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.

Sensitivity Analysis of Major Parameters on Piping Failure Probabilities

2004 ◽  
Vol 261-263 ◽  
pp. 821-826
Author(s):  
Sung Gyu Jung ◽  
Chang Soon Lee ◽  
In Gyu Park ◽  
Se Hwan Lee ◽  
Tae Eun Jin
Keyword(s):  
Sensitivity Analysis ◽  
Size Distribution ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Crack Size ◽  
Deterministic Approach ◽  
Inspection Interval ◽  
Different Types ◽  
Failure Probabilities

In-service inspections (ISI) of pipes in the nuclear power plants are currently performed based on mandated requirements in the ASME Section XI, which is based on deterministic approach of the critical welds. The 20 years of ISI experience in U.S.A. has revealed less correlation between the critical welds and actual failures, and much conservatism in current ISI requirements. To reduce those problems, risk-informed ISI technology has been developed and proved to be useful. This paper presented a method for predicting piping failure probabilities in an application of risk-informed ISI, and analyzed the effect of input parameters on piping failure probabilities. Results generated using this approach revealed that the calculated failure probabilities can be sensitive to the different types of stressors, crack size distribution, inspection interval, etc..

Laser-Based Maintenance and Repair Technologies for Reactor Components

2004 ◽  
Author(s):  
Masaki Yoda ◽  
Naruhiko Mukai ◽  
Makoto Ochiai ◽  
Masataka Tamura ◽  
Satoshi Okada ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Preventive Maintenance ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Rapid Heating ◽  
Water Environment ◽  
Inspection System ◽  
Material Surface ◽  
Maintenance And Repair

Stress corrosion cracking (SCC) is the major factor to reduce the reliability of aged reactor components. Toshiba has developed various laser-based maintenance and repair technologies and applied them to existing nuclear power plants. Laser-based technology is considered to be the best tool for remote processing in nuclear power plants, and particularly so for the maintenance and repair of reactor core components. Accessibility could be drastically improved by a simple handling system owing to the absence of reactive force against laser irradiation and the flexible optical fiber. For the preventive maintenance, laser peening (LP) technology was developed and applied to reactor components in operating BWR plants. LP is a novel process to improve residual stress from tensile to compressive on material surface layer by irradiating focused high-power laser pulses in water. We have developed a fiber-delivered LP system as a preventive maintenance measure against SCC. Laser ultrasonic testing (LUT) has a great potential to be applied to the remote inspection of reactor components. Laser-induced surface acoustic wave (SAW) inspection system was developed using a compact probe with a multi-mode optical fiber and an interferometer. The developed system successfully detected a micro slit of 0.5mm depth on weld metal and heat-affected zone (HAZ). An artificial SCC was also detected by the system. We are developing a new LP system combined with LUT to treat the inner surface of bottom-mounted instruments (BMI) of PWR plants. Underwater laser seal welding (LSW) technology was also developed to apply surface crack. LSW is expected to isolate the crack tip from corrosive water environment and to stop the propagation of the crack. Rapid heating and cooling of the process minimize the heat effect, which extends the applicability to neutron-irradiated material. This paper describes recent advances in the development and application of such laser-based technologies.

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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