Failure Mode And Effects Analysis Technique Research Articles

A comprehensive protocol for evaluating health, safety, and environmental risks of hospital solid waste through FMEA technique

This protocol outlines a comprehensive approach to evaluating hospital solid waste levels and assessing associated health, safety, and environmental (HSE) risks using the Failure Mode and Effects Analysis (FMEA) methodology. The study focuses on Imam Khomeini Hospital (RA) and employs both quantitative and qualitative methods. Over a 3-month period, waste production and potential risks are assessed, with specific attention to household, infectious, medicinal, and sharps waste. Through FMEA, potential failure modes and associated risks in waste management sectors are identified, enabling targeted interventions for risk mitigation. The protocol emphasizes the importance of aligning waste management practices with international standards and highlights the need for comprehensive training, awareness campaigns, and effective waste management methods to ensure the safety and environmental responsibility of hospital waste management practices

AI in education risk assessment mechanism analysis

ABSTRACT Artificial intelligence (AI) has had a significant impact on several sectors and fields. Failure Modes and Effects Analysis (FMEA) is a powerful risk management and prevention tool that can help companies identify and address any weaknesses in their practices. The Risk Priority Number (RPN) approach has been criticized for its flaws since it calculates the product of severity, occurrence, and detection ratings of threats. By considering a variety of criteria and aspects, and by thoughtfully and carefully combining expert perspectives, the proposed FMEA model may more accurately evaluate the risks associated with AI in Education. Picture Fuzzy Sets (PFSs) and Grey Relational Analysis-TOPSIS (GRA-TOPSIS) are combined to achieve this. The goal of this research is to use an updated FMEA model to assess and rank the risk hierarchy of the seven identified hazards. According to the data, algorithmic risk is the most significant issue that requires urgent attention. Relevant constraints and suggestions are also provided to encourage research on the dangers of AI in Education. Suggestions include creating a national or international regulatory authority to control the use of artificial intelligence in education, promoting the use of the FMEA technique as a common framework for assessing the risks connected to AI in Education.

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Overview of Failure Mode and Effects Analysis (FMEA): A Patient Safety Tool.

Applicability of the risk ranking methodology designed for water reservoirs to tailings storage facilities

The risks associated with operating water reservoirs and tailings storage facilities (TSFs) are different because of their different purposes, methods of construction and operation, and characteristics of the materials impounded and their flow behaviour. Regardless of the differences, these two types of structures are often put in the same category when it comes to risk assessment and the application of relevant methodologies, which may result in unrealistic outcomes. In this paper we investigate whether it is possible to apply the risk ranking methodology designed for water reservoirs to TDFs and overview the key differences between these two types of structures. We also provide a comparative analysis of the results obtained by conducting a risk assessment of the operation of a TSF, applying the method recommended by the International Commission on Large Dams (ICOLD), and analyse the results using the failure mode and effects analysis (FMEA) technique. It is concluded that a more realistic evaluation of risk levels can be obtained by adopting a systematic approach to risk assessment in accordance with the traditional risk definition on which the FMEA technique is based.

Risk assessment of biological hazards to the nurses using ‘Failure Modes and Effects Analysis’ (FMEA) in the intensive care unit

Background: Nursing staff in the intensive care unit (ICU) is exposed to multiple biologic hazards. We aimed to identify biological hazards threatening the health of nurses in the ICU using the technique of Failure Modes and Effects Analysis (FMEA) tool.
 Methodology: A cross-sectional study by Focus Discussion Group (FDG) was performed in the ICU. Participants were familiar with the processes in ICU. The sampling method was purposeful and discrete and the researcher selected FDG group members. The study instrument was standard worksheet. Data analysis was performed by FMEA technique and based on Risk Priority Number (RPN).
 Results: 53 possible errors were identified in 14 common activities in the ICU. Error cases with unacceptable risk were identified, including "lack of hand hygiene" (RPN 177.75), "needle stick injuries” (RPN 167.62), “lack of personal protective equipment" (RPN 133.92) and "absence of N95 or FFP2 mask during CPR of COVID-19 patients" (RPN 111.60).
 Conclusion: The causes and effects of the errors were identified, and corrective measures were presented in three areas: reducing the frequency and severity and increasing the error detection capability. It was suggested that retraining courses for nurses be held to prevent errors and ensure the health of nurses.
 Abbreviations: FMEA - Failure Modes and Effects Analysis; FDG - Focus Discussion Group; ICU - intensive care unit; RPN - Risk Priority Number
 Key words: Biological Hazards; FMEA; Intensive Care Unit; Risk Assessment
 Citation: Hosseni L, Karampourian A, Hiva Azami H. Risk assessment of biological hazards to the nurses using ‘Failure Modes and Effects Analysis’ (FMEA) in the intensive care unit. Anaesth. pain intensive care 2023;27(6):646−651; DOI: 10.35975/apic.v27i6.2200
 Received: April 11, 2023; Revised: October 08, 2023; Accepted: : October 08, 2023

