Failure Mode And Effect Analysis Analysis Research Articles

Perbaikan Kualitas Produksi Roti Tawar Dengan Metode Failure Mode and Effect Analysis (FMEA) dan SQC di Tefa Bakery Politeknik Negeri Jember

One of the food industry markets that are currently in tight competition is the bread industry. Teaching Factory (Tefa) Bakery Politeknik Negeri Jember develops white bread products and always strives to maintain product quality, but there is still data in the field that shows product defects in bread which cause the product to be unfit for sale. Product defects that occur during the production process are quite large, namely 27%. This study aims to Politeknik Negeri Jember, especially in the production process of bread. The research methods include research preparation (observation, and literature review), problem identification, data collection and data analysis, data analysis using the Failure Mode and Effects Analysis (FMEA) method. The results of the FMEA analysis of the failure mode "Too long during the fermentation process in the proofing machine" has the highest RPN value of 64, then the failure mode "Unclean equipment" has the second highest RPN value of 34. Recommendations for improvement for the failure mode "Too long during processing fermentation in the proofing machine" means that the proofing machine is given an alarm so that when the dough enters the machine and performs fermentation, the workers immediately set an alarm and wait for the alarm to sound and the recommendation for improvement for the failure mode "Unclean equipment" is to make a more regular schedule for cleaning the pan.

Development of Maintenance Management Strategy Based on Reliability Centered Maintenance for Marginal Oilfield Production Facilities

The present work adopted Reliability Centered Maintenance (RCM) methodology to evaluate marginal oilfield Early Production Facility (EPF) system to properly understand its functional failures and to develop an efficient maintenance strategy for the system. The outcome of the RCM conducted for a typical EPF within the Niger Delta zone of Nigeria provides an indication of equipment whose failure can significantly affect operations at the production facility. These include the steam generation unit and the wellhead choke assembly, using a risk-based failure Criticality Analysis. Failure Mode and Effect Analysis (FMEA) was conducted for the identified critical equipment on a component basis. Each component of the equipment was analyzed to identify the failure modes, causes and the effect of the failure. The outcome of the FMEA analysis aided the development of a robust maintenance management strategy, which is based on an optimized mix of corrective, preventive and condition-based monitoring maintenance for the marginal oilfield EPF.

Integration of FMEA method and overall equipment effectiveness to increase effectiveness of TS analyzer machine

Total Sulfur Analyzer (TS Analyzer) is one of the machines in the laboratory to test the total sulfur content contained in coal samples. Technically, specifications and use are regulated by international standards. In 2020, the downtime of these tools was very high, causing the productivity of surveyors to decrease. Low productivity will have an impact on the company's losses. The productivity of this equipment and machines is crucial to maintaining the company's success in its business processes. This study aims to measure the effectiveness of the operational performance of the TS Analyzer machine in the laboratory, analyze the causative factors and make improvements. This research uses the integration method of Failure Mode And Effect Analysis (FMEA) and Overall Equipment Effectiveness (OEE). Based on the analysis of the causes of the problem, the low OEE value is caused by the high breakdown loss. Based on the FMEA analysis, it was found that the operator was wrong in operating the machine with RPN 343, electrical instability with RPN 343, unstable temperature with RPN 343 and improper thermocouple installation. The results showed that the OEE value of the TS Analyzer machine has increased after the improvement.

Automatic Failure Modes and Effects Analysis of an Electronic Fuel Injection Model

In the development of safety-critical systems, it is important to perform failure modes and effects analysis (FMEA) to identify potential failures. However, traditional FMEA activities tend to be considered difficult and time-consuming tasks. To compensate for the difficulty of the FMEA task, various types of tools are used to increase the quality and the effectiveness of the FMEA reports. This paper explains an automatic FMEA tool that integrates the model-based design (MBD), FMEA, and simulated fault injection techniques in a single environment. The automatic FMEA tool has the following advantages compared to the existing FMEA analysis tool: First, the automatic FMEA tool automatically generates FMEA reports, unlike the traditional spreadsheet-based FMEA tools. Second, the automatic FMEA tool analyzes the causality between the failure modes and the failure effects by performing model-based fault injection simulation. In order to demonstrate the applicability of the automatic FMEA, we used the electronic fuel injection system (EFI) Simulink model. The results of the automatic FMEA were compared to those of the legacy FMEA.

