Standby Equipment Reliability Data Analysis on Risk Monitor of Nuclear Power Plant

2018 ◽  
Author(s):  
Yingfei Ma ◽  
Zhijian Zhang ◽  
He Wang ◽  
Sijuan Chen ◽  
Anqi Xu ◽  
...  
Keyword(s):  
Data Analysis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Failure Modes ◽  
Reliability Data ◽  
Online Risk ◽  
Equipment Reliability ◽  
Spare Time ◽  
Risk Monitor

Reliability data works as the basis of risk monitor of nuclear power plant. The failure modes of the equipment in a nuclear power plant can be divided into operation failure, demand failure and standby failure. A standby equipment is affected by the demand stress and the standby stress simultaneously, so the method of reliability data analysis must consider the two types of failure. The reliability data in online risk monitor should reflect the change of equipment reliability with time, including standby equipment. A method to deal with the reliability data of the standby equipment is presented in this paper. This model takes into account the failure of the equipment during the spare time and the failure of the starting time. Considering the characteristics of the reliability data in the nuclear power plant, the method of parameter estimation is studied. Finally, this method is applied to online risk monitor in nuclear power plant and the suggestion of reliability data application is put forward.

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.

scholarly journals Human Reliability Studies With Microworld Simulators

2019 ◽  
Vol 63 (1) ◽  
pp. 1829-1833 ◽  
Author(s):  
Ronald Boring ◽  
Thomas Ulrich ◽  
Roger Lew ◽  
Martin Rasmussen Skogstad
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Data Collection ◽  
Nuclear Power ◽  
Human Error ◽  
Data Sources ◽  
Human Reliability ◽  
Reliability Data ◽  
Experimental Control ◽  
Advantages And Disadvantages

The authors have recently developed a microworld, a simplified process control simulator, to simulate a nuclear power plant. The microworld provides an environment that can be readily manipulated to gather data using a range of participants, from students to fully qualified operators. Because the microworld represents a simplified domain, it is possible to have more precise experimental control compared with the complex and confounding environment afforded by a full-scope simulator. In this paper, we discuss collecting human reliability data from a microworld. We review the generalizability of human error data from the microworld compared to other data sources like full-scope simulator studies and compare advantages and disadvantages of microworld simulator studies to support human reliability data collection needs.

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.

The Comparison Research About Two Reliability Management Methods in AP1000 Design

2017 ◽  
Author(s):  
Zhixin Xu ◽  
Chengzhang Wang ◽  
Jingjing Liu
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Implementation Process ◽  
Power Production ◽  
Classification Method ◽  
Reliability Management ◽  
Equipment Reliability ◽  
Comparison Research ◽  
R Classification

As a kind of Generation-III passive nuclear power plant, AP1000 has applied two kinds of equipment reliability management methods: the equipment Power Production Reliability Classification (R-Classification) method and Design-Reliability Assurance Program (D-RAP). To invest these two methods, the comparison is implemented between the classification principles, judgment basis and implementation process. According to the RCS and CVS systems, the R-Classification and D-RAP results are compared and some suggestions to enhance the NPP reliability managements are proposed.

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