A Functional Verification based Fault Injection Environment

Blasting operation is the main unit of ore production in open-pit mining cycle. Detonation energy is mostly released as ground vibration using Rayleigh waves that extensively damage nearby infrastructures within or outside of a mining area. This work introduces a new hybrid approach based on the integration of fuzzy failure mode and effects analysis with the fuzzy slack-based measure model form of data envelopment analysis; this approach prioritizes and analyzes ground vibration risks on open-pit infrastructures. The approach uses a special risk prioritization algorithm by failure mode and effects analysis and data envelopment analysis under fuzzy conditions. In the fuzzy slack-based measure part of the approach, α-level cut method is used to prepare the necessary data. An open-pit copper mine is exemplified to present the approach in detail and analyze ground vibration risk. Potential failure modes, consist of infrastructures and blasting sources, are specified on the map on the mine area. Results indicate that blasting at the point of 3 imposes the highest ground vibration risk on the industrial site located within the final pit limits. The failure modes of water treatment site, crusher, gas station, explosive storage, switchgear, thickener site, power station, and processing plant have other priorities.

Objective To analyze the risks of hypertonic glucose injection ( 20%) (hypertonic glucose) in clinical application using failure mode and effect analysis (FMEA) method and improve the relevant management measures. Methods A study group on risk prevention strategies of hypertonic glucose was established in the First People′s Hospital of Hefei. Domestic and foreign literature on medication error cases, preventive measures or management strategies, and data from questionnaires and on-the-spot investigations were collected. The failure modes in the process of hypertonic glucose application were analyzed and assessed. Severity (S), frequency of occurrence (O), and likelihood of detection (D) of the risks related to these failure modes were scored and the risk priority numbers (RPN) were determined. The failure modes with higher RPN were screened out and the corresponding prevention strategies were formulated. The incidence of medication errors and potential hazards, cognition rate of knowledge on drugs, qualification rate of drug storage, and satisfaction rate for management measures before and after the improvement were analyzed comparatively. Results According to the results from literature search, questionnaires, and on-the-spot investigations, a total of 24 failure modes were found in the 6 links relevant with hypertonic glucose application in clinical, which included links of prescription writing and delivery, pharmacist dispensing, nurse dispensing, post-administration monitoring, drug management, and information technology. Ranking the RPN from high to low, 7 major failure modes with higher RPN were selected from the 24 failure modes, including the existence of drugs with similar name and drugs with similar package, wrong drug dispensing, absence of post-administration monitoring, lack of marked labels for drugs, garbles caused by lack of medication knowledge, and untimely record and report of medication errors. The incidences of medication errors and potential safety hazards before and after management improvement were 3.2% (7/216) and 0.4% (1/234), respectively and the difference had no statistical significance (P=0.058). The average cognitive rates of physicians, nurses and pharmacists before and after the improvement were 65.2%(15/23), 54.2%(13/24), 67.9%(19/28) and 95.8%(23/24), 91.7%(22/24), 95.5%(21/22), respectively and the differences were statistically significant (P<0.05 for all). The qualification rates of drug storage before and after management improvement were 70.2%(151/215) and 98.7%(230/233), respectively and the difference was statistically significant (P<0.05). The average satisfaction degrees of medical staff with hyperosmotic glucose management were 81.3%(61/75) and 95.7%(65/70), respectively before and after the management improvement and the difference was statistically significant (P<0.05). Conclusion Failure modes in the process of hypertonic glucose use can be effectively identified using the FMEA method, and the risk events can be effectively reduced after the corresponding improvement measures. Key words: Healthcare failure mode and effect analysis; Glucose solution, hypertonic; Hospital administration; High-alert medicine