Electron flash-RT program in clinical setting for human translation.

e13596 Background: A FLASH-RT program was established at Dartmouth-Hitchcock Medical Center in minimally-modified clinical setting by joint efforts of biomedical engineering, radiation oncology, radiation biology and medical physics teams. Various projects on dosimetry, chemical sensing, molecular profiling, software/hardware development, and translational studies have been conducted. The aim is to share logistical considerations and experience on running a FLASH-RT program to support institution-wide academic activities with an ultimate goal of treating human patients with FLASH-RT in 2022. Methods: A linac was converted in the clinical setting by qualified engineers to deliver an ultra-high dose rate (UHDR) electron beam. Routine safety and dosimetry checks were done by physicists for every reversible conversion. Long-term record-keeping and retrospective surveys were carried out to demonstrate the feasibility, safety, stability and accuracy of this dual-purpose (FLASH and conventional RT) approach. Comprehensive failure mode and effects analysis (FMEA) has been completed to systematically evaluate safety related considerations. A treatment planning system (TPS) has been developed in Varian Eclipse to facilitated comparative studies. The FLASH-capable linac has been utilized as shared resource to support institution-wide academic activities as well as normal clinical treatments. Results: With its safety (no accident or FLASH-related malfunction), flexibility (> 100 conversions in 2 years), reliability (̃6000 hours in flash mode and ̃5x105 Gy accumulative dose delivered at isocenter) and accuracy (̃5% conversion-to-conversion variations) demonstrated by commissioning, long-term user experience and comprehensive FMEA analysis, the FLASH-RT platform has been actively utilized for researches in six major categories 1) FLASH beam dosimetry; 2) real-time beam delivery monitoring and control; 3) oximetry and chemical sensing; 4) preclinical/translational small/large animal treatment with tumor control and normal tissue complication endpoints, 5) treatment plan and delivery optimization; 6) the design of phase I/II trials. Key findings in each category will be reported. Conclusions: A FLASH-RT program in clinical setting is established at Dartmouth with joint efforts, promoting collaborative projects to advance FLASH-RT to clinical treatment. The system has been reliably utilized for over two years for mechanistic as well as translational studies and support a phase I/II trial treating cutaneous lymphoma with eFLASH-RT.

Sustainable Food Production: An Intelligent Fault Diagnosis Framework for Analyzing the Risk of Critical Processes

Fault diagnosis and prognosis methods are the most useful tools for risk and reliability analysis in food processing systems. Proactive diagnosis techniques such as failure mode and effect analysis (FMEA) are important for detecting all probable failures and facilitating the risk analysis process. However, significant uncertainties exist in the classical-FMEA when it comes to ranking the risk priority numbers (RPNs) of failure modes. Such uncertainties may have an impact on the food sector’s operational safety and maintenance decisions. To address these issues, this research provides a unique FMEA framework for risk analysis within an edible oil purification facility that is based on certain well-known intelligent models. Fuzzy inference systems (FIS), adaptive neuro-fuzzy inference systems (ANFIS), and support vector machine (SVM) models are among those used. The findings of the comparison of the proposed FMEA framework with the classical model revealed that intelligent strategies were more effective in ranking the RPNs of failure modes. Based on the performance criteria, it was discovered that the SVM algorithm classifies the failure modes more accurately and with fewer errors., e.g., RMSE = 7.30 and MAPE = 13.19 with that of other intelligent techniques. Hence, a sensitivity FMEA analysis based on the SVM algorithm was performed to put forward suitable maintenance actions to upgrade the reliability and safety within food processing lines.

REORGANIZATION OF THE ASSEMBLY STATION IN THE PRODUCTION PROCESS OF THE SLIDING FLOOR FOR RELOADING RAMPS IN THE CONTEXT OF IMPROVING THE QUALITY OF THE FINISHED PRODUCT

The paper presents the definitions of the term quality quoted in the literature on the subject. FMEA (Failure Mode and Effects Analysis) methodology was presented. A reloading ramp was characterized as the subject of the study. A cause-and-effect analysis of non-compliance in the finished product was performed using the Pareto-Lorenz diagram, Ishikawa diagram and brainstorming. The FMEA analysis of the assembly table indicated the elements of the workstation causing the most frequently occurring non-conformities. Based on the obtained results, corrective actions were proposed to reorganize the assembly station. The implemented activities made it possible to reduce the critical RPN coefficients for the elements of the assembly table and to shorten the time necessary to make one piece of the finished product.

Process improvement design at PT URW using failure mode and effect analysis

PT URW is a company engaged in the textile sector that processes yarn into semi-finished fabrics. Such is the case that PT URW has a problem with its waste materials. To address this, the researcher has implemented the method of failure mode and effect analysis (FMEA). In data collection and processing, the researcher defined the problem, collected data, took measurements using the critical waste method, and interpreted the data into the Pareto Chart. The area that had the greatest value was continued to the analysis stages of critical waste analysis, FMEA analysis, and alternative solutions. The research was concluded by providing corrective solutions based on the potential causes that had been found in the previous stage and by validating the formed planned improvements.

