Design FMEA for a diesel engine using two risk priority numbers

One of the prominent techniques in the field of Total Quality Management (TQM) is Failure Mode and Effects Analysis (FMEA). FMEA facilitates the recording of failures and analysing them to provide solutions for preventing their recurrence. During the early periods of FMEA evolution, there were two types namely Design FMEA and Process FMEA (PFMEA) in practice. In recent days, more types such as System FMEA, Service FMEA and Maintenance FMEA are being prescribed by the researchers. Meanwhile, a number of benefits of FMEA implementation have been reported. Yet, FMEA has not found its implementation in various fields. One among them is service field. This paper reports the examination of FMEA implementation in service industry. This direction of research led to the design of an improved model, named as 'Modified service FMEA'. Its implementation was examined in an Indian State Government owned passenger Transport Company. Despite certain practical hurdles, this exercise was successful in developing modified service FMEA table and pinpointing the seriousness of failures through the portrayal of Service Lost (SL) and Cost Lost (CL).

Design FMEA of electrical systems is a costly and labour intensive process. Ideally it would be done when the electrical system is first designed, and repeated whenever any change is made to the design. Because of the cost, this has not been possible in the past. This paper describes how an existing tool for automating electrical design failure mode and effects analysis (FMEA) can be augmented to make incremental design FMEA much less of a burden for the engineer. The tool is able to generate the effects for each failure mode and to assign significance values to the effects. The first time that it is run on a design, the engineer still has quite a lot of work to do, examining the results and deciding what actions need to be taken because of the FMEA. When a change is made to the circuit, the engineer runs the FMEA tool again and receives a new report. Because of the uniformity of the reports provided by the FMEA tool, it has proved possible to write software which sorts out the failure effects which have changed from the previous analysis and only report those results to the engineer. This makes examination of the repercussions of the incremental FMEA much less effort for the engineer, and makes it feasible to perform an incremental FMEA every time the design is amended.

<div class="htmlview paragraph">Fault tree analysis (FTA) evolved from the aerospace industry in the 1960’s. A fault tree is deductive logic model that is generated with a top undesired event in mind. FTA answers the question, “how can something occur?” as opposed to failure modes and effects analysis (FMEA) that is inductive and answers the question, “what if?” FTA is used in risk, reliability and safety assessments. FTA is currently being used by several industries such as nuclear power and chemical processing. Typically the automotive industries uses failure modes and effects analysis (FMEA) such as design FMEAs and process FMEAs. The use of FTA has spread to the automotive industry. This paper discusses the use of FTA for automotive applications. With the addition automotive electronics for various applications in systems such as engine/power control, cruise control and braking/traction, FTA is well suited to address failure modes within these systems. FTA can determine the importance of these failure modes from various perspectives such as cost, reliability and safety. A fault tree analysis of a car starting system is presented as an example.</div>

Purpose:Though FMEA (Failure Mode and Effects Analysis) is becoming more widely adopted for risk assessment in radiation therapy, to our knowledge it has never been validated against actual incident learning data. The objective of this study was to perform an FMEA analysis of an SBRT (Stereotactic Body Radiation Therapy) treatment planning process and validate this against data recorded within an incident learning system.Methods:FMEA on the SBRT treatment planning process was carried out by a multidisciplinary group including radiation oncologists, medical physicists, and dosimetrists. Potential failure modes were identified through a systematic review of the workflow process. Failure modes were rated for severity, occurrence, and detectability on a scale of 1 to 10 and RPN (Risk Priority Number) was computed. Failure modes were then compared with historical reports identified as relevant to SBRT planning within a departmental incident learning system that had been active for two years. Differences were identified.Results:FMEA identified 63 failure modes. RPN values for the top 25% of failure modes ranged from 60 to 336. Analysis of the incident learning database identified 33 reported near‐miss events related to SBRT planning. FMEA failed to anticipate 13 of these events, among which 3 were registered with severity ratings of severe or critical in the incident learning system. Combining both methods yielded a total of 76 failure modes, and when scored for RPN the 13 events missed by FMEA ranked within the middle half of all failure modes.Conclusion:FMEA, though valuable, is subject to certain limitations, among them the limited ability to anticipate all potential errors for a given process. This FMEA exercise failed to identify a significant number of possible errors (17%). Integration of FMEA with retrospective incident data may be able to render an improved overview of risks within a process.