The COVID-19 outbreak is of great concern due to the high rates of infection and the large number of deaths worldwide. In this paper, we considered a Bayesian inference and failure mode and effects analysis of the modified susceptible-exposed-infectious-removed model for the transmission dynamics of COVID-19 with an exponentially distributed infectious period. We estimated the effective reproduction number based on laboratory-confirmed cases and death data using Bayesian inference and analyse the impact of the community spread of COVID-19 across the United Kingdom. We used the failure mode and effects analysis tool to evaluate the effectiveness of the action measures taken to manage the COVID-19 pandemic. We focused on COVID-19 infections and therefore the failure mode is taken as positive cases. The model is applied to COVID-19 data showing the effectiveness of interventions adopted to control the epidemic by reducing the reproduction number of COVID-19. Results have shown that the combination of Bayesian inference, compartmental modelling and failure mode and effects analysis is effective in modelling and studying the risks of COVID-19 transmissions, leading to the quantitative evaluation of the action measures and the identification of the lessons learned from the governmental measures and actions taken in response to COVID-19 in the United Kingdom. Analytical and numerical methods are used to highlight the practical implications of our findings. The proposed methodology will find applications in current and future COVID-19 like pandemics and wide quality engineering.

Aims: The growth of population and people’s need for health care have increased medical wastes productions. The aim of this study is the hazardous waste risk assessment of hospital by using of two methods: failure mode and effects analysis and preliminary hazard analysis (PHA). Methods: The first, worksheets related to fracture mode and effects analysis were distributed among hospital experts and then the potential hazard for the type of wastes was determined. Results: At method of failure mode and effects analysis, 8 cases had the risk priority number &gt;100 and according to the results of the PHA, the sharp, infectious, human anatomical, chemical/pharmaceutical, and radioactive wastes, respectively, had a high potential of risk. Conclusion: failure mode and effects analysis can be used as a new approach to preventing errors and improving processes with the aim of increasing safety and reducing of probability danger in long term.

BackgroundMedical ultrasound device has been more and more widely used in the hospital and Its safety risk is significantly increased when failures occur. However, there is a lack of quantitative risk assessments of different types of failure modes for medical ultrasound device. This study utilizes a failure mode, effect and criticality analysis (FMECA) approach for quantitative risk evaluation of different failure modes for ultrasound devices.MethodsThe 4216 medical ultrasound device failure records at various hospitals were investigated. A failure mode and effect analysis method was developed for the quantitative evaluation of the risks of different failure modes. Visual correlation analysis was conducted for all categories to identify the causes of various failure modes. Based on the severity, occurrence and detectability of the failure causes determined, the risk priority number (RPN) for each failure mode was back-calculated through the obtained tracking diagram.ResultsThe failure modes of unclear images, unable to power on and dark shadows on an image had the highest RPNs. One failure mode could be caused by various factors, and the failure location was not necessarily related to the cause of the failure.ConclusionsThis quantitative approach more accurately evaluated the risks of different failure modes, and the results of the corresponding analysis of failure modes and causes could support the rapid determination of the causes of failures in clinical practice.

Objective To explore the effect of failure mode and effect analysis (FMEA) method in strengthening the management of critical value of blood specimens in hospital. Methods According to the procedures of FMEA, critical values management team were established, drew flow chart of generating test specimens and crisis value, explored the potential failure mode and failure cause, analyzed the severity, possibility and critical value, and formulate and implement countermeasures according to the research outcome. Results The crisis value of the failure mode fell from 1 320 to 151 after the implementation of FMEA, and rate of decline was 88.56% ; number of unqualified blood samples decreased from 510 cases to 78 cases, and the difference is statistically significant (χ2=407.27, P < 0.05); critical value processing defects decreased from 85 cases (a total of 236 cases) to 3 cases (a total of 229 cases), and the difference was statistically significant (χ2=91.26, P < 0.05). Conclusions FMEA method is helpful to improve the accuracy and precision of critical value management of clinical blood samples, so as to ensure the safety of patients in hospitals. Key words: Failure mode and effect analysis; Critical value management

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.