ANALYSING THE OPERATIONAL RISK INVOLVED IN THE MANUFACTURING OF AUTOMOBILES

Risk management is an important issue in manufacturing companies in to‐ day’s competitive market. Failure modes and effects analysis (FMEA) method is a risk management tool to stabilize production and enhance market com‐ punitiveness by using risk priority numbers (RPN). Although the traditional FMEA approach is a successfully and commonly used method, it has some shortcomings such as notion of equal importance of the factors, severity, incident and delectability, and not following the ordered weighted rule. Thus, in order to improve RPN, an added method blending grey personal analysis (GRA) with FMEA is used in this study. The purpose of this paper is to contribute to risk management activities by proposing solutions to assembly line challenges in an automotive manufacturing company by using combined GRA and FMEA method. In the proposed method, the priorities of production failures were determined by GRA method, and these failures were minimized by using FMEA technique. The study results indicated the actions that lead to enhancement in the product. The accomplishment of cor‐ restive/preventive activities resulted in 96 % development in door seal cuts problem caused by the door step building. Door seal cuts problem caused by instrument panel assembly and the noisy door window problem are solved totally

An integrated risk assessment method using Z‐fuzzy clouds and generalized TODIM

AbstractAs a systematic risk assessment instrument, failure mode, and effects analysis (FMEA) has been extensively applied for enhancing systems’ quality. However, the classical FMEA technique has limitations in many application scenarios. Although fuzzy logic‐based risk assessment techniques are introduced to enhance conventional FMEA, they still have limitations, for example, needing prior presumptions, neglecting decision‐makers’ bounded rationality, and lacking the capability to reflect the judgmental reliability and randomness of experts’ evaluations simultaneously. To cover these limitations, an extended FMEA method is presented in this paper by using Z‐fuzzy clouds and generalized TODIM (An acronym in Portuguese of interactive and multiple attribute decision‐making). First, a novel concept of Z‐fuzzy clouds is proposed to handle various factors of uncertainties by combining the advantages of the Z‐number theory, the interval‐valued intuitionistic fuzzy sets theory, and the cloud model theory, and the basic operations and Minkowski‐type distance measure of Z‐fuzzy clouds are also defined and discussed. Furthermore, more risk factors are added into the conventional three risk factors and to further build a multiple dimension risk factor system for better evaluating failures. Additionally, the experts’ bounded rationality and the risk factors’ interrelationships are considered by two developed weighting models. Finally, a real study case, sensitivity analysis, and several comparisons are performed to validate the effectiveness of the proposed model. Results demonstrate that the proposed model is more rational compared with the previous several methods.

Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques

Carbon fiber-reinforced plastics (CFRP)-packaged fiber Bragg grating (FBG) sensors are widely used in full-scale structural testing of wind turbine blades (WTBs). However, the specific process to make CFRP-packaged FBG sensors, such as packaging, bonding, welding, etc., are mainly manually operated, and no unified standard or rule has been formed yet. Non-standard specific processes, coupled with complex stress distribution, unstable working environments, etc., result in the CFRP-packaged FBG sensors having various failures with time, resulting in inaccurate measurements. Thus, the need to carry out related failure analysis is urgent. This paper therefore performed a reliability analysis for CFRP-packaged FBG sensors using failure mode and effects analysis (FMEA) and fault tree analysis (FTA) techniques. The results provide an important basis towards analyzing performance degradation and functional failures for CFRP-packaged FBG sensors.