Purpose: Omitting potential failure modes or inferencing inappropriate failure modes during failure mode and effects analysis (FMEA) implementation can lead to a lack of understanding of the causes of failures incurred or might lead to introducing inappropriate improvements. Therefore, it is critical to accurately infer the failure modes, which, in turn, requires selecting a suitable failure mode inference method because the results of such methods generally vary depending on the purpose and context of FMEA implementation.<BR>Methods: In this study, the advantages and disadvantages of functional and hardware approaches were compared and analyzed by applying them in mechanical and circuit components with respect to the implementation of component FMEA.<BR>Results: It was observed that the hardware approach can be more desirable for identifying the causes and mechanism of failures to prevent omissions and address the failure modes. However, when it becomes difficult to represent failure modes based solely on physical aspects, the functional approach can be used simultaneously with the hardware approach.<BR>Conclusion: Based on the aforementioned comparison and analysis, guidelines for selecting an effective FMEA method for implementing FMEA in organizations have been presented. However, this study focused only on component FMEA and did not consider system FMEA. Thus, the procedure for system FMEA, in terms of FMEA objectives and implementation, should also be examined.

The application and use of process and design FMEA (failure mode and effects analysis) in the British rail and automotive industries are considered. Guidelines from BS.5760 standard are reviewed, and associated problem areas discussed. These guidelines are based on risk priority numbers (RPNs) which are calculated for every recorded cause of failure. Since these guidelines do not address themselves to the computation of RPNs for all modes of failure for each item and module of the system, a methodology based on probability theory is developed to calculate RPNs for every recorded failure cause. The probabilistic approach presented is extended to calculate priority indices for every recorded failure mode, component, and for the total system. The authors introduce the idea of an ordered matrix FMEA where all qualitative and quantitative information recorded is stored in a pictorial and convenient way. The probabilistic and matrix approaches have been incorporated in a computer program implemented on an IBM PC. The program developed is used to identify efficiently all potential causes of failure. An illustrative example of its use is given. >

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.

Summary Such classic risk-assessment techniques as failure mode and effect analysis(FMEA) and fault-tree analysis (FTA) are in common use by engineers but have anumber of shortcomings. Dynamic simulation, a new computer technique, countersmany of these deficiencies by introducing a time element that accommodatesrealistic component-failure characteristics and testing and inspectionactivities. This paper demonstrates how FMEA and FTA have been used to helpdesign North Sea well completions and how dynamic simulation models complexsystems realistically to allow comparison of designs and to provide insightinto operating availability and component-testing frequencies. Introduction Both single- and dual-string gas-lift completions are commonly used in theNorth Sea, but it is not obvious which design provides greater safely andreliability. The single completion (Fig. 1) with annular safety valve issimpler to operate and to work over, but during normal production, it has alarge volume of high-pressure gas beneath the tubing hanger that acts on thehanger, tubing spool, production casing, and wellhead seals. The alternativedual-string design (Fig. 2), in which production is routed through the longstring and injection gas through the second or short string, reduces the gasinventory above the safety valve and removes the lift-gas pressure from theannulus. It is a more complicated system to operate and to maintain, however, and it has more component parts. To assist with the selection of completiontype, a conventional risk analysis was undertaken to rank the risks associatedwith each completion during both production and workover modes and to identifycritical components to allow for possible design changes. An FMEA provided aqualitative comparison of the two designs, and an FTA quantified the risks. While these static techniques successfully identified the critical components, they could not address such problems as changes in the well system withproblems as changes in the well system with time, undetected failures, effectsof inspection and maintenance, or components with failure distributions otherthan exponential. Use of a dynamic simulator, which simulates the performanceof the completion throughout many life cycles, provided a more realistic riskassessment and allowed sensitivity tests to be performed on maintenance andtest frequencies and undetected failures. We assumed that such conditions aswater cut and reservoir pressure were identical for both completion types. Theoretically, the influence of such conditions and the effect of varyingequipment sizes can be modeled by adjusting the reliability data, but eachspecific situation requires its own analysis. Reliability data were provided byRefs. 1 and 2, in addition to some proprietary data. Uncertainties are alwaysassociated with reliability data, and the application of the data isnecessarily based on judgment. Risks expressed numerically should be treated ascomparative rather than absolute. Note that, in the static and dynamic studiespresented here, different types of failure presented here, different types offailure distributions were used because more realistic distributions dmexponential ones were available for use in the dynamic simulation. Static Analysis FMEA. After definition of the two well systems to be compared (Figs. 1 and2), the first step in assessing the reliability of each system was to preparean FMEA. This involved identification of possible component failures and theirconsequences to provide a qualitative comparison of the inherent risks in eachsystem. To provide a basis for comparison of the FMEA for each well system, weprepared a list of very critical events (VCE's) prepared a list of verycritical events (VCE's) and critical events (CE's) to summarize each analysis. Events were defined as follows. VCE (Severity 1): loss of control of the well, possible loss of life, and evacuation necessary. CE (Severity 2): loss of oneor more safety barriers but not all barriers, evacuation possible, and majorworkover required. possible, and major workover required. Comparison of VCE'sand CE's for each system provided a qualitative assessment as to which systemwas likely to be more or less hazardous. FTA. FTA techniques previously applied to well-completion designs were usedin this study to provide a quantitative comparison of different designs. TheFTA is based on the component identification carried out in the FMEA, but theFTA differs from the FMEA because it is a top-down approach. Therefore, the topevent of interest (i.e., catastrophic wellhead-system failure) is defined and alogic tree is constructed of lower-level failures until the component-failurelevel is reached at the bottom of the tree. JPT P. 1374

