mode and Effect Analysis (FMEA) method is detailed, focusing on identifying failure modes, effects, causes, severity, occurrence, detection, and risk prioritization. Successful FMEA implementation is defined by a 30%-50% reduction in the new RPN value compared to the initial RPN value.

This paper aims at investigating the dynamic response of the steel box girder under internal blast loads through experiments and numerical study. Two blast experiments of steel box models under internal explosion were conducted, and then, the numerical methods are introduced and validated. The dynamic response process and propagation of the internal shock wave of a steel box girder under internal blast loading were investigated. The results show that the propagation of the internal shock wave is very complicated. A multi‐impact effect is observed since the shock waves are restricted by the box. In addition, the failure modes and the influence of blast position as well as explosive mass were discussed. The holistic failure mode is observed as local failure, and there are two failure modes for the steel box girder′s components, large plastic deformation and rupture. The damage features are closely related to the explosive position, and the enhanced shock wave in the corner of the girder will cause severe damage. With the increasing TNT mass, the crack diameter and the deformation degree are all increased. The longitudinal stiffeners restrict the damage to develop in the transverse direction while increase the crack diameter along the stiffener direction.

7 - Hazard analysis methods