Improvement of productivity has become an important goal for mining industries in order to meet the expected targets of production and increased price competitiveness. Productivity can be improved in ...different ways. The effective utilization of men and machinery is one such way. Equipment is prone to numerous unexpected potential failures during its operation. Failure Mode and Effect Analysis (FMEA) is one of the suitable techniques of reliability modeling used to investigate the failure behavior of a complex system. In conventional FMEA, the risk level of failures, a ranking of failures and prioritization of necessary actions is made on the basis of estimated Risk Priority Number (RPN). While this approach is easy and uncomplicated, there are a few flaws in acquiring the best approximation of the failure. The estimation of RPN is made by multiplying the Severity (S), Occurrence (O) and Detection (D) alone and irrespective of the degree of importance of each input. Hence, a new risk management approach known as the Fuzzy rule base interface system was proposed in this research in order to mitigate the failures. Fuzzy FMEA is designed in order to acquire the highest Fuzzy RPN value which will be used as the focus of enhancements to reduce the probability of occurrence of some kind of failure for a second time. This study focused on the Root Cause Analysis (RCA) of underground mining machinery such as Load-Haul-Dumper (LHD). 16 potential risks of various sub-system breakdowns were identified in Fuzzy FMEA. The highest value of RPN 168 (for potential failure mode-F9) was obtained for the electrical subsystem (SSE), as was the highest FRPN 117 (F9). There is a difference between the RPN and FRPN values. The FRPN value is obtained from Fuzzy field generation with consideration of the degree of importance of the given input data. In addition, the recommendations were made based on the analysis to reduce the uneven occurrence of failures.
•Helps to identify the potential failure models and reasons for the occurrence of failures.•Prioritize the various failure modes on the basis of Risk-Priority-Number (RPN) value.•Assess the recommended actions for possible design modifications to reduce the failures.•Documentation of FMEA data sheet will be used as the historical data for future reference.•To overcome the deficiencies of FMEA an accurate methodology called Fuzzy FMEA was introduced.
•Fuzzy logic approach is preferable to fix the drawbacks for reprioritization of the Risk Priority Number (RPN).•Fuzzy logic could reduce the drawback of occurred Traditional FMEA in evaluation and ...prioritization of failures.•The application of using Fuzzy FMEA in the emergency department can be adopted suitably. All of members were able to assess dependently without any bias from the team members.•Fuzzy FMEA can be applied for the first time to improve decision making process in an emergency department of a public hospital.
Hospitals are one of the important service industries of health care for patients. The emergency department is the heart of every hospital, because the errors or failures occurring in it will significantly affect the safety of patients and the goodwill of the hospital. Therefore, emergency departments should be monitored carefully. This study proposed the application of Fuzzy failure mode and effects analysis (FMEA) for prioritization and assessment of failures that likely occur in the working process of an emergency department. All individuals were assessed independently without the interference of team members. In addition, this method could reduce the limitations of traditional FMEA. The prioritization of risks could also help the emergency department to choose corrective actions wisely. In conclusion, the Fuzzy FMEA method was found to be suitably adopted in the emergency department. Finally, this method helped to increase the level of confidence on hospitals.
•Hazards of a sterilization unit of a large hospital determined.•Number of FMEA classes was increased from 3 to 5.•Fuzzy FMEA rules for the sterilization unit were created.•Fuzzy FMEA was applied in ...a sterilization unit.•Classic FMEA and fuzzy FMEA were compared.
Global system pressures prompt hospitals to consider the risk factors of the healthcare system. The sterilization unit is a focal point of the healthcare system in regards to risk factors, and these units should be properly managed. Therefore, to study risk factors in sterilization units, we utilized failure mode and effects analysis (FMEA), which is a proactive risk assessment method for examining all failure modes and eliminating or reducing the highest risk priority failures. In this study, a 5×5 matrix and both classical and fuzzy approaches of FMEA were developed for a sterilization unit to assess and identify the hazards discussed in prior studies and new hazards discovered during this study. The method proposed in this study provided both accurate risk assessments and effective responses to those risks. Finally, a case study of the sterilization unit of a large hospital is presented to demonstrate the effectiveness of the proposed methods.
Failure mode and effect analysis (FMEA) is a well-known engineering technique to recognize and reduce possible failures for quality and reliability improvement in products and services. It is a ...group-oriented method usually conducted by a multidisciplinary and cross-functional expert panel. In this paper, we explore two key issues inherent to the FMEA practice: the representation of diversified risk assessments of FMEA team members and the determination of priority ranking of failure modes. Specifically, a framework integrating cloud model, a new cognitive model for coping with fuzziness and randomness, and preference ranking organization method for enrichment evaluation (PROMETHEE) method, a powerful and flexible outranking decision making method, is developed for managing the group behaviors in FMEA. Moreover, FMEA team members' weights are objectively derived taking advantage of the risk assessment information. Finally, we illustrate the new risk priority model with a healthcare risk analysis case, and further validate its effectiveness via sensitivity and comparison discussions.
