Failure Mode And Effects Analysis Results Research Articles

A new approach to risk assessment in failure mode and effect analysis based on engineering textual data

Failure Mode and Effect Analysis (FMEA) is a valuable tool for improving the quality of products and service systems. However, traditional FMEA methods require examining various engineering textual materials and attending multiple meetings, which can be time-intensive. Additionally, accurately evaluating the severity (S), occurrence (O), and detection (D) of failure modes is essential to ensure the accuracy of FMEA results. Despite efforts by researchers to improve the efficiency of FMEA, the evaluation of these risk factors (S, O, and D) still relies too heavily on a manual and inefficient process. To address these issues, this paper proposes a machine learning-enabled FMEA approach. This new approach combines the strengths of the BERT (Bidirectional Encoder Representations from Transformers) model in transforming textual failure descriptions into word vectors, the advantages of the VSM (Vector Space Model) in determining semantic similarity between different failure modes, and the merits of TOPSIS based on objective weights in handling multicriteria risk assessment. The proposed approach was applied to an actual case study for failure mode and effect analysis in auto parts processing. Comparisons between the proposed method and conventional FMEA were conducted to demonstrate the effectiveness of the new approach.

Enhancing Safety in AI-Driven Cone Beam CT-based Online Adaptive Radiation Therapy: Development and Implementation of an Interdisciplinary Workflow

PurposeThe emerging online adaptive radiotherapy (OART) treatment strategy based on cone-beam computed tomography allows for real-time replanning according to a patient's current anatomy. However, implementing this procedure requires a new approach across the patient's care path and monitoring of the "black box" adaptation process. This study identifies high-risk failure modes (FMs) associated with AI-driven OART and proposes an interdisciplinary workflow to mitigate potential medical errors from highly automated processes, enhance treatment efficiency, and reduce the burden on clinicians. Materials and MethodsAn interdisciplinary working group was formed to identify safety concerns in each process step using failure mode and effects analysis (FMEA). Based on the FMEA results, the team designed standardized procedures and safety checklists to prevent errors and ensure successful task completion. The Risk Priority Numbers (RPNs) for the top twenty FMs were calculated before and after implementing the proposed workflow to evaluate its effectiveness. Three hundred seventy-four adaptive sessions across five treatment sites were performed, and each session was evaluated for treatment safety and FMEA assessment. ResultsThe OART workflow has four components, each with 4, 8, 13, and 4 sequentially executed tasks and safety checklists. Site-specific template preparation, which includes disease-specific physician directives and Intelligent Optimization Engine template testing, is one of the new procedures introduced. The interdisciplinary workflow significantly reduced the RPNs of the high-risk FMs, with an average decrease of 110 (maximum reduction of 305.5 and minimum reduction of 27.4). ConclusionThis study underscores the importance of addressing high-risk FMs associated with AI-driven OART and emphasizes the significance of safety measures in its implementation. By proposing a structured interdisciplinary workflow and integrated checklists, the study provides valuable insights into ensuring the safe and efficient delivery of OART while facilitating its effective integration into clinical practice.

Evaluation of the sustainability of party logistics service providers with failure modes and effects analysis method: its application in a company

Purpose- The purpose of this research is to increase customer satisfaction and business performance in the sector to eliminate failure situations of the processing phases of the departments of the business and to develop the quality of the system. The objective of this research is to enhance customer satisfaction and business performance by addressing and eliminating failure situations in the processing phases of the business departments and improving the quality of the system. To achieve this, the study focuses on evaluating third-party and fourth-party logistics service providers in the context of sustainability. While there are existing approaches to evaluate party logistics under general headings, there is a lack of specific studies focusing on companies operating in a single field. Therefore, this study aims to fill that gap by examining the business evaluation criteria of third-party and fourth-party logistics service providers for a white goods company in Turkey. Methodology- During the research, the concept of third-party logistics has gained attention within the scope of sustainability, the competition among organizations providing this service has become more intense, as have customer expectations. The research utilizes Failure Mode and Effects Analysis (FMEA) and Pareto Analysis (PA) to assess the sustainability of the supply chain and batch logistics for the company. Findings- As a result of the research, the findings in the direction of quality improvement-development within the scope of the service provided by the business are presented. The initial phase of process enhancement for logistics operations begins with the FMEA results. Development initiatives may begin if improvement initiatives are maintained. Development activities and resources ought to be distributed in accordance with this level of priority with regard to the first work that has to be performed to enhance the process. Conclusion- All risk considerations might not be realized inside a particular process since there are only a limited number of possibilities to be assigned for enhancement prosperity. By conducting risk prevention studies in accordance with the priorities that will be decided upon each period, it is intended to produce continuous improvement. Overall, while it may not be possible to address every risk, prioritizing risks and conducting risk prevention studies can help organizations achieve continuous improvement and increase their chances of success. Keywords: Failure Mode and Effects Analysis (FMEA), Pareto Analysis (PA), party logistics, quality, supply chain, sustainability. JEL Codes: 030, N01, L15, L21, L84

