Explore the vast range of titles available.

Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump.

PurposeTo address the limitations of traditional failure mode and effects analysis (FMEA) methods in elevator fault analysis, including heavy reliance on human experience, limited use of large scale heterogeneous text data, static analysis results and insufficient interpretability, this study aims to develop an intelligent FMEA method for the elevator domain.Design/methodology/approachAn elevator FMEA method based on retrieval augmented generation (RAG) is proposed. An external knowledge base is constructed by integrating a knowledge graph (KG) with a vector database. During retrieval, a multi-route retrieval strategy is adopted to obtain candidate documents. A reranking model named CapsGCN-Rank based on a graph convolutional capsule neural network is designed to perform fine-grained filtering and reranking of candidate documents. The reranked documents are then combined with a large language model to generate structured fault analysis results.FindingsExperimental results show that the proposed method outperforms several baseline methods in context precision, context recall, as well as the relevance and correctness of generated answers. The method effectively improves the accuracy and completeness of elevator FMEA.Originality/valueThe proposed approach introduces structured semantics from the KG, a multi-route retrieval strategy and dynamic routing of capsule neural networks into the RAG framework. It enables fine-grained document reranking and interpretable fault analysis for the elevator domain, providing an effective solution for FMEA in complex industrial scenarios.

This research is driven by the necessity for effective and high-quality maintenance systems capable of fulfilling rigorous equipment maintenance standards. The purpose of this study was to identify potential failures in an air knocker, evaluate the risk of failure through the Fuzzy FMEA approach, and optimize decision-making using the AHP method. This study was based on theories related to maintenance, Fuzzy FMEA, and AHP. The research methodologies employed comprised a literature review, data analysis, and the application of Expert Choice software to compute weights and ratings according to pertinent criteria. The results showed that the integration of Fuzzy FMEA and AHP methods was effective in identifying potential failures, evaluating risks more accurately, and prioritizing optimal corrective actions in the maintenance system. This study suggests that the integration of Fuzzy FMEA and AHP methods can improve risk management and decision-making in maintenance systems. This strategy assists organizations in mitigating the risk of failure, enhancing efficiency, and more effectively addressing process requirements. This methodology enables a more comprehensive examination of risk variables and the efficient management of uncertainties, as well as the decision-making process for assessing the risks associated with air knocker operations.