Implementasi Metode Overall Equipment Effectiveness Berbasis Six Big Losses Guna Mengevaluasi Efektivitas Mesin Packing Semen

Integrated maintenance management is an important factor in maintaining the stability of machine operations in manufacturing companies. PT AAAA Factory Gresik is a cement manufacturing company, where the Section Division of Gresik Finish Mill Packer Operation has a problem of downtime cement packing machine 40 kg and causes production delay. This study aims to evaluate the effectiveness of the 40kg cement packing machine as well as to calculate losses due to unproductiveness of the packing machine. Overall Equipment Effectiveness (OEE), Six Big Losses, and Failure Mode and Effect Analysis (FMEA) were used as research method. This study found that OEE value for January-December 2019 of 41.25% are considered as lower percentage in compare with the ideal value of international standards which is 85%. Moreover, the largest average losses are found, namely reduce speed losses, idle and stoppages minor losses and defects in process with percentage in following order, 4.49%, 4.49% and 2.76%.Result from FMEA analysis have been identified 3 highest Risk Priority Number (RPN), namely transportation with an RPN value of 294, the second RPN value of 288 is a blockage in the packing machine, finally the engine button is jammed with an RPN value of 252. The proposed improvement scenario is the start of the engine operating operators checking and cleaning machines regularly and the marketing division expands the sales market.

Importance of Risk Communication and Risk Analysis in Medical Device Industry

In medical device industry the risk management plays a very vital role. There should be proper communication from each and every stakeholder related to risk management of each respective department, it can be Production, Design and Development or Quality Control and all other departments. In this current research work the role of risk analysis which had been done accordingly ISO 14971 for risk management of medical device using FMEA is implemented. FMEA (Failure Mode and Effects Analysis) plays important role in risk analysis by having several steps for mitigation of risk. Also it had been used for identifying hazard of each risk throughout the lifecycle of the medical device. Risk communication should be advanced so, that the risk identified can be easily controlled by taking appropriate risk control measures. In any medical device industry risk analysis should be done properly and as well the risk communication channel should be strong for proper and immediate action. In this research paper practically the role of Risk communication and risk analysis is covered. Risk management of any of the organization can only be effective if the risk analysis is done strongly and the communication related to risk is proper. In this research FMEA analysis for risk analysis is done on a medical device and also the communication from risk manager to the other entire stakeholders of the risk management from various departments are fully taken into the consideration.

Analisa Perbaikan Manajemen Perawatan Dengan Metode Reliability Centered Maintenance Bagian Cnc Wirecut di PT X

PT X is one of the manufacturing companies in Indonesia, which specializes in manufacturing molds and dies which offers products to the internal group and the domestic market. This study discusses the policy analysis of machine repair maintenance management using the RCM (Reliability Centered Maintenance) method approach at the PT X manufacturing company. Some of the problems that occur are sudden engine failure, which will cause over production. One machine that is considered critical is the CNC Wirecut K90 machine. With the application of using RCM it is expected to improve machine reliability through several systematic RCM implementations: system selection and information collection, defining system boundaries, system descriptions and function block diagrams, describing system functions and functional failures, compiling Failure Mode and Effect Analysis (FMEA), composing Logic Tree Analysis (LTA), selection of actions. The new policy set by the Realibility Centered Maintenance (RCM) method consists of 14 failure modes that are resolved by time-directed (CD) and 6 failure modes that are overcome by run to failure (RTF). In the FMEA analysis, there are 4 components that most often fail to function, namely the electrode pin, wire guide, filter and contact fit. The determination of this component is based on the RPN value and data in the field. In the RCM analysis, the electrode pin, wie guide and contact fit components are included in the run to failure (RTF) treatment policy category. Meanwhile, the filter component is in the category of condition direction (CD) maintenance policy with an average failure rate of 29,951 days.

Analisis Penyebab Kerusakan Unit Pompa Pendingin AC dan Kompresor menggunakan Metode FMEA

PT. XYZ is a company engaged in the steel industry which produces steel slabs, steel billets, steel plates and steel wires. One of the equipment that often gets damaged is the air conditioning pump and compressor unit in the fluid center service. The results of the availability of the AC and compressor refrigeration pump units show that the availability value is still below the company standards set so that an analysis of the root causes of the problem is needed and finding the best solution to fix existing problems by applying the failure mode and effect analysis (FMEA) method. FMEA is a method that systematically and structurally can analyze and identify the consequences of system and process failures and reduce or analyze the chances of failure. The purpose of this study is to identify and analyze the level of damage and its causes by applying the FMEA method. Based on the Pareto diagram of the damage to the air conditioning pump and compressor engine unit, it was found that the highest frequency of damage was the power supply with a down time percentage of 32.4%. The results of FMEA analysis produce two components that have a very high RPN value which is categorized as potential severity, namely fuse as the first component with an RPN value of 378 and the second is a fuse holder with an RPN value of 252. These two components are the main priority for improvement in the pump engine unit air conditioners and compressors especially for the machine and human aspects.