Development of Advanced Advisory System for Anomalies (AAA) to Predict and Detect the Abnormal Operation in Fired Heaters for Real Time Process Safety and Optimization

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

Systems failure analysis using Z-number theory-based combined compromise solution and full consistency method

Financial and time constraints force managers to put limited failure modes in priority to implement corrective and preventive actions. Thus, how prioritization can be done more constructively is of particular importance. This study aims to introduce an improved Failure Modes and Effects Analysis (FMEA) technique based on an extension of the Combined Compromise Solution (CoCoSo) method and the Full Consistency Method (FUCOM) to assess and prioritize failures in a production process. These developed methods called Z-CoCoSo and Z-FUCOM rely on the Z-number theory. They can consider uncertainty and reliability simultaneously in determining the weights of risk factors and the value of these factors in the studied problem. The proposed approach can cover some shortcomings of the conventional FMEA technique employed in identifying potential failures before their occurrence. Applying this approach enables experts to consider different weights for risk factors and uncertainty in the risk assessment process and provides them with a reliable ranking with high separability. Implementing the introduced approach for a real case study in the automotive parts industry has been compared with FMEA and other existent versions of the used methods demonstrating its reliable prioritization.

FMEA Using the Normalized Projection-Based TODIM-PROMETHEE II Model for Blood Transfusion

Failure mode and effect analysis (FMEA) is a forceful way for preventing the occurrence of possible damages before the process of management activities, which is widely utilized in various fields. However, experts’ bounded rationality, imprecise risk assessments and unobjective risk factors’ weights are prominent challenges existed in the conventional FMEA model. Thus, to better meet the challenges and further improve the performance of FMEA, this paper constructs a novel FMEA model for risk ranking by integrating the TODIM with the PROMETHEE II technique under q-rung orthopair fuzzy environment. Considering the fuzziness and uncertainty in risk assessments, q-rung orthopair fuzzy sets (q-ROFSs) are utilized to assist experts in describing their actual opinions concerning each failure modes flexibly and freely. Further, based on the proposed normalized projection model for q-ROFSs, a comprehensive criteria weighting determination method is employed for obtaining the optimal weights and a novel distance measurement of q-ROFSs is also further presented. Subsequently, the integrated TODIM-PROMETHEE II technique is developed for ranking failure modes, which has the ability of considering risk aversion behavior of experts. After that, the detailed steps are depicted via a representative case in blood transfusion. Finally, the comparative analysis is conducted to verify the stable and superiority of the FMEA method proposed in this paper. The proposed method under q-ROF environment can solve some cases which the traditional FMEA technique cannot, as well as helping experts to express their opinions flexibly, which improves the implementation accuracy of the FMEA.

Reducing Energy Losses of Steam Boilers Caused by Blowdown with Using the FMEA Method

ABSTRACT In this study, the boiler blowdown was systematically examined and, the risks that increase the energy loss was determined using the potential Failure Mode and Effects Analysis (FMEA) method. Risk priority numbers (RPN) were calculated for each potential risk. It was revealed that ‘Performing more blowdown than it is necessary’ has the highest priority score. Improvement opportunities were identified by using the tree diagram, which is one of the process improvement techniques. Three suggestions were developed, such as flash steam heat recovery, reduction of water conductivity using the reverse osmosis technique, and the use of both methods. Repayment periods were calculated for each investment. If the flash steam unit and reverse osmosis system are applied together, 113.5 KW/h energy can be saved. It has been demonstrated that the FMEA technique can be used in the investigation and reduction of energy loss.

Identification and assessment of risk in construction projects using the integrated FMEA-SWARA-WASPAS model under fuzzy environment: a case study of a construction project in Iran

Nowadays, focusing on risk management is one of the core objectives of any organization. Failing to assess project risks during the planning stage could be hazardous throughout the entire project lifecycle. Therefore, risk assessment is so important and various methods have been developed for it. One of the most important risk assessment methods is Failure Mode and Effects Analysis (FMEA). Despite the widespread applications of this method in various industries and organizations, FMEA is associated with some shortcomings that can lead to unrealistic results. In order to identify and evaluate the risks affecting construction projects and cover some shortcomings of the FMEA technique suggested an integrated model based on FMEA, Stepwise Weight Assessment Ratio Analysis (SWARA), and Weighted Aggregated Sum Product Assessment (WASPAS) methods under fuzzy environment in this research. To this end, a case study about the construction projects of the Iranian Road and Urban Development Organization is presented to illustrate the potential benefits and application of the proposed integrated approach. The results suggest that the new integrated model is more capable and accurate in identifying and prioritizing risk compared to the conventional FMEA technique and can provide useful information to help manage the risks of construction projects.