Purpose: Failure Mode and Effects Analysis (FMEA) provides a proactive method of improving the quality and safety of treatments by identifying and correcting hazards points in the process of clinical care. FMEA, however, is often thought to be prohibitively labor intensive. This study outlines a method of streamlined FMEA, conducted with limited resources, and assesses its feasibility and effectiveness. Methods: FMEA was performed on the external beam service of a clinic treating approximately 650 patients per year on three linear accelerators. A facilitator and local team leader were identified, and a plan was developed to complete the exercise in four one‐ hour meetings as follows: 1 (core group), introduction and process mapping, 2 (all staff) identification of failure modes from expert user input, 3 (core group) scoring of failure modes according to an FMEA risk priority number, RPN, i.e. the product of severity, occurrence and detectability scores, and 4 (all staff) error proofing of the top‐five ranked failure modes. Results: Fifty‐ two failure modes were identified, 43 of which were scored and ranked. Specific interventions were developed for the five highest ranked failure modes. FMEA scoring after intervention indicated that the average RPN score for the top five modes decreased from 273 to 161 (p=0.03) while FMEA scoring of a control group of failure modes with no intervention did not show a significant change in RPN (p=0.07). The exercise was accomplished within the expected timeline and required 55 total hours of staff time and 20 hours of facilitator effort. Conclusion: Streamlined FMEA analysis is feasible with a relatively modest effort, and can reduce the risk profile of a facility. This may open the possibility of performing repeat FMEA on a regular basis.

Failure Mode and Effect Analysis: An Application in Reducing Risk in Blood Transfusion

To develop a proactive risk assessment model for human papillomavirus (HPV) genotyping testing by integrating Failure Mode and Effects Analysis (FMEA) with quality indicators (QIs), ensuring compliance with ISO 15189:2022 and improving diagnostic accuracy. A multidisciplinary team designed and performed detailed FMEA across pre-analytical, analytical, and post-analytical phases of HPV genotyping testing. To improve objectivity, we integrated Sigma metrics into the FMEA framework through a molecular diagnostics-specific model of QIs (MQI). The FMEA model systematically identified testing phases, potential failure modes, their effects, root causes, and existing controls. Risk was quantified using Severity, Occurrence (from 1-year QI data), and Detection scores (1-5 scale). Risk Priority Numbers (RPNs) were calculated (Severity×Occurrence×Detection) to prioritize failure modes, with mandatory interventions implemented for high-risk items (RPN≥40). Five high-risk failure modes (e.g., sample misidentification, data analysis errors) were identified and successfully mitigated to acceptable levels (RPN<40) through process optimization and standardization, achieving RPN reductions of 20-80 %. We established a molecular diagnostics-specific MQI, comprising 14pre-analytical, 25 analytical, and three post-analytical phase QIs. QI-based risk assessment of 35 evaluable QIs for HPV genotyping testing revealed one high-risk QI ("Incorrect results due to information system failures") and three medium-risk QIs, all of which were addressed through corrective actions. This study developed an integrated FMEA-QI model for HPV genotyping testing, establishing both a traditional FMEA framework and a molecular diagnostics-specific MQI. The combined approach improves risk assessment objectivity and enables multidimensional analysis compared to conventional methods.

Failure modes and effects analysis (FMEA) is a popular reliability tool in petroleum engineering. In FMEA, potential failure modes or corrective actions are evaluated, each assigned a Risk Priority Number (RPN) score, and prioritized for decision making. FMEA is also known as Failure Modes, Effects, and Criticality Analysis (FMECA), while focuses on failure modes prioritization. Despite of the popularity of FMEA and FMECA, it is not clear, how potential failure modes and corrective actions could be represented systematically, for effective decision making. In this paper, two new representations (i.e., a tree representation and a vector representation), for potential failure modes and corrective actions, are proposed. The tree representation for a potential failure mode allows its root cause(s), effect(s) and corrective action(s), together with their severity, occurrence and detection rating(s), to be represented as a three-layer tree model. The tree representation for a corrective action with similar contents is outlined too. The RPN model, together with its score, is represented as a node of the tree model. These tree models can also be represented as their associated equivalence layered-vector representations. In this paper, the usefulness of the proposed approaches is illustrated with benchmark FMEA worksheets pertaining to petroleum engineering.

