Condition Assessment of Nuclear Power Plant Equipment Based on Machine Learning Methods: A Review

Xu, Ye‐Shuang; Cai, Yunze; Lin, Song

doi:10.1080/00295450.2023.2169042

Cited by 10 publications

(5 citation statements)

References 74 publications

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“…Y. Xu et al (2023) focus on the importance of assessing the condition of nuclear power plant (NPP) equipment and points out its significance for improving the safety and efficiency of NPPs. The paper is notable for offering a detailed literature review and highlighting issues that require further research in this area.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, Y. Xu et al (2023) focus on monitoring the condition of NPP equipment and use machine learning methods for this purpose. Both studies have practical applications in various technology areas, but their subjects and methods differ.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, the study by Y. Xu et al (2023) focuses on monitoring the condition of NPP equipment and uses machine learning methods for this purpose. The third study by Z.…”

Section: Discussionmentioning

confidence: 99%

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Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions

Belytskyi,

Yermolenko,

Petrenko

et al. 2023

Tehnìka ta energetika

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The research relevance is determined by the need to improve the processes of measurement of objects size in hard-to-reach conditions. In the modern industrial environment, where high measurement accuracy is critical for ensuring safety and maximizing the efficiency of production processes, the study of this topic is relevant in the context of rapid technological development and increased requirements for production quality. The study aims to evaluate the possibilities of using modern computer vision methods for measuring and reconstructing objects in difficult technical conditions, such as the enclosure of a water-water power reactor. The study employed 3D photogrammetry methods, including Depth from Stereo and Multi-View Stereo, as well as Structure from Motion methods. The study determined that modern computer vision methods, in particular machine learning methods, can be successfully used for measuring and reconstructing objects in hard-to-reach conditions. The study showed that the measurement accuracy can reach values close to 1 mm under ideal conditions and at a distance of 1.5 from the measuring device to the object. At the same time, the Multi-View Stereo method revealed greater uniformity of the spatial distribution of errors compared to the Depth from the Stereo method. In practice, in the conditions of real photos, the Multi-View Stereo method turned out to be more demanding to accurately determine the position of the camera. Due to its low demand for the exact coordinates of the cameras, the Depth from the Stereo method showed better results, showing less error in the measurements. The study highlighted the possibility of using the proposed method to distinguish fluctuations in the height of the surface of the object, which is important for further applications in the field of reactor maintenance and other areas of industry. The practical value of this research lies in the development and validation of methods for measuring and reconstructing objects in conditions where traditional methods become limited or impractical

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions

Belytskyi,

Yermolenko,

Petrenko

et al. 2023

Tehnìka ta energetika

View full text Add to dashboard Cite

show abstract

“…Transfer learning is the application of knowledge or patterns learned from the same type of equipment to new equipment or new problems, focusing on how to map the data in the source domain and the target domain from the original feature space to the target feature space and better utilize the existing labeled data samples in the source domain for RUL prediction [88,89].…”

Section: State Of Research On Dynamic Data-driven Rul Prediction Methodsmentioning

confidence: 99%

Summarization of Remaining Life Prediction Methods for Special Power Plants

Liang,

Li,

Zhao

et al. 2023

Applied Sciences

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With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.

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“…45 support in the panel, as an example, was separately analyzed from the perspectives of the safety valve opening times and duration, with the results shown in Figure 14. (1) The service life and failure probability of the safety valve are strongly related to the number of valve openings [30]. The FAD320/40 safety valve, examined in this study, has a recommended cycle count of 3000 times, as specified by the manufacturer.…”

Section: Analysis Of Strike Ground Pressure Based On Safety Valve Ope...mentioning

confidence: 99%

Extraction and Application of Hydraulic Support Safety Valve Characteristic Parameters Based on Roof Pressure Data

Xing,

Cheng,

Wan

et al. 2023

Sensors

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The safety valves of powered supports control the maximum working resistance, and their statuses must be known to ensure the safety of both the support and the overlying strata. However, the inspection of powered support valves involves manual or semiautomated operations, the costs of which are high. In this study, an extreme point extraction method was developed for the determination of the characteristic parameters of safety valves using roof pressure data, and a safety valve state monitoring module was constructed. Using the longwall face of 0116306 with top coal caving in the Mindong Mine as an example, the characteristic parameters of the safety valves were extracted, including the peak, reseating, and blowdown pressures, as well as the recovery and unloading durations. The results of the field tests showed the following: (1) The amplitude threshold method based on extreme points can be used to accurately extract characteristic parameters, and the distribution of the characteristic parameters of the safety valves follows either a Gaussian or an exponential distribution. (2) The mining pressure analysis results, derived from the characteristic parameters, closely align with the in situ mining pressure observations. This method can be used for the online monitoring of safety valve conditions, increasing the operational efficiency and quality of safety valve inspections.

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Condition Assessment of Nuclear Power Plant Equipment Based on Machine Learning Methods: A Review

Cited by 10 publications

References 74 publications

Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions

Application of machine learning and computer vision methods to determine the size of NPP equipment elements in difficult measurement conditions

Summarization of Remaining Life Prediction Methods for Special Power Plants

Extraction and Application of Hydraulic Support Safety Valve Characteristic Parameters Based on Roof Pressure Data

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