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions.

Risk management in humanitarian supply chain based on FMEA and grey relational analysis

The COVID-19 pandemic has disrupted the humanitarian supply chain management (HSCM) necessary for delivering emergency items during the disaster. The combined effects of climate change and the pandemic uncover the vulnerabilities of humanitarian supply chain operations and highlight the importance of risk management. This study aimed to identify priority risk factors and proposed mitigating risk strategies of a local government that is at the forefront of relief operations. It used Grey Relational Analysis (GRA) method to validate the Failure Mode and Effect Analysis (FMEA) approach in identifying priority issues relating to the supply chain risks. This paper reveals that the results of FMEA and GRA are almost similar.

Road safety assessment and risks prioritization using an integrated SWARA and MARCOS approach under spherical fuzzy environment

There are a lot of elements that make road safety assessment situations unpredictable and hard to understand. This could put people's lives in danger, hurt the mental health of a society, and cause permanent financial and human losses. Due to the ambiguity and uncertainty of the risk assessment process, a multi-criteria decision-making technique for dealing with complex systems that involves choosing one of many options is an important strategy of assessing road safety. In this study, an integrated stepwise weight assessment ratio analysis (SWARA) with measurement of alternatives and ranking according to compromise solution (MARCOS) approach under a spherical fuzzy (SF) set was considered. Then, the proposed methodology was applied to develop the approach of failure mode and effect analysis (FMEA) for rural roads in Cosenza, southern Italy. Also, the results of modified FMEA by SF-SWARA-MARCOS were compared with the results of conventional FMEA. The risk score results demonstrated that the source of risk (human) plays a significant role in crashes compared to other sources of risk. The two risks, including landslides and floods, had the lowest values among the factors affecting rural road safety in Calabria, respectively. The correlation between scenario outcomes and main ranking orders in weight values was also investigated. This study was done in line with the goals of sustainable development and the goal of sustainable mobility, which was to find risks and lower the number of accidents on the road. As a result, it is thus essential to reconsider laws and measures necessary to reduce human risks on the regional road network of Calabria to improve road safety.

An Area-Based Metrics to Evaluate Risk in Failure Mode and Effects Analysis Under Uncertainties

Failure mode and effects analysis (FMEA) is a widely used, powerful tool to identify and assess potential failure modes in products and to make products more reliable. Due to the complexity of products and lack of knowledge, FMEA involves many uncertainties in practice. In previous studies, numerous modified FMEA methods based on fuzzy logic and Dempster-Shafer (D-S) evidence theory have been employed to address these uncertainties. These studies focus on how to handle uncertainties and to identify a more reliable prioritization of risk priority numbers (RPNs). However, studies have not sufficiently examined how many uncertainties are present in resulting RPNs. To better model and process various types of uncertainties in FMEA, two new area-based metrics are constructed in this paper. One is the interval area metric (IAM), which is used in RPN representation. The other is the dimensionless uncertainty metric (DUM), which is used to measure how many uncertainties there are in RPN. IAM is used to rank the risks in failure modes, and DUM is used to rank the uncertainties in failure modes. Then, an expert system is presented to qualitatively evaluate the DUM, which can help FMEA users intuitively judge whether further investigation should be performed to alleviate the epistemic uncertainties in each failure mode. Finally, a practical risk evaluation case regarding the grinding wheel system of a numerically controlled (NC) machine is provided to demonstrate the application and effectiveness of the proposed FMEA. The case study shows that the calculation programs of IAM and DUM do not require any assumptions or need to address conflict among experts. In addition, proposed method can not only give a more accurate rating of each failure mode, but also help designers intuitively see the uncertainty grade of each RPN, which is useful to help them understand FMEA results.

