Effortless incremental design FMEA

Failure Modes and Effects Analysis (FMEA) is an effective technique for identifying and eliminating possible failures. FMEA has been widely used in the manufacturing industry and in various phases of the life cycle of a product. The aim of FMEA is to ensure high quality and reliability. Design FMEA (DFMEA) is used to identify and analyze potential failure modes and their associated causes for product design. It is a verification activity that can help avoid a large percentage of product design problems before the design is finalized. When dealing with new generations of a product including design changes on the basis of existing ones, DFMEA of earlier generations can be used as a reference for such design modifications. Traditionally, criticality or risk assessment in FMEA is performed by developing a risk priority number (RPN). Failure modes that have high RPN values are assumed to be more critical and deserve more attention. In this paper, we propose a method to assess failure modes in DFMEA considering two RPNs for diesel engines. In addition to the traditional RPN, a new RPN is proposed. A diesel engine cylinder head is used as an example to illustrate the application of the two RPNs in a design FMEA.

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).

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.

<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.

With the publication of the quality guideline ICH Q9 "Quality Risk Management" by the International Conference on Harmonization, risk management has already become a standard requirement during the life cycle of a pharmaceutical product. Failure mode and effect analysis (FMEA) is a powerful risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to biopharmaceutical processes brings about some difficulties. The proposal presented here is intended to serve as a brief but nevertheless comprehensive and detailed guideline on how to conduct a biopharmaceutical process FMEA. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. The application for such a biopharmaceutical process FMEA is widespread. It can be useful whenever a biopharmaceutical manufacturing process is developed or scaled-up, or when it is transferred to a different manufacturing site. It may also be conducted during substantial optimization of an existing process or the development of a second-generation process. According to their resulting risk ratings, process parameters can be ranked for importance and important variables for process development, characterization, or validation can be identified. Health authorities around the world ask pharmaceutical companies to manage risk during development and manufacturing of pharmaceuticals. The so-called failure mode and effect analysis (FMEA) is an established risk analysis tool that has been used for decades in mechanical and electrical industries. However, the adaptation of the FMEA methodology to pharmaceutical processes that use modern biotechnology (biopharmaceutical processes) brings about some difficulties, because those biopharmaceutical processes differ from processes in mechanical and electrical industries. The proposal presented here explains how a biopharmaceutical process FMEA can be conducted. It includes a detailed 1-to-10-scale FMEA rating table for occurrence, severity, and detectability of failures that has been especially designed for typical biopharmaceutical processes. With the help of this guideline, different details of the manufacturing process can be ranked according to their potential risks, and this can help pharmaceutical companies to identify aspects with high potential risks and to react accordingly to improve the safety of medicines.

With increasing deployment, complexity, and sophistication of equipment and related processes within the clinical imaging environment, system failures are more likely to occur. These failures may have varying effects on the patient, ranging from no harm to devastating harm. Failure mode and effect analysis (FMEA) is a tool that permits the proactive identification of possible failures in complex processes and provides a basis for continuous improvement. This overview of the basic principles and methodology of FMEA provides an explanation of how FMEA can be applied to clinical operations in a radiology department to reduce, predict, or prevent errors. The six sequential steps in the FMEA process are explained, and clinical magnetic resonance imaging services are used as an example for which FMEA is particularly applicable. A modified version of traditional FMEA called Healthcare Failure Mode and Effect Analysis, which was introduced by the U.S. Department of Veterans Affairs National Center for Patient Safety, is briefly reviewed. In conclusion, FMEA is an effective and reliable method to proactively examine complex processes in the radiology department. FMEA can be used to highlight the high-risk subprocesses and allows these to be targeted to minimize the future occurrence of failures, thus improving patient safety and streamlining the efficiency of the radiology department.

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.

Failure mode and effect analysis (FMEA) is a risk management technique frequently applied to enhance the system performance and safety. In recent years, many researchers have shown an intense interest in improving FMEA due to inherent weaknesses associated with the classical risk priority number (RPN) method. In this study, we develop a new risk ranking model for FMEA based on soft set theory and COPRAS method, which can deal with the limitations and enhance the performance of the conventional FMEA. First, trapezoidal fuzzy soft set is adopted to manage FMEA team members’ linguistic assessments on failure modes. Then, a modified COPRAS method is utilized for determining the ranking order of the failure modes recognized in FMEA. Especially, we treat the risk factors as interdependent and employ the Choquet integral to obtain the aggregate risk of failures in the new FMEA approach. Finally, a practical FMEA problem is analyzed via the proposed approach to demonstrate its applicability and effectiveness. The result shows that the FMEA model developed in this study outperforms the traditional RPN method and provides a more reasonable risk assessment of failure modes.

Failure mode and effects analysis using fuzzy evidential reasoning approach and grey theory

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. >

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

Failure Mode and Effects Analysis (FMEA) can be used as a structured method to prioritize all possible vulnerable areas (failure modes) for design review of safety-critical supervisory control and data acquisition (SCADA) Systems. However, the traditional RPN based FMEA has some inherent limitations. Thus the main purpose of this study was to propose a new pattern recognition based FMEA to evaluate, prioritize and correct a SCADA system’s failure modes. In the new FMEA method, a vague set based Risk Priority Number (RPN) is proposed to measure safety risk status of failure modes and a rule based pattern recognition method is also proposed to prioritize action priorities of failure modes. Finally, a case study was conducted to demonstrate that the proposed new FMEA method is not only capable of addressing its inherent problems but also is effective and efficient to be used as the basis for continuous improvement of a safety-critical SCADA system.

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