Objective To consummate the cleaning process of medical power tool so as to improve the quality of cleaning of medical power tool. Methods The failure mode and effect analysis (FMEA) project group was built up in April 2016. The cleaning process of medical power tool was analyzed with the method of FMEA. The intervention was carried out aiming at high risk factor. Besides, the cleaning process of medical power tool was consummated and improved continually combining the detection results of ATP bioluminescence assay. The risk assessment of the cleaning process of medical power tool was carried out to determine high risk factors and formulate as well as implement improvement measures. The control effect of risk priority number (RPN) of cleaning on medical power tool and cleaning effect were compared before (March 2016) and after (September 2016) FMEA. Results The RPN of the cleaning process of medical power tool decreased from (218.17±89.14) before implementing FMEA to (33.26±9.54) after implementing FMEA. The qualified rate of ATP bioluminescence assay in washing quality increased from 68.6% to 98.5% before and after implementation with a significant difference (χ2=22.94, P<0.01) . Conclusions The application of FMEA can effectively control the risk factors of the cleaning process of medical power tool, improve the cleaning quality of power tool and reduce the opportunity for infection in patients. Key words: Failure mode and effect analysis; Cleaning; Medical power tool; Management

Objective To investigate IMRT planning process using the combined application of failure modes and effects analysis (FMEA) and fault tree analysis (FTA) by reference to the report of Task Group 100 of the AAPM, and stablish and optimize the quality. Methods A multidisciplinary team detailed the process mapping of IMRT planning using Eclipse TPS. The team evaluated the potential failure modes (FMs) of every process step. The evaluation was divided into two groups according to whether quality management (QM) was considered. For every FM, occurrence (O), severity (S) and detectability (D) by consensus were evaluated, and the product of O, S and D yielded the risk priority number (RPN), which permitted the ranking of the FMs. Finally, FTA was used to determine the root factors contributing to the riskiest failure modes. Results The IMRT plan process consisted of 10 major sub-processes and 33 steps, which amounted to 47 failure modes. For the group without quality management, the RPN of FMs was between 13.2-271.8, 27 of which had RPN≥80, and 18 FMs had S≥8. For the group with quality management, the RPN of FMs was between 11.2-158.4, 11 of which had RPN≥80. The difference of RPN between the two groups was statistically significant (RPN of the group without QM=101.17±66.34, RPN of the group with QM=59.54±35.64, t=8.501, P<0.05). Finally, FTA was used to determine the root factors contributing to the FMs, i. e., prescription dose definition and importing images. Conclusions The FMEA and FTA methods are operable and practical, which can systematically and comprehensively analyze the potential failures and risks existing in the process of IMRT plan. And the FMEA and FTA can contribute to establish and optimize the quality management program in radiotherapy. Key words: Intensity modulated radiotherapy; Failure modes and effects analysis; Failure tree analysis; Quality management

Failure Mode and Effects Analysis (FMEA) originated as an industrial engineering technique used for risk management and safety improvement of complex processes. In the context of radiotherapy, the AAPM Task Group 100 advocates FMEA as the framework of choice for establishing clinical quality management protocols. However, there is concern that widespread adoption of FMEA in radiation oncology will be hampered by the perception that implementation of the tool will have a steep learning curve, be extremely time consuming and labor intensive, and require additional resources. To overcome these preconceptions and facilitate the introduction of the tool into clinical practice, the medical physics community must be educated in the use of this tool and the ease in which it can be implemented. Organizations with experience in FMEA should share their knowledge with others in order to increase the implementation, effectiveness and productivity of the tool.This session will include a brief, general introduction to FMEA followed by a focus on practical aspects of implementing FMEA for specific clinical procedures including HDR brachytherapy, physics plan review and radiosurgery. A description of common equipment and devices used in these procedures and how to characterize new devices for safe use in patient treatments will be presented. This will be followed by a discussion of how to customize FMEA techniques and templates to one's own clinic. Finally, cases of common failure modes for specific procedures (described previously) will be shown and recommended intervention methodologies and outcomes reviewed. Learning Objectives: Understand the general concept of failure mode and effect analysis Learn how to characterize new equipment for safety Be able to identify potential failure modes for specific procedures and learn mitigation techniques Be able to customize FMEA examples and templates for use in any clinic