Interval type-2 fuzzy inference-based failure mode and effect analysis model in a group decision-making setting

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.

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.

Railway Dangerous Goods Transportation System Risk Assessment: An Approach Combining FMEA With Pessimistic–Optimistic Fuzzy Information Axiom Considering Acceptable Risk Coefficient

In this article, a new systemic risk assessment approach, which combines failure mode and effect analysis (FMEA) and pessimistic–optimistic fuzzy information axiom (POFIA) considering acceptable risk coefficient (ARC), is proposed to evaluate the risk of railway dangerous goods transportation system (RDNGTS). This approach transforms the system risk assessment problem into the rank problem of severity of risk factors affecting system security. The triangular fuzzy numbers (TFNs) are applied to score the severity of failures ( S ), portability of occurrence ( O ), and possibility of detection ( D ) for each RDNGTS risk subindicator. The information contents of S , O , and D are calculated for each risk subindicator, two models are applied to calculate the information contents: POFIA, and POFIA considering ARC (POFIA-ARC). The product of information contents of S , O , and D is used to replace the risk priority number of FMEA. entropy weight method is used to calculate the weight of each risk subindicator. The comparison among the FMEA-POFIA-ARC, FMEA-POFIA, FMEA, FMEA with TFNs is conducted based on the historical data of Chinese RDNGTS accidents from 1986 to 2017. Results show that the potential human risk should be paid more attention. Compared with the analysis results of statistical accident number, the results of the approaches proposed in this article (especially the FMEA-POFIA-ARC) are more reliable than the results of FMEA and FMEA TFNs combined approach.

Evaluation and improvement the safety of total marrow irradiation with helical tomotherapy using repeat failure mode and effects analysis

Background & purposeHelical tomotherapy has been applied to total marrow irradiation (HT-TMI). Our objective was to apply failure mode and effects analysis (FMEA) two times separated by 1 year to evaluate and improve the safety of HT-TMI.Materials and methodsA multidisciplinary team was created. FMEA consists of 4 main steps: (1) Creation of a process map; (2) Identification of all potential failure mode (FM) in the process; (3) Evaluation of the occurrence (O), detectability (D) and severity of impact (S) of each FM according to a scoring criteria (1–10), with the subsequent calculation of the risk priority number (RPN=O*D*S) and (4) Identification of the feasible and effective quality control (QC) methods for the highest risks. A second FMEA was performed for the high-risk FMs based on the same risk analysis team in 1 year later.ResultsA total of 39 subprocesses and 122 FMs were derived. First time RPN ranged from 3 to 264.3. Twenty-five FMs were defined as being high-risk, with the top 5 FMs (first RPN/ second RPN): (1) treatment couch movement failure (264.3/102.8); (2) section plan dose junction error in delivery (236.7/110.4); (3) setup check by megavoltage computed tomography (MVCT) failure (216.8/94.6); (4) patient immobilization error (212.5/90.2) and (5) treatment interruption (204.8/134.2). A total of 20 staff members participated in the study. The second RPN value of the top 5 high-risk FMs were all decreased.ConclusionQC interventions were implemented based on the FMEA results. HT-TMI specific treatment couch tests; the arms immobilization methods and strategy of section plan dose junction in delivery were proved to be effective in the improvement of the safety.

Lean Thinking in Banking Industry: Waste Identification on Credit Disbursement Process

Waste is not only found in the manufacturing industry but also in the service industry, especially in banking industry. This paper describes a comprehensive lean approach applied to the banking industry. Lean in banking industry is used to find waste along the flow of value. This paper focuses on finding waste in consumer loan disbursement process by classifying consumer credit loan activity into value added activity, necessary but non value added activity, and waste. Process Activity Mapping is used to describe all activities in consumer credit disbursement waste identification. The identification shows that total time of consumer credit disbursement activities is 7,461 minutes equal to 15,54 days. This means the average time required to complete the consumer credit process starts from the documents receipt to the disbursement of credit is 15,54 working days. Failure Mode and Effect Analysis (FMEA) is used to decide the level of factor causing waste. The results of FMEA shows the length of time required for consumer loan disbursement due to verification repetition process and waiting time of documents to processed.