A new hybrid MCDM approach for RPN evaluation for a medical device prototype

AbstractThe Failure Mode and Effect Analysis (FMEA) is a useful instrument born in the aerospace industry and widely used to improve a process or product's efficiency. Over the years, this instrument has been adopted in increasingly different contexts, such as HealthCare. This paper proposes an approach aimed at improving the defects typical of the classic FMEA in the design phase, that is, in a scenario full of uncertainties and with little information available, using a new hybrid Multiple‐criteria decision‐making (MCDM) method in order to obtain a priority index more performant than Risk Priority Number (RPN). In the proposed method, the three assessment criteria have a different weighting in the index's final computation, differently from the classical RPN. These weights are obtained with a scientific technique, thus avoiding that excessive subjectivity influences the final result. A more efficient priority index is obtained through a new hybrid approach that solves some classical RPN gaps. A case study concerning an endoscope Ear Nose Throat Entropy (ENT) prototype is examined to illustrate the proposed method. FMEA analysis in HealthCare is increasingly used for its flexibility and reliability. This study focuses on using new techniques to eliminate certain defects or exploit some qualities better. The use of a robust and elastic innovative MCDM method to calculate a new priority index and a scientific technique to obtain the weight of the selection are the interesting insights proposed in this paper.

A modified method to improve failure analysis

Failure mode and effects analysis (FMEA) is an effective method that is widely used to manage failures and prevent risks, and, thanks to its simplicity and its versatility, the most important and renowned quality norms request it use. However, in literature, several works have criticized this tool and claims that it had short-comes. The FMEA team members are considered with the same importance and failures values are calculated meaning their judgement and without any differentiation. Moreover, failures modes are supposed to be independent and the effect of their relationship is ignored in the risk analysis. The accuracy of the FMEA analysis is also reduced due to the fact that risk factors are supposed to have the same importance. In this article, we propose to combine FMEA with other methods in order to overcame it limitedness. Firstly, a criteria selection will be proposed in order to select the most relevant factors for risk analysis. Their weights will be calculated using the best worst method (BWM). Secondly, a weight associated to every team member will be calculated and used to obtain failures values. Thirdly, the design structure method (DSM) will be used to identify the relationship between failures and to judge their importance, we propose to use the fuzzy technique for order of preference by similarity method (FTOPSIS). In order to test the applicability and the effectiveness of this method, a case study will be proposed in the end.

The Risk Priority Number Evaluation of FMEA Analysis Based on Random Uncertainty and Fuzzy Uncertainty

The risk priority number (RPN) calculation method is one of the critical subjects of failure mode and effects analysis (FMEA) research. Recently, RPN research under a fuzzy uncertainty environment has become a hot topic. Accordingly, increasing studies have ignored the important impact of the random sampling uncertainty in the FMEA assessment. In this study, a fuzzy beta‐binomial RPN evaluation method is proposed by integrating fuzzy theory, Bayesian statistical inference, and the beta‐binomial distribution. This model can effectively realize real‐time, dynamic, and long‐term evaluation of RPN under the condition of continuous knowledge accumulation. The major contribution of the proposed model is to use the random uncertainty and fuzzy uncertainty in an integrated model and provide a Markov Chain Monte Carlo (MCMC) method to solve the complex integrated model. The study presented a case study, which presented how to apply this model in practice and indicated the significant influence on the measurement error caused by ignoring the random uncertainty caused by expert evaluation in RPN calculations.

Ontology-based reliability analysis of aircraft engine lubrication system

This article focuses on identifying limitations and deficiencies of reliability methods that are currently used in the aviation industry. The goal is to propose a solution to address these issues and, consequently, improve the way reliability analyses are carried in the industry. In collaboration with an aircraft engine manufacturer, Failure Mode and Effects Analysis (FMEA) of an aircraft engine lubrication system was carried the traditional way and with current tools used by the company. Reliability ontology suitable to carry the analysis in semi-automatic way was then proposed, implemented, and used with the same FMEA analysis. The results show that the ontology-based approach has significant potential for improving the consistency and overall quality of the reliability analyses in the aviation. This article details the process of development of an FMEA ontology model, case study of its application and the comparison of the traditional and the ontology-based approach.