Purpose:To optimize the clinical processes of radiotherapy and to reduce the radiotherapy risks by implementing the powerful risk management tools of failure mode and effects analysis(FMEA) and PDCA(plan‐do‐check‐act).Methods:A multidiciplinary QA(Quality Assurance) team from our department consisting of oncologists, physicists, dosimetrists, therapists and administrator was established and an entire workflow QA process management using FMEA and PDCA tools was implemented for the whole treatment process. After the primary process tree was created, the failure modes and Risk priority numbers(RPNs) were determined by each member, and then the RPNs were averaged after team discussion.Results:3 of 9 failure modes with RPN above 100 in the practice were identified in the first PDCA cycle, which were further analyzed to investigate the RPNs: including of patient registration error, prescription error and treating wrong patient. New process controls reduced the occurrence, or detectability scores from the top 3 failure modes. Two important corrective actions reduced the highest RPNs from 300 to 50, and the error rate of radiotherapy decreased remarkably.Conclusion:FMEA and PDCA are helpful in identifying potential problems in the radiotherapy process, which was proven to improve the safety, quality and efficiency of radiation therapy in our department. The implementation of the FMEA approach may improve the understanding of the overall process of radiotherapy while may identify potential flaws in the whole process. Further more, repeating the PDCA cycle can bring us closer to the goal: higher safety and accuracy radiotherapy.

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.

This paper introduces a formulation of appropriate risk estimation methods that can be used for improving of processes in the electronics area. Two risk assessment methods have been chosen with regard to the specifics of adhesive joining based on electrically conductive adhesives. The paper provides a combination of a failure mode and effect analysis (FMEA) and fault tree analysis (FTA) for optimizing of the joining process. Typical features and failures of the process are identified. Critical operations are found and actions for avoiding failures in these actions are proposed. A fault treehas been applied to the process in order to get more precise information about the steps and operations in the process, and the relations between these operations. The fault tree identifies potential failures of the process. Then the effects of the failures have been estimated by the failure mode and effect analysis method. All major differences between failure mode and effect analysis and fault tree analysis are defined and there is a discussion about how to use the two techniquescomplement each other and achieve more efficient results.

Risk evaluation is an important part of the product design and product manufacturing process; it entails the pursuit of the highest product quality and preventing failure under the constraints of limited resources. The failure mode and effects analysis approach is one of the most widely applied risk evaluation tools that uses the product of the three risk elements of product failure items, severity, occurrence probability, and detection probability, to calculate the risk priority number, the priority of failure risk. However, the typical failure mode and effects analysis method ignores the consideration of objective weights, which may lead to incorrect evaluation results. Moreover, the method of expressing information about product failure provided by experts also directly affects the results of risk assessment. To comprehensively assess the risk of product failure, in this study, the hybrid of the Fermatean fuzzy set and entropy method was used to prioritize product failure items risk. This study used a service failure mode and effects analysis numerical example of self-service electric vehicles to illustrate and test the correctness of the proposed new hybrid Fermatean fuzzy set and entropy method. The mathematical operation results were also compared with the listing of different calculation methods. The test results prove that the proposed new hybrid Fermatean fuzzy set and entropy method can fully consider the cognitive information provided by experts to provide more accurate risk ranking results of failure items.

BackgroundDoor-to-balloon (D2B) time is an important factor in the outcome of ST segment elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention. We aimed to use failure mode and...

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.

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

This study proposes a risk analysis approach for complex healthcare processes that combines qualitative and quantitative methods to improve patient safety. We combine Healthcare Failure Mode and Effect Analysis with Computer Simulation (HFMEA-CS), to overcome widely recognised HFMEA drawbacks regarding the reproducibility and validity of the outcomes due to human interpretation, and show the application of this methodology in a complex healthcare setting. HFMEA-CS is applied to analyse drug adherence performance in the surgical admission to discharge process of pheochromocytoma patients. The multidisciplinary team identified and scored the failure modes, and the simulation model supported in prioritisation of failure modes, uncovered dependencies between failure modes, and predicted the impact of measures on system behaviour. The results show that drug adherence, defined as the percentage of required drugs received at the right time, can be significantly improved with 12%, to reach a drug adherence of 99%. We conclude that HFMEA-CS is both a viable and effective risk analysis approach, combining strengths of expert opinion and quantitative analysis, for analysing human-system interactions in socio-technical systems. Practitioner summary: We propose combining Healthcare Failure Mode and Effects Analysis with Computer Simulation (HFMEA-CS) for prospective risk analysis of complex and potentially harmful processes, to prevent critical incidents from occurring. HFMEA-CS combines expert opinions with quantitative analyses, such that the results are more reliable, reproducible, and fitting for complex healthcare settings.