Cross-sectional comparison of public-private partnerships in transport infrastructure development in Nigeria

PurposeAlthough scientific research community has shown considerable interest in identifying critical success factors (CSFs) for public-private partnership (PPP) projects, yet effort at assessing and compare CSFs within similar PPP infrastructure projects received scant attention. The purpose of this paper is to identify, assess, and compare the CSFs in PPP transport infrastructure projects.Design/methodology/approachThe study adopted literature review and three PPP case studies including structured interviews and review of documentary reports in each case study. The outcome of literature review provided a total list of 26 identified success factors, which was used to design a case study protocol using failure mode and effect analysis (FMEA) technique. FMEA was conducted on each 26 identified success factor to assess their criticality in the three PPP case studies.FindingsThe results of FMEA revealed a total of ten CSFs in the concession of the road; four CSFs in the concession of the airport; and eight CSFs in the concession of the seaport. Also, the cross-case analysis showed the three prevalent CSFs, this includes government involvement by providing guarantees, political support, and project economic viability.Practical implicationsThe study findings including lessons learnt in each case study would positively influence policy development towards PPP transport infrastructure projects and the manner in which partners (i.e. public and private sector) go about the development of PPP transport projects.Originality/valueThis research would help PPP stakeholders to focus their attention and priorities in managing the identified CSFs in achieving long-term success in PPP transport infrastructure projects implementation.

Fuzzy inference system application for failure analyzing in automobile industry

PurposeNowadays, quality is one of the most important key success factors in the automobile industry. Improving the quality is based on optimizing the most important quality characteristics and usually launched by highly applied techniques such as failure mode and effect analysis (FMEA). According to the literature, however, traditional FMEA suffers from some limitations. Reviewing the literature, on one hand, shows that the fuzzy rule-base system, under the artificial intelligence category, is the most frequently applied method for solving the FMEA problems. On the other hand, the automobile industry, which highly takes advantages of traditional FMEA, has been deprived of benefits of fuzzy rule-based FMEA (fuzzy FMEA). Thus, the purpose of this paper is to apply fuzzy FMEA for quality improvement in the automobile industry.Design/methodology/approachFirstly, traditional FMEA has been implemented. Then by consulting with a six-member quality assurance team, fuzzy membership functions have been obtained for risk factors, i.e., occurrence (O), severity (S), and detection (D). The experts have also been consulted about constructing the fuzzy rule base. These evaluations have been performed to prioritize the most critical failure modes occurring during production of doors of a compact car, manufactured by a part-producing company in Iran.FindingsFindings indicate that fuzzy FMEA not only solves problems of traditional FMEA, but also is highly in accordance with it, in terms of some priorities. According to results of fuzzy FMEA, failure modes E, pertaining to the sash of the rear right door, and H, related to the sash of the front the left door, have been ranked as the most and the least critical situations, respectively. The prioritized failures could be considered to facilitate future quality optimization.Practical implicationsThis research provides quality engineers of the studied company with the chance of ranking their failure modes based on a fuzzy expert system.Originality/valueThis study utilizes the fuzzy logic approach to solve some major limitations of FMEA, an extensively applied method in the automobile industry.

Comprehensive protocol of traceability during IVF: the result of a multicentre failure mode and effect analysis.