Application of FMEA Analysis in the Short Cheese Supply Chain

Abstract The aim of this study was to apply Failure Mode and Effect Analysis (FMEA) methodology to determine the biological, chemical and physical failures that may occur during the farmhouse production of white brined cheese (short cheese supply chain) in the Republic of Serbia. For that purpose, the values for occurrence (O), severity (S) and detection (D) were determined. These estimated values were used to calculate risk priority number (RPN), for each potential failure. Very high values for RPN were determined for biological failures in short cheese supply chain. The highest values were determined for milking step, followed by cheese ripening step and transportation of cheese by personal vehicle. The main chemical risks associated with raw milk were the presence of aflatoxins and residues of veterinary medicines. Our results indicated that the greatest risks in short cheese supply chain can be attributed to biological and chemical failures, due to great severity and low level of detection of identified failures. The proposed corrective measures include different pre-requisite programs. Even the application of these measures will not result in great risk reduction, as the severity and detection will remain the same. The lowest RPN values were obtained for physical failures, as they are visible and therefore easier to detect.

The Measurement and Improvement of Effectiveness in K-440 Haul Truck Using Overall Equipment Effectiveness in Coal Mining Company

The objectives of this study were to determine the effectiveness of K-440 haul trucks based on Overall Equipment Effectiveness (OEE) calculation, to find out the main factors that caused the ineffectiveness of K-440 haul trucks based on fishbone diagram and Failure Mode and Effect Analysis (FMEA), and to analyze the proposed implementation of appropriate improvements to overcome the main problems of K-440 haul trucks. The method used was the mixed method, and the type of research was descriptive. The analysis methods used were OEE, fishbone diagram, and FMEA. The results show that the average effectiveness of the three K-440haul trucks is still far below international standards, which is 44,99%. The material is a significant factor that causes low OEE based on fishbone diagram analysis. There are five problems based on the Risk Priority Number (RPN) critical value from FMEA analysis, namely transmission which is not functioning properly, worn brake lining, unrotated propeller shaft on the shaft, cracked brake drum, and broken spring. Then, the implementation of eight pillars of Total Productive Maintenance (TPM) is proposed in overcoming the main problem of K-440haul trucks. Based on these results, the company needs to implement eight pillars of TPM and carry out OEE calculations regularly.

Risk-cost model for FMEA approach through Genetic algorithms – A case study in automotive industry

Failure mode and effects analysis (FMEA) is a proactive method for eliminating the potential failures emerged from various systems such as products, processes, designs, or systems. FMEA utilizes the risk priority number (RPN) to determine the risk priority order of failure modes. RPN is calculated by multiplying the values of three risk factors: severity (S), detection (D) and occurrence (O) Although FMEA techniques are used in different industries in many situations when multiple experts give their opinions about one failure mode, the risk evaluations can be vague and imprecise, which could arise conflicting evidence that is hard to manage. This paper presents a risk-cost analysis model that uses Genetic algorithms to generate an FMEA for the raw materials and finished parts reception process, in automotive industry. Unlike the classic FMEA analysis, in our model the risk factors D and O are determined by resources cost analysis involved in their improvement. Here comes the Genetic algorithm that will determine the optimal cost under an acceptable risk. The proposed solution uses modern information technology for data acquisition (complex event processing), automation of analysis process (artificial intelligence) and long-term support for quality staff in FMEA analysis (real time and batch data analytics).

Quality Control of Plastic Cup Products Using Statistical Process Control Methods and Failure Modes and Effect Analysis

PT. ICP is a company engaged in manufacturing of packaging, with a wide variety of packaging technologies that fit the needs of the current market share. Among the resulting product is aplastic cup, the results of thermoforming. This research aims to know the main cause of the defect (defect) in a plastic cup products and to reduce product defects in the production process. From the results of the observations made in September 2017 until December 2017 known that the plastic cup products with total production of 63,314,964 pcs to 3,671,341 pcs disability amount. Based on the problems faced by the company efforts on product quality control plastic cup to find the cause of a disability and find solutions for improvement. Proper methods used in the problems that occurred in PT ICP are using Statistical Process Control (SPC) and the method of Failure Mode and Effects Analysis (FMEA). The method is intended to reduce defects in the product and look for the main cause of defect products in a plastic cup. From the results of research conducted has been known that the biggest cause of disability plastic cup is of a rough lip with disabilities amount of 1,346,308 pcs with a cumulative value of 42%. FMEA analysis and the results of that unknown cause rough lip is from wear cutting factor with a value of 224 RPN.