A Comprehensive Risk Management Approach to Information Security in Intelligent Transport Systems

Background: Water safety planning is a comprehensive risk assessment and management approach encompassing all steps in a drinking-water supply chain, from catchment to consumer. A Water Safety Plan (WSP) ensures drinking water safety through this approach. In this study, risk factors are initially identified and evaluated. Then control and corrective measures are determined to reduce or eliminate health and environmental hazards of rural water supply systems in Khorramshahr City, Iran, according to the guidelines of the WSP provided by the World Health Organization. Methods: This research is a descriptive cross-sectional study in which rural water supply systems in Khorramshahr were studied using the Failure Mode and Effects Analysis (FMEA) risk systematic method. The Risk Priority Number (RPN) was calculated to determine the risk level after identifying 14 risk factors using experts’ opinions. Then, control and corrective measures were considered for medium, high, and very high-level risk factors. Results: The evaluation results of 14 risk factors identified in the distribution network and point of consumption indicated that 71.5% of them were at the medium risk level and 28.5% at the high (critical) risk level. After determining control and corrective measures, 92.9% of risk factors reached the medium (manageable) level and 7.1% the critical level. Conclusion: This result indicates that water safety guidelines can replace traditional methods of inspection and process control, and significant improvements can be achieved with the help of risk assessment by the FMEA method and step-by-step implementation of the WSP as an essential evolutionary solution for preventive measures and reducing the level of existing risks.

The implementation of Intelligent Transport System (ITS) technology is expected to significantly improve road safety and traffic efficiency. One of the key components of ITS is precise vehicle positioning. Positioning with decimetre to sub-metre accuracy is a fundamental capability for self-driving, and other automated applications. Global Navigation Satellite System (GNSS) Precise Point Positioning (PPP) is an attractive positioning approach for ITS due to its relatively low-cost and flexibility. However, GNSS PPP is vulnerable to several effects, especially those caused by the challenging urban environments, where the ITS technology is most likely needed. To meet the high integrity requirements of ITS applications, it is necessary to carefully analyse potential faults and failures of PPP and to study relevant integrity monitoring methods. In this paper an overview of vulnerabilities of GNSS PPP is presented to identify the faults that need to be monitored when developing PPP integrity monitoring methods. These vulnerabilities are categorised into different groups according to their impact and error sources to assist integrity fault analysis, which is demonstrated with Failure Modes and Effects Analysis (FMEA) and Fault Tree Analysis (FTA) methods. The main vulnerabilities are discussed in detail, along with their causes, characteristics, impact on users, and related mitigation methods. In addition, research on integrity monitoring methods used for accounting for the threats and faults in PPP for ITS applications is briefly reviewed. Both system-level (network-end) and user-level (user-end) integrity monitoring approaches for PPP are briefly discussed, focusing on their development and the challenges in urban scenarios. Some open issues, on which further efforts should focus, are also identified.

This paper intends to present the application of FMEA method on Three-Way Catalytic Converter (TWC) system. Catalytic converter of auto-exhaust emission is one of the most successful applications of heterogeneous catalysis, both in commercial and environmental point of view. FMEA method will be applied to this system to quantitatively determine and evaluate its risk factors. This method is being employed effectively for identifying and addressing what potentially could go wrong with a product or process. It is expected to enhance the lifetime of the TWC by improving its resistance to deactivation. It is widely accepted that FMEA is one of the best quality improvement tool. For the last several decades, FMEA has been widely used in industry especially in automotive sectors. This research will cover mostly on the system and design of the TWC itself as the most important part for controlling the exhaust emission from automobiles. By improving its resistance to deactivation will contribute to longer lifetime of automotive catalytic converter.

Potential failure mode and effects analysis for the ITER NB injector

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster-Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine.

Improving failure analysis efficiency by combining FTA and FMEA in a recursive manner

Failure mode and effects analysis (FMEA) is a critical but labor-intensive process in product development that aims to identify and mitigate potential failure modes to ensure product quality and reliability. In this paper, a novel framework to improve the FMEA process by integrating generative artificial intelligence (AI), in particular large language models (LLMs), is presented. By using these advanced AI tools, we aim to streamline collaborative work in FMEA, reduce manual effort and improve the accuracy of risk assessments. The proposed framework includes LLMs to support data collection, pre-processing, risk identification, and decision-making in FMEA. This integration enables a more efficient and reliable analysis process and leverages the strengths of human expertise and AI capabilities. To validate the framework, we conducted a case study where we first used GPT-3.5 as a proof of concept, followed by a comparison of the performance of three well-known LLMs: GPT-4, GPT-4o and Gemini. These comparisons show significant improvements in terms of speed, accuracy, and reliability of FMEA results compared to traditional methods. Our results emphasize the transformative potential of LLMs in FMEA processes and contribute to more robust design and quality assurance practices. The paper concludes with recommendations for future research focusing on data security and the development of domain-specific LLM training protocols.