Objective: The objective of this study is to identify, analyze and control the risks to occupational health and safety present in the operation of a printing machine, through FMEA and modeling in Excel VBA of the parameters severity, occurrence and detectability, with the aim of identifying the failure modes that must be treated as a priority.   Theoretical Framework: FMEA is widely used in the identification and analysis of potential or known failures in processes, therefore enabling decision-making support. To be most effective, FMEA must be developed by a team. With the aim of adjusting points for improving the application of the technique, it is proposed to combine FMEA with other existing analysis and multi-criteria methods. In this research, FMEA is used in conjunction with Excel VBA programming that allows multiplication within ranges of randomly generated values, using the histogram to prioritize the RPN.   Method: The methodology adopted for this research involves defining, based on the criticality of the printing machines, the machine that will be the object of study to develop and apply a methodology for occupational health and safety through the application of FMEA by focal group, composed of eight professionals, and modeling of RPN parameters to prioritize risk mitigation measures. The classification of the research regarding its nature is applied; as for the nature of the data, it is quali-quantitative; as for the purpose, it is exploratory; As for the delimitation, it is non-experimental. In the case study, the following data collection methods were used: interviews with printing workers, observations and application of the FMEA form (from ISO 31010).   Results and Discussion: The results obtained revealed that the risks present in the operation include: Electric shock; neck/spine pain; limb fracture; cuts; abrasions, among others. The modeling carried out through programming in Excel VBA made it possible to multiply, within the respective ranges of intervals, random numbers obtained for the variables Si, Oi and Di, obtaining 200 RPN results and Si x Oi products. The failure mode which must be addressed, as a priority, concerns the “Descending of the rocker arm with the hand(s) positioned in the field of action” on the Melmaq machine model 800 TM it is possible to mitigate the most significant risk by installing sensor to detect the presence of the hand and rocker arm pressure adjustment system. Throughout the research, some difficulties were encountered, with emphasis on the challenge of finding professionals who had knowledge about machine operation, work safety, as well as the FMEA risk management technique, and who were willing to participate in the research.   Research Implications: Practically, the research directly contributed to risk management in the operation of the paper shearing machine at the printing company under study. Theoretically, the research develops and applies a methodology that utilizes the FMEA technique supported by VBA modeling in Excel for the parameters of severity, occurrence, and detectability.   Originality/Value: The search in the Scopus and Web of Science databases, during the conducting of the research, revealed a lack of publications in the area of occupational safety risk management in printing machines using FMEA. Objective: The objective of this study is to identify, analyze and control the risks to occupational health and safety present in the operation of a printing machine, through FMEA and modeling in Excel VBA of the parameters severity, occurrence and detectability, with the aim of identifying the failure modes that must be treated as a priority.   Theoretical Framework: FMEA is widely used in the identification and analysis of potential or known failures in processes, therefore enabling decision-making support. To be most effective, FMEA must be developed by a team. With the aim of adjusting points for improving the application of the technique, it is proposed to combine FMEA with other existing analysis and multi-criteria methods. In this research, FMEA is used in conjunction with Excel VBA programming that allows multiplication within ranges of randomly generated values, using the histogram to prioritize the RPN.   Method: The methodology adopted for this research involves defining, based on the criticality of the printing machines, the machine that will be the object of study to develop and apply a methodology for occupational health and safety through the application of FMEA by focal group, composed of eight professionals, and modeling of RPN parameters to prioritize risk mitigation measures. The classification of the research regarding its nature is applied; as for the nature of the data, it is quali-quantitative; as for the purpose, it is exploratory; As for the delimitation, it is non-experimental. In the case study, the following data collection methods were used: interviews with printing workers, observations and application of the FMEA form (from ISO 31010).   Results and Discussion: The results obtained revealed that the risks present in the operation include: Electric shock; neck/spine pain; limb fracture; cuts; abrasions, among others. The modeling carried out through programming in Excel VBA made it possible to multiply, within the respective ranges of intervals, random numbers obtained for the variables Si, Oi and Di, obtaining 200 RPN results and Si x Oi products. The failure mode which must be addressed, as a priority, concerns the “Descending of the rocker arm with the hand(s) positioned in the field of action” on the Melmaq machine model 800 TM it is possible to mitigate the most significant risk by installing sensor to detect the presence of the hand and rocker arm pressure adjustment system. Throughout the research, some difficulties were encountered, with emphasis on the challenge of finding professionals who had knowledge about machine operation, work safety, as well as the FMEA risk management technique, and who were willing to participate in the research.   Research Implications: Practically, the research directly contributed to risk management in the operation of the paper shearing machine at the printing company under study. Theoretically, the research develops and applies a methodology that utilizes the FMEA technique supported by VBA modeling in Excel for the parameters of severity, occurrence, and detectability.   Originality/Value: The search in the Scopus and Web of Science databases, during the conducting of the research, revealed a lack of publications in the area of occupational safety risk management in printing machines using FMEA.

In order to enable failure mode and effects analysis (FMEA) to play a better quality control role in complex manufacturing products or systems, the current research status of FMEA is reviewed from failure mode identification, risk assessment, and industrial standard application. Firstly, the research status of system failure identification is summarized from the following aspects: the breakthrough point of identification, the types of identification methods, and the normalized description of failure modes. Then, sort out the research status of risk assessment from five aspects: risk factor evaluation criteria, risk assessment opinion expression, expert opinion consensus, risk opinion assessment aggregation, and sensitivity analysis, and find out research hotspots and blind spots; finally, the changes of FMEA standards in various fields are summarized and compared, and the future development trend of FMEA in the context of intelligent manufacturing is discussed.

