scholarly journals An Extended FMEA Model for Exploring the Potential Failure Modes: A Case Study of a Steam Turbine for a Nuclear Power Plant

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Huai-Wei Lo ◽  
James J. H. Liou ◽  
Jen-Jen Yang ◽  
Chun-Nen Huang ◽  
Yu-Hsuan Lu
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Failure Modes ◽  
Critical Infrastructure ◽  
Detection System ◽  
Risk Index ◽  
Assessment Model ◽  
Potential Failure

Critical types of infrastructure are provided by the state to maintain the people’s livelihood, ensure economic development, and systematic government operations. Given the development of ever more complicated critical infrastructure systems, increasing importance is being attached to the protection of the components of this infrastructure to reduce the risk of failure. Power facilities are one of the most important kinds of critical infrastructure. Developing an effective risk detection system to identify potential failure modes (FMs) of power supply equipment is crucial. This study seeks to improve upon prior approaches for risk assessment by proposing a hybrid risk-assessment model using the concepts of failure mode and effect analysis (FMEA) and multiple-criteria decision-making (MCDM). The proposed model includes a cost-based factor for decision-makers. The subjectivity and uncertainty in FM assessment are adjusted through the rough number method. The original risk priority number (RPN) can be expanded by including the entropy weights in the risk index. Furthermore, to rank the risk priorities in a rational manner, a modified technique for order preference by similarity to ideal solution (modified TOPSIS) is adopted. The applicability and effectiveness of the proposed method were demonstrated by considering an example of a turbine steam engine in a nuclear power plant.

Human reliability in non-destructive inspections of nuclear power plant components: modeling and analysis

2019 ◽  
Vol 7 (2B) ◽  
Author(s):  
Vanderley Vasconcelos ◽  
Wellington Antonio Soares ◽  
Raissa Oliveira Marques ◽  
Silvério Ferreira Silva Jr ◽  
Amanda Laureano Raso
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Human Factors ◽  
Nuclear Power ◽  
Power Plants ◽  
Failure Modes ◽  
Cooling System ◽  
Human Reliability ◽  
Non Destructive ◽  
Plant Components

Non-destructive inspection (NDI) is one of the key elements in ensuring quality of engineering systems and their safe use. This inspection is a very complex task, during which the inspectors have to rely on their sensory, perceptual, cognitive, and motor skills. It requires high vigilance once it is often carried out on large components, over a long period of time, and in hostile environments and restriction of workplace. A successful NDI requires careful planning, choice of appropriate NDI methods and inspection procedures, as well as qualified and trained inspection personnel. A failure of NDI to detect critical defects in safety-related components of nuclear power plants, for instance, may lead to catastrophic consequences for workers, public and environment. Therefore, ensuring that NDI is reliable and capable of detecting all critical defects is of utmost importance. Despite increased use of automation in NDI, human inspectors, and thus human factors, still play an important role in NDI reliability. Human reliability is the probability of humans conducting specific tasks with satisfactory performance. Many techniques are suitable for modeling and analyzing human reliability in NDI of nuclear power plant components, such as FMEA (Failure Modes and Effects Analysis) and THERP (Technique for Human Error Rate Prediction). An example by using qualitative and quantitative assessesments with these two techniques to improve typical NDI of pipe segments of a core cooling system of a nuclear power plant, through acting on human factors issues, is presented.

U.S. Nuclear Power Plant Performance Assessment Using the Versatile Economic Risk Tool (VERT)

2021 ◽  
Author(s):  
Jaden C. Miller ◽  
Spencer C. Ercanbrack ◽  
Chad L. Pope
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Fault Tree ◽  
Plant Performance ◽  
Economic Risk ◽  
Reliability Data ◽  
Water Reactor ◽  
Tree Models

Abstract This paper addresses the use of a new nuclear power plant performance risk analysis tool. The new tool is called Versatile Economic Risk Tool (VERT). VERT couples Idaho National Laboratory’s SAPHIRE and RAVEN software packages. SAPHIRE is traditionally used for performing probabilistic risk assessment and RAVEN is a multi-purpose uncertainty quantification, regression analysis, probabilistic risk assessment, data analysis and model optimization software framework. Using fault tree models, degradation models, reliability data, and economic information, VERT can assess relative system performance risks as a function of time. Risk can be quantified in megawatt hours (MWh) which can be converted to dollars. To demonstrate the value of VERT, generic pressurized water reactor and boiling water reactor fault tree models were developed along with time dependent reliability data to investigate the plant systems, structures, and components that impacted performance from the year 1980 to 2020. The results confirm the overall notion that US nuclear power plant industry operational performance has been improving since 1980. More importantly, the results identify equipment that negatively or positively impact performance. Thus, using VERT, individual plant operators can target systems, structures, and components that merit greater attention from a performance perspective.

The Use of the ASME PRA Standard in Pressure Vessel and Piping Work

2002 ◽  
Author(s):  
Richard A. Hill
Keyword(s):  
Risk Assessment ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Pressure Vessel ◽  
Nuclear Power ◽  
Risk Information ◽  
Technical Requirements ◽  
Operation And Maintenance ◽  
Construction Operation ◽  
Standard Sets

After several years of intense labor by many industry people, ASME is about to issue its newly approved PRA standard. This standard is for probabilistic risk assessment (PRA) for nuclear power plant applications. It is not a standard on how to build a PRA model; although, that could be inferred from the standard’s technical requirements. This Standard sets forth requirements for PRAs used to support risk-informed decisions related to design, licensing, procurement, construction, operation, and maintenance. It also prescribes a method for applying these requirements depending the degree to which risk information is needed and credited.

Condition Monitoring of a Nuclear Power Plant Check Valve Based on Acoustic Emission and a Neural Network

2005 ◽  
Vol 127 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Min-Rae Lee ◽  
Joon-Hyun Lee ◽  
Jung-Teak Kim
Keyword(s):  
Neural Network ◽  
Acoustic Emission ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Condition Monitoring ◽  
Nuclear Power ◽  
Failure Modes ◽  
Check Valve ◽  
Flow Loop ◽  
Artificial Neural

The analysis of acoustic emission (AE) signals produced during object leakage is promising for condition monitoring of the components. In this study, an advanced condition monitoring technique based on acoustic emission detection and artificial neural networks was applied to a check valve, one of the components being used extensively in a safety system of a nuclear power plant. AE testing for a check valve under controlled flow loop conditions was performed to detect and evaluate disk movement for valve degradation such as wear and leakage due to foreign object interference in a check valve. It is clearly demonstrated that the evaluation of different types of failure modes such as disk wear and check valve leakage were successful by systematically analyzing the characteristics of various AE parameters. It is also shown that the leak size can be determined with an artificial neural network.

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