Risk Evaluation in Blood Donation Using Failure Mode and Effective Analysis

The recognition and management of risk in donation process and blood product is critical to ensure donor and patient safety. To achieve this goal, the failure mode and effects analysis (FMEA) is a convenient method; moreover it was used to prevent the occurrence of adverse events and look at what could go strong at each step. This study aimed to utilize FMEA in central blood bank in Khartoum to evaluate the potential risk and adverse event that may occur during the donation process. According to the severity, occurrence and the detection of each failure mode, the risk priority number (RPN) was calculated to determine which of the failures should take priority to find a solution and applying corrective action to reduce the failure risk. The statistical package for social sciences (SPSS) version 11 was used as descriptive and analytical statistics tool. The FMEA technique provides a systematic method for finding vulnerabilities in a process before they result in an error, and in this study a satisfactory outcome was reached.

Failure mode and effect analysis: An interval-valued intuitionistic fuzzy cloud theory-based method

Failure mode and effect analysis (FMEA) is a proactive quality management instrument to improve the reliability of systems. Nevertheless, the classical FMEA technique has suffered from many weaknesses, e.g., inability to handle inaccurate information, strong sensitiveness to variations in assessments. Although fuzzy theories are utilized to enhance the classical FMEA, they still have some deficiencies, e.g., requiring extra assumptions, lacking mechanism to describe the hesitation and randomness of assessment information simultaneously, ignoring the psychological effects of experts, and considering only three risk aspects among most of them. Hence, this work presents a novel concept of interval-valued intuitionistic fuzzy clouds (IVIFCs), which combines the merit of interval-valued intuitionistic fuzzy set in reflecting vagueness and hesitation of decision information and the strength of cloud model in manipulating randomness of quantitative information, and a new FMEA based on IVIFCs. Then, the individual bounded rationalities are determined by a developed weighting method considering both subjective and objective importance. Moreover, a hierarchical structure containing eight risk elements is established to identify risk orders of failures. Additionally, a well-defined Excel computational program is presented to reduce the calculation burden effectively. Finally, a real application of a machine tool is conducted by the proposed FMEA to illustrate its effectiveness and superiority.

A two-stage Failure Mode and Effect Analysis of offshore wind turbines

Abstract This paper proposes a two-stage Failure Mode and Effect Analysis (FMEA) technique as a basis for implementing the failure analysis of offshore wind turbines. At the first stage, critical failure causes and failure modes of each component of offshore wind turbines are identified. In the next stage, critical components and systems of offshore wind turbines are ascertained by a cost-and-risk-based index that considers both risk priority and failure costs of components. The objective is to overcome some weaknesses of the traditional FMEAs including: (i) Risk-based FMEA ignores practical information extracted in the operation stage of offshore wind turbines such as failure cost and, (ii) Cost-based FMEA addresses mainly failures of components and systems and cannot deepen to failure modes and failure causes of offshore wind turbines. A methodology towards conducting uncertainty analysis of FMEA results is developed to provide a new insight into a good understanding of FMEAs and their results. The developed uncertainty analysis methodology reveals that the proposed two-stage FMEA technique is adequate to reduce the uncertainty of FMEA results and is superior in failure analysis of offshore wind turbines. The application of the methodology can provide recommendations toward corrective actions and condition-based maintenance implementations.

Risk assessment in discrete production processes considering uncertainty and reliability: Z-number multi-stage fuzzy cognitive map with fuzzy learning algorithm

The Failure Mode and Effects Analysis (FMEA) technique due to its proactive nature can identify failures and their causes as well as potential effects, and provide preventive/controlling measures before they occur. Nevertheless, some of the shortcomings of the FMEA technique like lack of a mental framework for considering the relationships between risks, lack of systematic perspective in confronting with risks, and weakness of Risk Priority Number (RPN) score in mathematical basis and disregarding the uncertainty of problem reduce the reliability of the outputs. In this study, an approach based on the Multi-Stage Fuzzy Cognitive Map and the Z-number theory (Z-MSFCM) is proposed to simultaneously consider the concept of uncertainty and reliability in quantities of risk factors and the weights of causal relationships in the MSFCM. Besides, a novel learning approach for Z-MSFCM has been applied based on the combination of the Particle Swarm Optimization (PSO) and S-shaped transfer function (PSO-STF) to preserve the uncertain environment of the problem. The proposed approach has been applied in a manufacturing automotive parts company and results indicate that: first, Z-MSFCM by considering the causal relationships between risks and their uncertainty and reliability in comparison with traditional RPN can provide better process-oriented insight into the impact of risks on the system; and second, the PSO-STF has high potential in generating solutions with high separability compared to Nonlinear Hebbian Learning and PSO algorithms. To put it differently, the mentioned advantages of the proposed approach can help decision-makers to analyze the problem with high reliability.

