Non-Destructive Condition Monitoring of Nuclear Power Plant Power Cables

Author(s):  
Ramy S. A. Afia ◽  
Ehtasham Mustafa ◽  
Tamus Zoltan Adam
2013 ◽  
Vol 284-287 ◽  
pp. 1749-1753
Author(s):  
Kyung Nam Jang ◽  
Jong Soeg Kim ◽  
Sun Chul Jeong ◽  
Kyung Heum Park ◽  
Sung Yull Hong

In nuclear power plants, there are many cables that perform safety-related functions. These cables should implement condition monitoring during the operation period in the nuclear power plant, in order to assess the remaining qualified life and extend the qualified life. In this study we focused on the indenting method which can measure the hardness of the cable jacket. This method is selected because it is non-destructive and requires short testing time and small sized equipment. In order to address the problems with the existing indenting test equipment, we developed new indenting test equipment, which could automatically move on the surface of the object cable. The newly developed equipment is designed for a small-sized and light-weight robot using wireless communication in order to implement condition monitoring in a harsh environment or locations that are inaccessible to the tester. The developed wireless cable indenting robot is composed of three parts, which are mechanical and electrical hardware parts and remote-control part. In order to analyze the degradation tendency of the cable, we prepared four thermally aged specimens and one un-aged specimen. Using the developed robot, we measured the modulus of the cable jacket of each specimen. The test data showed that the modulus of the cable jacket increased linearly as the accelerated aging time increased. From these results, we can analyze the degradation trends pertaining to cables installed in nuclear power plant according to the operation period.


2019 ◽  
Vol 7 (2B) ◽  
Author(s):  
Vanderley Vasconcelos ◽  
Wellington Antonio Soares ◽  
Raissa Oliveira Marques ◽  
Silvério Ferreira Silva Jr ◽  
Amanda Laureano Raso

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.


2005 ◽  
Vol 127 (3) ◽  
pp. 230-236 ◽  
Author(s):  
Min-Rae Lee ◽  
Joon-Hyun Lee ◽  
Jung-Teak Kim

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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