Desk-top computer database technique for integrated R&M analysis

For the reliability of micro inertial measurement unit (MIMU), the failure mode analysis and random vibration test are carried out. Firstly, based on the structure and function analysis of MIMU, the MIMU failure mode, effects and criticality analysis (FMECA) which is carried out to obtain the key failure modes, reliability critical parts and weak parts of MIMU. Secondly, The MIMU vibration step test with a wide-band (15Ηz-2ΚHz) is designed and carried out. It can be concluded that when the environmental stress is overloaded, the MIMU will have sudden failures such as information calculation errors. Under normal stress, gyroscope performance degradation and accelerometer performance degradation are the main failure modes of MIMU. The research can provide reference for MIMU reliability design and evaluation.

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.

This paper presents a tool for reliability and failure mode analysis based on an advanced version of the popular failure mode, effects and criticality analysis (FMECA) procedure. To help the analyst formulating efficiently effective criticality assessments of the possible causes of failure, the fuzzy logic technique is adopted. Particular attention has been devoted to support the maintenance staff with a fuzzy criticality assessment model easy to implement and design. To test the proposed methodology, an actual application concerning a process plant in milling field for human consumption flour is showed in the paper.

In HRC production at PT XYZ which is carried out continuously to pursue the production target will be in effect on the HRC production component with the risk management methods is Failure Mode, Effect and Criticality Analysis (FMECA). The purpose of this study is to identify the source of potential failure in HRC production, analyze the RPN value, and determine control recommendations. Data collection methods are carried out by means of direct observation of HRC production, interviews, and literature studies. The results on the risk assessment by the FMECA method are leakage and operational failure of the burner device with a RPN value of 420, the Refractory Brick Furnace crack of 324, the scale piece bounced off the 294 reservoir, the oil leak on the flat pass, the work roll, and the backup roll of 252 , damage to the roller table machine is 240, operational failure on the Looper engine of 240, and the failure of the HRC product binding manually is 240. Risk control in HRC production is the use of flame rod indicators, ultraviolet sensors, ultrasonic tests, installation of slab pieces cover, thermal infrared camera, LOTO use, quadratic linear inverse system or predictive control, distance guard, and periodic maintenance.

The failure mode, effect and criticality analysis (FMECA) is a famous bottom-up analytical method for quantifying and ranking critical failures of a product normally at its design stage. At present, as the importance of equipment maintenance increases in industry, FMECA is often applied in maintenance management. In practical cases, equipment is composed of different components, and its maintenance optimization should consider requirements of reliability, availability and costs together. This paper proposes the method called the extended failure mode, effect and criticality analysis (e-FMECA) for equipment maintenance management optimization on the basis of the FMECA theory. This method gives the procedure of analyzing the risks of different failure modes associated with reliability, availability and maintenance costs of equipment. In order to measure these risks of reliability, availability and maintenance costs quantitatively, a fuzzy risk priority method for e-FMECA is developed. Finally, the case study of the e-FMECA method in terms of metro rolling stocks is given. The proposed e-FMECA might be beneficial for selecting the maintenance strategies, maintenance polices and maintenance optimizing objects for maintenance management optimization.

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.

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.

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

Failure mode, effects and criticality analysis (FMECA) is a well-known reliability analysis tool for recognizing, evaluating and prioritizing the known or potential failures in system, design, and process. In conventional FMECA, the failure modes are evaluated by using three risk factors, severity (S), occurrence (O) and detectability (D), and their risk priorities are determined by multiplying the crisp values of risk factors to obtain their risk priority numbers (RPNs). However, the conventional RPN has been considerably criticized due to its various shortcomings. Although significant efforts have been made to enhance the performance of traditional FMECA, some drawbacks still exist and need to be addressed in the real application. In this paper, a new FMECA model for risk analysis is proposed by using an integrated approach, which introduces Z-number, Rough number, the Decision-making trial and evaluation laboratory (DEMATEL) method and the VIsekriterijumska optimizacija i KOmpromisno Resenje (VIKOR) method to FMECA to overcome its deficiencies in real application. The novelty of this paper in theory is that the proposed approach integrates the strong expressive ability of Z-numbers to vagueness and uncertainty information, the strong point of DEMATEL method in studying the dependence among failure modes, the advantage of rough numbers for aggregating experts’ diversity evaluations, and the strength of VIKOR method to flexibly model multi-criteria decision-making problems. Based on the integrated approach, the proposed risk assessment model can favorably capture and aggregate FMECA team members’ diversity evaluations and prioritize failure modes under different types of uncertainties with considering the failure propagation. In terms of application, the proposed approach was applied to the risk analysis of failure modes in offshore wind turbine pitch system, and it can also be used in many industrial fields for risk assessment and safety analysis.