Health and Safety Hazard Identification and Evaluation in Sun Air Reaserch Institiute of Ferdowsi University of Mashhad Using the FMEA Approach

Introduction: Renewable energies, such as wind and solar energy have attracted the attention of scientific, research, and industrial communities due to its fewer threats to the environment and human health. The purpose of this study was to identify and assess the safety and health risks of the Sun Air research institute of Ferdowsi University of Mashhad.
 Methods: In this descriptive cross-sectional study, the failure mode and effect analysis (FMEA) technique was used to identify and assess the risks of all Sun Air Research Institute of Ferdowsi University of Mashhad units in the second half of 2019. The HSE management team identified the risks of each unit separately and assessed the risks. All risks were prioritized following identification based on the range of priority numbers (RPN) method resulting from multiplication of the Occupancy, Severity, and Detectability components as well as the control measures.
 Results: The RPN ranged from 18 cases related to repetitive work in inspection operations to RPN=180 related to electric current leakage. Among the risks, three potential risks had higher priority, which included electric current leakage (RPN=180), inhalation of toxic substances (RPN=162), and falling from a height (RPN=150). Other risks were ranked next in priority.
 Conclusion: The results showed that use of FMEA technique was appropriate to identify and assess occupational risks in research and renewable energy production environments that lead to identification of risks at different levels. This helps the HSE team to eliminate or reduce the level of risks by providing control solutions with the priority of unacceptable risks.

Integrated Decision-Making Approach Based on SWARA and GRA Methods for the Prioritization of Failures in Solar Panel Systems under Z-Information

Encountering a problem or error in the final stages of providing products or services increases costs and delays scheduling. The key task is to ensure quality and reliability in the early stages of the production process and prevent errors from occurring from the beginning. Failure mode and effect analysis (FMEA) is one of the tools for identifying potential problems and their impact on products and services. The conventional FMEA technique has been criticized extensively due to its disadvantages. In this study, the concepts of uncertainty and reliability are considered simultaneously. The processes of weighting risk factors, prioritizing failures by using the stepwise weight assessment ratio analysis (SWARA)–gray relational analysis (GRA) integrated method based on Ζ-number theory and complete prioritization of failures are implemented. Crucial management indices, such as cost and time, are considered in addition to severity, occurrence and detection factors along with assigning symmetric form of the weights to them. This, in turn, increases the interpretability of results and reduces the decision-maker’s subjectivity in risk prioritization. The developed model is implemented on solar panel data with 19 failure modes determined by the FMEA team. Results show that the proposed approach provides a more complete and realistic prioritization of failures than conventional FMEA and fuzzy GRA methods do.

Comparison and Verification of Reliability Assessment Techniques for Fuel Cell-Based Hybrid Power System for Ships

In order to secure the safe operation of the ship, it is crucial to closely examine the suitability from the design stage of the ship, and to set up a preliminary review and countermeasures for failures and defects that may occur during the construction process. In shipyards, the failure mode and effects analysis (FMEA) evaluation method using risk priority number (RPN) is used in the shipbuilding process. In the case of the conventional RPN method, evaluation items and criteria are ambiguous, and subjective factors such as evaluator’s experience and understanding of the system operate a lot on the same contents, resulting in differences in evaluation results. Therefore, this study aims to evaluate the safety and reliability for ship application of the reliability-enhanced fuel cell-based hybrid power system by applying the re-established FMEA technique. Experts formed an FMEA team to redefine reliable assessment criteria for the RPN assessment factors severity (S), occurrence (O), and detection (D). Analyze potential failures of each function of the molten-carbonate fuel cell (MCFC) system, battery system, and diesel engine components of the fuel cell-based hybrid power system set as evaluation targets to redefine the evaluation criteria, and the evaluation criteria were derived by identifying the effects of potential failures. In order to confirm the reliability of the derived criteria, the reliability of individual evaluation items was verified by using the significance probability used in statistics and the coincidence coefficient of Kendall. The evaluation was conducted to the external evaluators using the reestablished evaluation criteria. As a result of analyzing the correspondence according to the results of the evaluation items, the severity was 0.906, the incidence 0.844, and the detection degree 0.861. Improved agreement was obtained, which is a significant result to confirm the reliability of the reestablished evaluation results.