Large, powerful corporations were formerly solely and exclusively responsible for supplies, manufacturing, and distribution; however, the supply chain has undergone significant transformations over the last half-century. Almost all supply chain processes are currently outsourced, owing to the initiatives of cutting-edge, contemporary businesses. According to a compilation of studies, analysts, and news sources, the level of risk associated with modern supply chains is considerably higher than the majority of supply chain managers believe. Supply chain vulnerabilities continue to pose a substantial obstacle for a great number of organizations. Neglecting to adequately address these risks—encompassing natural disasters, cyber assaults, acts of terrorism, the credit crisis, pandemic scenarios, and war—could result in substantial reductions in metrics such as profitability, productivity, revenue, and competitive advantage. Unresolved concerns persist with respect to the risk assessment of the supply chain. The purpose of this article is to propose a framework for risk evaluation that can be efficiently applied to the evaluation of hazards within the supply chain. This research study significantly enhances the existing knowledge base by offering supply chain managers a pragmatic tool to evaluate their processes, regardless of the mathematical foundations or the variety of variables utilized in risk assessment. The outcomes of multiple aggregation methods are compared using a case study from an automotive EMS production; the conclusions are validated by risk and FMEA specialists from the same factory.

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.

The increase of productivity and decrease of production loss is an important goal for modern industry to stay economically competitive. For that, efficient fault management and quick amendment of faults in production lines are needed. The prioritization of faults accelerates the fault amendment process but depends on preceding fault detection and classification. Data-driven methods can support fault management. The increasing usage of sensors to monitor machine health status in production lines leads to large amounts of data and high complexity. Machine Learning methods exploit this data to support fault management. This paper reviews literature that presents methods for several steps of fault management and provides an overview of requirements for fault handling and methods for fault detection, fault classification, and fault prioritization, as well as their prerequisites. The paper shows that fault prioritization lacks research about available learning methods and underlines that expert opinions are needed.

Abstract Introduction Cardiac implantable electronic devices (CIEDs) are increasing in prevalence. Exposing patients with CIEDs to magnetic resonance imaging (MRI) can lead to adverse outcomes. This has led certain radiology departments to not accept MRI referrals related to patients with CIEDs. Patients with MR-conditional CIEDs can be safely scanned under specific conditions. Our institution has accepted such referrals since 2014. The aim of this study was to systematically identify and reduce risk in our CIED-MRI protocol using failure mode and effects analysis (FMEA). Methods A multidisciplinary FMEA team was assembled and included senior stakeholders from the CIED-MRI protocol. A process map was constructed followed by risk analysis and scoring. Targeted interventions were formulated and implemented; high-risk failure modes were prioritized. A new process map and protocol were drafted and repeat risk analysis was performed. Monitoring and re-evaluation of the CIED-MRI pathway were instigated at departmental quality assurance (QA) meetings. Results Interventions included direct CIED characterization using wireless technology pre-MRI, CIED programming and reprogramming in the MRI suite before and immediately after MRI reducing device downtime and continuous patient monitoring during MRI by a cardiac physiologist. The cumulative risk priority number (RPN) decreased from 1190 pre-FMEA to 492 post-FMEA. Discussion Despite the risk of exposing CIEDs to the MR environment, patients with MR-conditional CIEDs can be safely scanned with an appropriate multidisciplinary support. We found FMEA an indispensable tool in identifying and minimizing risk with no adverse events recorded since FMEA recommendations were implemented.

Failure modes and effects analysis (FMEA) is a commonly used step-by-step approach to assess potential failures existing in a product or process design. In this paper, a modified FMEA model based on an interval-valued spherical fuzzy extension of technique for order preference by similarity to ideal solution (IVSF-TOPSIS) is proposed to cope with drawbacks of the traditional risk priority number (RPN) computation. Spherical fuzzy sets are the integration of Pythagorean fuzzy sets and neutrosophic sets. They provide more freedom to experts in decision making by including the degree of membership, non-membership, and hesitation of fuzzy sets. Therefore, initially, TOPSIS is merged with a special branch of spherical sets “interval-valued spherical fuzzy sets” to determine priorities of emerged failures. As a novelty to traditional RPN of FMEA, three parameters called cost, prevention, and effectiveness in addition to the existed parameters of severity, occurrence and detection are attached to the proposed approach. Weights of these parameters are determined via an interval-valued spherical weighted arithmetic mean operator (IVSWAM). As a demonstration, a case study in a marble manufacturing facility is provided to show the applicability of the novel model. Results show that the most crucial failure modes concern with the maintenance and repairing works of the factory and the lack of technical periodic checks of lifting vehicles regarding “block area: crane” failures. Some comparative and validation studies are also performed to test the solidity of the approach.