scholarly journals Model Based Transient Control and Component Degradation Monitoring in Generation IV Nuclear Power Plants DE-FG07-02ID22612 (Formerly: DE-FG03-02SF22612/A000) Quarter 2 Report January - March 2003

2003 ◽  
Author(s):  
James Paul Holloway
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Model Based ◽  
Degradation Monitoring ◽  
Transient Control

scholarly journals An Enhanced Preventive Maintenance Optimization Model Based on a Three-Stage Failure Process

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ruifeng Yang ◽  
Jianshe Kang ◽  
Zhenya Quan
Keyword(s):  
Optimization Model ◽  
Preventive Maintenance ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Failure Process ◽  
Maintenance Optimization ◽  
Maintenance Policy ◽  
Model Based ◽  
Inspection Interval

Nuclear power plants are highly complex systems and the issues related to their safety are of primary importance. Probabilistic safety assessment is regarded as the most widespread methodology for studying the safety of nuclear power plants. As maintenance is one of the most important factors for affecting the reliability and safety, an enhanced preventive maintenance optimization model based on a three-stage failure process is proposed. Preventive maintenance is still a dominant maintenance policy due to its easy implementation. In order to correspond to the three-color scheme commonly used in practice, the lifetime of system before failure is divided into three stages, namely, normal, minor defective, and severe defective stages. When the minor defective stage is identified, two measures are considered for comparison: one is that halving the inspection interval only when the minor defective stage is identified at the first time; the other one is that if only identifying the minor defective stage, the subsequent inspection interval is halved. Maintenance is implemented immediately once the severe defective stage is identified. Minimizing the expected cost per unit time is our objective function to optimize the inspection interval. Finally, a numerical example is presented to illustrate the effectiveness of the proposed models.

Robustness Analysis and Improvement of Fault Diagnosis Model for Nuclear Power Plants Based on Random Forest

2021 ◽  
Author(s):  
Jiangkuan Li ◽  
Meng Lin
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Robustness Analysis ◽  
Noisy Data ◽  
Data Driven ◽  
Model Based ◽  
Diagnosis Model

Abstract With the development of artificial intelligence technology, data-driven methods have become the core of fault diagnosis models in nuclear power plants. Despite the advantages of high flexibility and practicability, data-driven methods may be sensitive to the noise in measurement data, which is inevitable in the process of data measurement in nuclear power plants, especially under fault conditions. In this paper, a fault diagnosis model based on Random Forest (RF) is established. Firstly, its diagnostic performance on noiseless data and noisy data set containing 13 operating conditions (one steady state condition and 12 fault conditions) is analyzed, which shows that the model based on RF has poor robustness under noisy data. In order to improve the robustness of the model under noisy data, a method named ‘Train with Noisy Data’ (TWND) is proposed, the results show that TWND method can effectively improve the robustness of the model based on RF under noisy data, and the degree of improvement is related to the noise levels of added noisy data. This paper can provide reference for robustness analysis and robustness improvement of nuclear power plants fault diagnosis models based on other data-driven methods.

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