Reliability-Centered Maintenance of Generating Unit Using Stochastic FMECA
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본 논문은 신뢰도 기반 유지보수(RCM : Reliability-Centered Maintenance)계획에 의한 발전설비의 유지보수주기를 평가하고 있다. 유지보수주기는 발전설비의 안정성 및 비용을 결정하기 위한 기준으로 활용된다. 본 논문에서는 설비 수명평가를 통해 확률분포를 이용한 확률론적 TMECA(Failure Mode, Effects and Criticality Analysis)에 대해 논하고, 국내 발전설비중 비교적 평균수명이 짧은 복합화력 발전소를 선정하여 유지보수 주기를 평가하였다. 발전소 유지보수 평가는 향후 구조 개편된 전력시장에서 유지보수계획 수립 방법의 유용한 지표로 활용 가능할 것이다.Typical early failures and reliability of modern cordless mobile portable devices, characterized by their inability to provide quality RF performance, are analyzed. Multifaceted specifics of device technology, reliability and FA are discussed. Systematic analytical methodology is applied and RF-specific methods are outlined. Product and application statistics are displayed. Integrated block diagrams and joint FTA-FMECA are developed. FA results and classification of typical full, parametric, unbalanced and intermittent fault-failure modes and mechanisms for characteristic components and circuitries are presented. Fault evolution and interactions altering failure criticality are exemplified and substantiated. Reduction of early failures leads to significant reliability improvement, design and manufacturing cost reduction. RF reliability theoretical interpretation and modeling are discussed
Cordless
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<p>The Photovoltaic (PV) system is divided mainly into two subsystems; PV modules and a Balance of System (BoS) subsystems. This work shows two approaches for a reliability analysis on the subsystem level of aBoS: Failure mode effects criticality analysis (FMECA) and a Markov Process. FMECA concerns the root causes of failures and introduces prioritization numbers to highlight critical components of a BoS. Meanwhile, a Markov process is a reliability methodology that aims to predict the probability of success and failure of a BoS. In this way, a Markov process is a supportive tool for helping decision-makers to judge the criticality of failures associated with the operation of PV systems. Results show that the PV inverter contributes significantly to the failures of a BoS. Accordingly, further investigations are conducted on a PV inverter to prioritize the maintenance activities by determining the risk priority number of its component failures through quantitative CA. The novelty of the proposed methodologies stems from analyzing the roots of failure causes of BoS components and estimating the probability of failure of these components in order to improve the early development of a BoS, enhance maintenance management, and satisfy the demanding reliability by electric utilities.</p>
Component (thermodynamics)
Prioritization
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With the passage of time, any processing machine develops degraded effectiveness, marked by reduced mean time between failure (MTBF) and the reduction in the value of reliability. Even periodic preventive maintenance is not enough to restore reliability to its original value. Many a times even predictive preventive maintenance may require changed system of condition monitoring. Based on failure modes effect and criticality analysis (FMECA) and risk priority number (RPN) and comparing the thresholding value of the each component, it is possible to classify the critical components in regard to risk and hazards and losses involved in case of repair and maintenance. This requires determining the likelihood coefficient (LC) of each failure mode and the same for threshold values. In this paper, the author like to discuss and critically analyse the system, taking a case from the industry involving reliability based condition monitoring of Turbo Blower.
Condition Monitoring
Threshold limit value
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Preventive maintenance can avail the generation utilities to reduce cost and gain more profit in a competitive supply-side power market. So, it is necessary to perform reliability analysis on the systems in which reliability is essential. In this paper, RCM (Reliability -Centered Maintenance) analytical method is adopted using real historical failure data in Korean power plants. Therefore, the reliability -based Probability model for predicting the failures of components in the power plant is also established, and application to FMECA(Failure Mode Effects and Critical Analysis) consideration of failure probability, Based on the weighting ranking of generating equipments which status to be probability estimation by FMECA. The FMECA is an engineering analysis and a core activity performed by reliability engineers to review the effects of probable failure modes of generating equipments and assemblies of the power system on system performance The results of this paper show that application of FMECA with stochastic approach to the preventive maintenance can efficiently avail decreasing the cost on maintenance and hence improve the total benefit.