Can traceability of gametes and embryos be ensured during IVF? The use of a simple and comprehensive traceability system that includes the most susceptible phases during the IVF process minimizes the risk of mismatches. Mismatches in IVF are very rare but unfortunately possible with dramatic consequences for both patients and health care professionals. Traceability is thus a fundamental aspect of the treatment. A clear process of patient and cell identification involving witnessing protocols has to be in place in every unit. To identify potential failures in the traceability process and to develop strategies to mitigate the risk of mismatches, previously failure mode and effects analysis (FMEA) has been used effectively. The FMEA approach is however a subjective analysis, strictly related to specific protocols and thus the results are not always widely applicable. To reduce subjectivity and to obtain a widespread comprehensive protocol of traceability, a multicentre centrally coordinated FMEA was performed. Seven representative Italian centres (three public and four private) were selected. The study had a duration of 21 months (from April 2015 to December 2016) and was centrally coordinated by a team of experts: a risk analysis specialist, an expert embryologist and a specialist in human factor. Principal investigators of each centre were first instructed about proactive risk assessment and FMEA methodology. A multidisciplinary team to perform the FMEA analysis was then formed in each centre. After mapping the traceability process, each team identified the possible causes of mistakes in their protocol. A risk priority number (RPN) for each identified potential failure mode was calculated. The results of the FMEA analyses were centrally investigated and consistent corrective measures suggested. The teams performed new FMEA analyses after the recommended implementations. In each centre, this study involved: the laboratory director, the Quality Control & Quality Assurance responsible, Embryologist(s), Gynaecologist(s), Nurse(s) and Administration. The FMEA analyses were performed according to the Joint Commission International. The FMEA teams identified seven main process phases: oocyte collection, sperm collection, gamete processing, insemination, embryo culture, embryo transfer and gamete/embryo cryopreservation. A mean of 19.3 (SD ± 5.8) associated process steps and 41.9 (SD ± 12.4) possible failure modes were recognized per centre. A RPN ≥15 was calculated in a mean of 6.4 steps (range 2-12, SD ± 3.60). A total of 293 failure modes were centrally analysed 45 of which were considered at medium/high risk. After consistent corrective measures implementation and re-evaluation, a significant reduction in the RPNs in all centres (RPN <15 for all steps) was observed. A simple and comprehensive traceability system was designed as the result of the seven FMEA analyses. The validity of FMEA is in general questionable due to the subjectivity of the judgments. The design of this study has however minimized this risk by introducing external experts for the analysis of the FMEA results. Specific situations such as sperm/oocyte donation, import/export and pre-implantation genetic testing were not taken into consideration. Finally, this study is only limited to the analysis of failure modes that may lead to mismatches, other possible procedural mistakes are not accounted for. Every single IVF centre should have a clear and reliable protocol for identification of patients and traceability of cells during manipulation. The results of this study can support IVF groups in better recognizing critical steps in their protocols, understanding identification and witnessing process, and in turn enhancing safety by introducing validated corrective measures. This study was designed by the Italian Society of Embryology Reproduction and Research (SIERR) and funded by the Italian National Transplant Centre (CNT) of the Italian National Institute of Health (ISS). The authors have no conflicts of interest. N/A.

Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach

In this paper, an integrated framework is presented for the risk analysis of a compressor house unit (CHU) in a medium size coal fired thermal power plant located in northern part of India. Failure mode and effect analysis (FMEA) was done and the various components of the considered system were prioritised on the basis of RPN score. To overcome the limitations of traditional FMEA approach in risk ranking knowledge base approach namely fuzzy FMEA and grey relation analysis (GRA) were used. In fuzzy FMEA, a fuzzy decision support system (FDSS) was developed to compute the FRPN scores and the ranking of various components of the considered system has been done. The ranking results so obtained using fuzzy FMEA approach were compared with traditional FMEA ranking results. Further, to make an intelligent decision making of risky components of the considered system grey relation analysis (GRA) has been done using the same linguistic terms which were defined for fuzzy FMEA approach and the ranking results were compared with those of FMEA and fuzzy FMEA results. The analysis results would be useful to the system analyst to arrive at best decision making of risky components of the considered system and plan a suitable maintenance policy accordingly.

Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach

In this paper, an integrated framework is presented for the risk analysis of a compressor house unit (CHU) in a medium size coal fired thermal power plant located in northern part of India. Failure mode and effect analysis (FMEA) was done and the various components of the considered system were prioritised on the basis of RPN score. To overcome the limitations of traditional FMEA approach in risk ranking knowledge base approach namely fuzzy FMEA and grey relation analysis (GRA) were used. In fuzzy FMEA, a fuzzy decision support system (FDSS) was developed to compute the FRPN scores and the ranking of various components of the considered system has been done. The ranking results so obtained using fuzzy FMEA approach were compared with traditional FMEA ranking results. Further, to make an intelligent decision making of risky components of the considered system grey relation analysis (GRA) has been done using the same linguistic terms which were defined for fuzzy FMEA approach and the ranking results were compared with those of FMEA and fuzzy FMEA results. The analysis results would be useful to the system analyst to arrive at best decision making of risky components of the considered system and plan a suitable maintenance policy accordingly.