Failure Mode and Effects Analysis (FMEA) is a systematic and analytical quality planning tool for identifying and addressing what potentially could go wrong with a product or process. The project ‘Failure Mode and Effects Analysis (FMEA) in the cladding industry’ describes the FMEA technique, investigates failures of cladding on a system, component and process level, and maps the cladding supply chain and cladding-related decision making. The level of knowledge of failures and the fragmented industry structure prevents rigorous use of FMEA exemplified by other industries. However, a simplified form of FMEA can be performed based on the research findings to prioritize and inform decision-making and facilitate site inspection/supervision. L'analyse des modes de défaillance et de leurs conséquences (AMDC) est un outil systématique de planification de la qualité qui sert à détecter tout ce qui pourrait nuire à un produit ou un processus et à y remédier. Le projet ‘Analyse des modes de défaillance et de leurs conséquences chez les fabricants de bardage’ a pour objectif de décrire la technique AMDC, d'étudier les défaillances de bardages au niveau système, composant et processus, et d'établir une correspondance entre la chaîne d'approvisionnement en bardages et le processus décisionnel en matière de bardage. Le niveau de connaissances en ce qui concerne les défaillances et la structure fragmentée de cette industrie empâchent l'utilisation rigoureuse de l'AMDC comme en attesient d'autres industries. Une forme simplifice de l'AMDC peut toutefois être utilisée sur la base des résultats de recherches, qui permet d'indiquer au processus décisionnel les priorités et de faciliter les interventions d'inspection et de surveillance sur les chantiers.

The automotive industry is one of the most dynamically growing fields of the manufacturingarea. Besides this, it has very strict rules concerning safety and reliability. In our work, our aim is to point out the importance of the automotive industry (based on statistics) and the rules in connection with risk and root cause analysis. The most important risk analysis method is the Failure Mode and Effect Analysis (FMEA). According to standards and OEM regulations, FMEA is obligatory in the automotive sector. In our study, we summarise the area of FMEA usage, its types and process steps.

The cost of home insulation?: Morin, N. C. & Kubinski, H. (1978). Potential toxicity of materials used for home insulation. Ecotoxic. envir. Safety2, 133

For the last years, a bibliometric examination of risk evaluation approaches for excavating systems has been presented in this publication. To develop an early warning system, it's essential to compile a list of possible dangers that can arise during excavation. Failure Mode and Effects Analysis (FMEA) is a useful approach. Traditional risk assessment techniques have been criticized for a variety of reasons, including a lack of correlation between risk variables, difficult arithmetic operations, and a lack of correctness and preciseness in the evaluations. A unique method of risk analysis in FMEA that uses digraphs and matrix approaches underneath the Pythagorean fuzzy scenario is presented in this research. To get started, we'll defy Pythagorean fuzzy numbers in a triangle form. Both language terminology and risk factor data and information are expressed using them (inclusive of occurrence, severity, and detection). The Pythagorean fuzzy digraph thus captures the interrelationships between the risk variables and the relative importance of each one, as seen in the figure. After that, we create a Pythagorean fuzzy test indicated for each identified failure mode and compute risk priority indexes to determine risk priorities. Using a metro station excavation as a case study, the accuracy of risk assessments in excavation is improved.

Medium-density fiberboard (MDF) production involves multiple intricate stages. Uneven thickness formation, large volumes of dynamic data from automated systems, and rapidly evolving technologies create substantial challenges for maintaining consistent quality control. Conventional approaches rely heavily on expert judgment and lack predictive capability, leaving a critical gap in timely and accurate risk assessment. This study addresses these challenges by integrating Failure Mode and Effects Analysis (FMEA) with machine learning techniques to evaluate and predict risks throughout the MDF production process. Real production data from an industrial facility were used to ensure practical relevance. Domain experts first assessed the Severity (S), Occurrence (O), and Detection (D) parameters using the PFMEA method. Predictive models—including K-Nearest Neighbors, Support Vector Machine, Neural Network, and an Ensemble Method—were then developed to estimate risk scores. The findings show that the Neural Network and Ensemble Method achieved the highest overall accuracy. This integrated approach reduces subjective bias, enhances predictive precision, and supports informed decision-making for quality control and risk mitigation in industrial MDF production.

Concurrent engineering has been widely accepted as a viable strategy for companies to reduce time to market and achieve overall cost savings. This article analyzes various risks and challenges in product development under the concurrent engineering environment. A three-dimensional early warning approach for product development risk management is proposed by integrating graphical evaluation and review technique (GERT) and failure modes and effects analysis (FMEA). Simulation models are created to solve our proposed concurrent engineering product development risk management model. Solutions lead to identification of key risk controlling points. This article demonstrates the value of our approach to risk analysis as a means to monitor various risks typical in the manufacturing sector. This article has three main contributions. First, we establish a conceptual framework to classify various risks in concurrent engineering (CE) product development (PD). Second, we propose use of existing quantitative approaches for PD risk analysis purposes: GERT, FMEA, and product database management (PDM). Based on quantitative tools, we create our approach for risk management of CE PD and discuss solutions of the models. Third, we demonstrate the value of applying our approach using data from a typical Chinese motor company.

A new risk assessment approach: Safety and Critical Effect Analysis (SCEA) and its extension with Pythagorean fuzzy sets

Classical and fuzzy FMEA risk analysis in a sterilization unit