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With affordable repair and maintenance costs, the goal of design for reliability (DFR) is to ensure that customer expectations for reliability are fully met across a product lifecycle. This chapter introduces several DFR tools that are commonly used by reliability practitioners and design engineers: reliability and redundancy allocation (RRA), failure-in-time (FIT) design, design for six-sigma (DFSS), fault-tree analysis (FTA), and failure mode, effect, and criticality analysis (FMECA). While RRA is implemented at the component level, both FIT and DFSS are often used in the detailed design of subsystems or assemblies. FTA uses a top-down approach to investigate the root cause of systems failure and to determine the ways to reduce the risk of such failures. FMECA allows the design engineers to identify and rank the potential failure modes in the early development phase, and further determine the remedies to remove or mitigate the risk of failures.
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It is well-known that reliability of equipment is the most important of all performance to all equipments. In order to make the equipments which include mechanical equipments, electrical equipments and so on work normally, reliability theory FMECA is applied in an aviation equipment to study its reliability and improve operational reliability of the product. Through its reliability mathematical model, average of operational time is predicted based on calculating failure probability of all electrical components. According to the process of FMECA, all kinds of the failure mode, reasons, effects and criticality of the products can be determined completely. By comparing these criticality data as shown, the paper analyses adopted method by that the contents, accents and operating process of maintenance may be instituted finally. FMECA-based method for reliability study of the equipment and the equipment maintenance perform well. The results indicate that application of FMECA method can analyze reliability in detail and improve operational reliability of the equipment. Therefore this will supply theoretical bases and concrete measures of maintenance of the products to improve operational reliability of products. FMECA can be feasible and effective for improving operational reliability of all equipments.
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In order to improve the reliability of a certain ship alarm system,failure modes effects and criticality analysis(FMECA)for the alarm system are conducted to clear and definite the potential risk existing in system design,and then the weak links and defects of system design are found out through the reliability enhancement testing(RET). The results of FMECA and RET are consistent with each other. The combination of them provided a method to improve the reliability of alarm system.
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The significance of reliability researches are described first. It is necessary to know concepts of reliability without a doubt, and most common measures of reliability are discussed, including reliability function and failure rate. It emphasizes two important analysis approaches: Failure Mode, Effects, and Criticality Analysis (FMECA) and Fault-Tree Analysis (FTA). Last, it provides the quantified result of reliability analysis of a test-bed system with FTA-based RiskA software as a case study, which shows the usefulness of FTA-based reliability measurement.
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Electric equipments of power station must be operated in the state of high reliability with its responsibility in the seamless power supply. Maintenance is one of the important and most costly phases in the lifecycle of any power generation systems, to improve maintenance management and system reliability and maintain the continuity of work of the system. Reliability-Centered Maintenance (RCM) is one of the most recent maintenance techniques. In this research, an enhanced RCM methodology based on quantitative relationships implemented at system component level and the overall system reliability was applied to identify the distribution components that are critical to system reliability. Only unit one of the Second Power Plant of South Baghdad were selected as a case study in this research application. The major contribution of the study is to create a maintenance plan through applying statistical, historical data to generate a failure probability distribution model by using the combined methods of Failure Mode of Effects and Criticality Analysis (FMECA) and Fault Tree Analysis (FTA). Evaluation criteria and matrixes of criticality are used to evaluate the criticality level of failure modes. The overall approach developed offers a cost-effective model, which can be applied prior to testing and inspection procedures during the RCM application. The results obtained from this case study show that the application of proposed RCM methodology based on preventive maintenance planning will decrease the total cost value of maintenance about 463469.85 $. That Indicates saving about 59% of the total downtime cost compared with current maintenance. As well as Proposed maintenance program reduce the required time to repair the plant components annually at a rate 6 day so that represent that the labor cost will minimized about 6424 $.
Downtime
Condition-Based Maintenance
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Reliability and maintainability studies and procedures relating to the design and development of an electronic governing system for a naval steam turbogenerator set are described. Details of the studies are given, including reliability prediction, failure mode effects and criticality analysis (FMECA) and reliability and maintainability (R and M) review.
Maintainability
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