Hydraulic systems are widely used in the aeronautic, machinery, and energy industries. The functions that these systems perform require high reliability, which can be achieved by examining the causes ...of possible defects and failures and by taking appropriate preventative measures. One of the most popular methods used to achieve this goal is FMEA (Failure Modes and Effects Analysis), the foundations of which were developed and implemented in the early 1950s. It was systematized in the following years and practically implemented. It has also been standardized and implemented as one of the methods of the International Organization for Standardization (ISO) 9000 series standards on quality assurance and management. Apart from wide application, FMEA has a number of weaknesses, which undoubtedly include risk analysis based on the RPN (Risk Priority Number), which is evaluated as a product of severity, occurrence, and detection. In recent years, the risk analysis has been very often replaced by fuzzy logic. This study proposes the use of matrix analysis and statistical methods for performing simplified RCA (Root Cause Analysis) and for classification potential failures for a variable delivery vane pump. The presented methodology is an extension of matrix FMEA and allows for prioritizing potential failures and their causes in relation to functions performed by pump components, the end effects, and the defined symptoms of failure of the vane pump.
In product manufacture, the high failure rate problem of produce product is the number of product defects. Several types of defects have a high enough percentage. To solve this problem, we need to ...identify the failures and to get the assessment information of the three risk factors. Our research using the traditional FMEA method at the production of Wiring Harness products to shows the current condition of various modes of failure in those areas. This study focuses on implementing fuzzy FMEA to identify the potential risks that may occur along with the assembling of the Wiring Harness process. The fuzzy FMEA approach is preventing product and process problems before they occur, this paper is also expected to result in some mitigation effort that can be applied to improve the Wiring Harness production process. With the Fuzzy FMEA method, we have found the highest FRPN value that shows the highest defect such as damage insulation is 8.5, damage terminal is 8.5, and the damaged part is 8.5 and the highest RPN from the traditional FMEA is damage insulation (324). To solve this problem, we propose to use the fishbone diagram and give suggestions for improvements to the highest failure modes that are damaged insulation.
Improving customer satisfaction is as important as reducing risk associated with product upgrades. The quality function deployment (QFD), as a customer-centric tool, is widely used for product ...improvement. The failure mode and effect analysis (FMEA) method is recognized as a reliability management tool. Considering the need to eliminate risks and meet customer expectations, building an integrated QFD-FMEA framework is useful for product upgrading. To enhance the performance, the QFD and FMEA methods are improved. First, the base criterion method (BCM) is used to derive the CR weights in the QFD method and RFs in the FMEA method. Second, the combined compromise solution (CoCoSo) method and interactive multi-criteria decision making (TODIM- Portuguese acronym) methods are utilized to derive the priority of TCs and FMs, respectively. Third, the interval-valued intuitionistic fuzzy set (IVIFS) is used to express the expert evaluation. Moreover, given that co-opetitional relationship exist in firms and lead to opinion interactions, the improved Hegselmann-Krause (HK) model is utilized in our improved method to stimulate opinion interaction under different competitive states in different phases. Finally, a case study is presented to validate the improved method, and its superiority is demonstrated through sensitivity and comparative analysis.
The concept of reliability-centered maintenance (RCM) is applied to the two wind-turbine models Vestas V44-600 kW and V90-2MW. The executing RCM workgroup includes an owner and operator of the ...analyzed wind turbines, a maintenance service provider, a provider of condition-monitoring services, and wind-turbine component supplier as well as researchers at academia. Combining the results of failure statistics and assessment of expert judgement, the analysis is focused on the most critical subsystems with respect to failure frequencies and consequences: the gearbox, the generator, the electrical system, and the hydraulic system. This study provides the most relevant functional failures, reveals their causes and underlying mechanisms, and identifies remedial measures to prevent either the failure itself or critical secondary damage. This study forms the basis for the development of quantitative models for maintenance strategy selection and optimization, but may also provide a feedback of field experience for further improvement of wind-turbine design.
In this study, it is aimed to compare traditional and fuzzy FMEA in identifying areas that may pose risks and need improvement in Test and Calibration Laboratories. Within this scope, FMEA is used in ...ranking the possible risks. One hundred ninety‐nine failures are detected in 91 inspections, carried out in the Test and Calibration Laboratories. Since FMEA uses experts’ evaluations, which are considered subjective, fuzzy logic is implemented to the approach where the evaluations are presented with linguistic variables. The comparison of FMEA and fuzzy FMEA showed that there exists a high correlation between these two analyses and the order of priority based on the Fuzzy Risk Priority Number calculation is overlapping with the Risk Priority Number sequence. Fuzzy FMEA can also be considered when the evaluations are not trustworthy or incomplete. Therefore, this study can be addressed as an example of how fuzzy implementation to FMEA substantially be used instead of traditional FMEA when there exist qualitative, subjective or incomplete evaluations, or in cases where traditional FMEA has troubles in practice.
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