Failure Mode and Effect Analysis (FMEA) for Enhancing Reliability of Water Tube Boiler in Thermal Power Plant

Failure mode and effect analysis is one of the QS-9000 quality system requirement supplements, with a wide applicability in all industrial fields. FMEA is the inductive failure analysis instruments which can be defined as a methodical group of activities intended to recognize and evaluate the potential failure modes of a product/ process and its effects with an aim to identify actions which could eliminate or reduce the chance of the potential failure before the problem occur. The purpose of this paper is to evaluate the FMEA research and application in the Thermal Power Plant Industry. The research will highlight the application of FMEA method to water tubes (WT) in boilers with an aim to find-out all the major and primary causes of boiler failure and reduce the breakdown for continuous power generation in the plant. Failure Mode and Effect Analysis technique is applied on most critical or serious parts (components) of the plant which having highest Risk Priority Number (RPN). Comparison is made between the quantitative results of FMEA and reliability field data from real tube systems. These results are discussed to establish relationships which are useful for future water tube designs.

SU‐F‐P‐33: Combining Research and Professional Practice in the Clinical Setting: A Medical Physicist Personal Experience

Purpose:To initiate a discussion on the current and evolving role of Medical Physicists based on author's professional and research experience in patient safety and quality control.Methods:Several professionals of the departments of Medical Physics and Radiation Oncology, chiefly devoted to clinical tasks, began a research program on patient safety and quality control in a framework provided by the implementation of intraoperative radiotherapy (IORT). We performed studies on virtual simulation for IORT, in vivo dosimetry, failure mode and effect analysis (FMEA), statistical process control (SPC), and receiver operating characteristics of dosimetric equipment. This was done with the support of our research foundation and different grants while continuing with our departmental clinical routine involving about 1600 annual treatments with two linacs and different brachytherapy techniques.Results:We published 5 papers in international journals in the last two years. This author conducted a doctoral research which resulted in a dissertation in 2015. The extra time spent after treatments was essential to succeed. Funding and support achieved via our foundation played a crucial role; but this would have not been possible without punctual external mentoring and partnership. FMEA conclusions were able to be implemented only with staff commitment; however, conclusions concerning equipment cannot be easily communicated to manufacturers. These tasks required extra training in the appropriated methods.Conclusion:Research needed the support of a dedicated foundation, which would have been very difficult to obtain with the sole participation of our departments. FMEA and SPC results may need engagement of staff and manufacturers, respectively, hard to achieve without strong recommendations or even a regulatory framework. All these fields need evolution of Medical Physicists’ roles and additional training. Devotion to both clinical tasks and research could be unfeasible for Medical Physicists in the clinical setting without the appropriate rearrangement of their schedules and tasks.Supported by grants IPT‐300000‐2010‐3 and PI11/01659. ERDF funds and Spanish Government.

Minimizing treatment planning errors in proton therapy using failure mode and effects analysis.

Failure mode and effects analysis (FMEA) is a widely used tool to evaluate safety or reliability in conventional photon radiation therapy. However, reports about FMEA application in proton therapy are scarce. The purpose of this study is to apply FMEA in safety improvement of proton treatment planning at their center. The authors performed an FMEA analysis of their proton therapy treatment planning process using uniform scanning proton beams. The authors identified possible failure modes in various planning processes, including image fusion, contouring, beam arrangement, dose calculation, plan export, documents, billing, and so on. For each error, the authors estimated the frequency of occurrence, the likelihood of being undetected, and the severity of the error if it went undetected and calculated the risk priority number (RPN). The FMEA results were used to design their quality management program. In addition, the authors created a database to track the identified dosimetric errors. Periodically, the authors reevaluated the risk of errors by reviewing the internal error database and improved their quality assurance program as needed. In total, the authors identified over 36 possible treatment planning related failure modes and estimated the associated occurrence, detectability, and severity to calculate the overall risk priority number. Based on the FMEA, the authors implemented various safety improvement procedures into their practice, such as education, peer review, and automatic check tools. The ongoing error tracking database provided realistic data on the frequency of occurrence with which to reevaluate the RPNs for various failure modes. The FMEA technique provides a systematic method for identifying and evaluating potential errors in proton treatment planning before they result in an error in patient dose delivery. The application of FMEA framework and the implementation of an ongoing error tracking system at their clinic have proven to be useful in error reduction in proton treatment planning, thus improving the effectiveness and safety of proton therapy.