The complexity of patient‐specific implants combined with the current limited expertise in reliability engineering and manufacturability in the additive manufacturing (AM) sector is posing a number of quality performance challenges. Worldwide medical device regulatory bodies are facing increasing pressure to devise adequate standards to ensure long‐term patient safety and product performance. The implementation of the Quality by Design (QbD) system to titanium 3D‐printed bone implants offers a proven system to ensure that products are designed and manufactured correctly from the beginning without errors. This article reports on the development of a failure mode, effects, and criticality analysis (FMECA) coupled with a 3D risk assessment approach. This integrated approach is based on a questionnaire performed with three industry firms and three university research groups with significant experience and expertise in medical device product development and/or research in this field. Research outcomes include a FMECA form containing 137 failure modes with AM materials, AM machine general, fabrication, electron beam melting machine, finishing, and design being as the most sensitive process areas in terms of product quality. We subsequently propose corresponding preventive and corrective strategies for risk mitigation. The approach forms part of the QbD system being developed by the authors specifically for additive manufactured titanium patient‐specific implants.

Fuzzy risk priority number assessment for solar gel battery manufacturing defects

Fourth generation maintenance philosophy and Quality management system 9001:2015, emphasis on the concept of risk based thinking. And it is one of the challenging part of maintenance department to adopt these philosophy by quantifying all the activities of the department. Risk rating based on reliability is one of the techniques to quantify the maintenance activities for those organizations, whose reliability of goods and services matter in the market. Hydropower sector is also among them whose reliability matters highly in consumer life standard. So this paper approaches to rate the risk of hydropower based on the reliability of components for the implementation of Risk-based maintenance. From the study of data recorded in log book of a small hydropower plant located in western part of Nepal, it was found that the overall reliability score of the plant was 0.9819, which was low as compared to other hydropower of Nepal, where turbine section of both the units of the plant found a higher risk score than other systems. Study adopted two tools i.e. Analytical Hierarchical based Fault Tree Analysis and Critical Analysis Technique to find the critical assets and Failure Mode and Effect Analysis for the rest of all to quantifying the risk in component level. The analysis shows that the cooling system as the critical asset for both the units and it contributes 72% and 64% in the reliability of the turbine section. The critical assets thus obtained was validated through the critical analysis technique. From which the risk score of cooling system found 90% in both the units. To analyze the risk in component level, Failure Mode and Effect Analysis of different sub component of critical assets was done. Among the sub-components of the cooling system, radial tube filter gets the highest risk score. Its components; cartridge, tube, and joints have the risk score of 450, 192, and 96 respectively. And from the critical analysis of the rest of the subsystems of uint-I and unit-II, components such as bearings, breaker of the transmission system, transmission equipment, excitation system and transformer of unit-I and breaker of the transmission system, transmission equipment and excitation system of unit-II are found to have higher critical score, hence, it is recommended to apply preventive activities on these components as per the manufacturer to reduce the risk that could be caused by their failure.

User experience (UX) is crucial for interactive system design. To improve UX, one method is to identify failure modes related to UX and then take action on the high-priority failure modes to decrease their negative impacts. For the UX of interactive system design, the failure modes under consideration are human errors or difficulties, and thus the risk factors concerning failure modes are subjective and even subconscious. Existing methods are not sufficient to deal with these issues. In this paper, a fuzzy failure mode and effect analysis (FMEA)-based hybrid approach is proposed to improve the UX of interactive system design. First, hierarchical task analysis (HTA) and systematic human error reduction and prediction approach (SHERPA) are combined to identify potential failure modes concerning UX. Subsequently, fuzzy linguistic variables are employed to assess the risk parameters of the failure modes, and the similarity aggregation method (SAM) is adopted to aggregate the fuzzy opinions. Then, on the basis of the aggregation results, fuzzy logic is adopted to compute the fuzzy risk priority numbers that can prioritize the failure modes. Finally, the failure modes with high priorities are considered for corrective actions. An in-vehicle information system was employed as a case study to illustrate the proposed approach. The findings indicate that, compared with other methods, our approach can provide more accurate results for prioritizing failure modes related to UX, and can successfully deal with the subjective and even subconscious nature of the risk factors associated with failure modes. This approach can be universally utilized to enhance the UX of interactive system design.

Risk assessment is a systematic method of identifying and preventing product design and production planning process problems. It is a critical step in the risk management procedure. Most current risk assessment tools use failure mode, effects, and criticality analysis (FMECA) to evaluate the risk of failure. The traditional FMECA method uses linguistic terms to represent the severity, occurrence, and detection on a numerical scale from 1 to 10. It cannot deal with the incomplete information of attribute data. In a high technology industry, in order to meet the market’s real demand and provide a suitable product, an original design manufacture (ODM) company offers customized design, production, and integration service to satisfied clients’ needs. However, there are many uncertainties, imprecise, vague, and subjective information in the process of collecting data of an ODM company that will increase the difficulty of risk assessment. It cannot be fully solved by the traditional FMECA method. Therefore, considering both tangible and intangible criteria, this paper proposes a soft technique for order preference by similarity to ideal solution (TOPSIS) approach to solve the risk assessment problem. At last, a case study of ODM notebook module development is provided to demonstrate this practicability and rationality of the proposed method under incomplete information. Compared with existing traditional RPN and TOPSIS methods, the proposed method can deal with group decision-making problems in a more efficient manner.

Risk assessment and management for supply chain networks: A case study