Research on sensor data optimization technology for thermal hydraulic experiment of nuclear reactor
[Display omitted] •Adaptive features based on data feature is used for noise removal for the first time.•The CCR is proposed for the first time to evaluate the denoising effect.•Obtained a more efficient, versatile and intelligent signal denoising method.•Improve the quality of experimental data, pr...
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Veröffentlicht in: | Nuclear engineering and design 2024-07, Vol.423, p.113176, Article 113176 |
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Sprache: | eng |
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•Adaptive features based on data feature is used for noise removal for the first time.•The CCR is proposed for the first time to evaluate the denoising effect.•Obtained a more efficient, versatile and intelligent signal denoising method.•Improve the quality of experimental data, promote the engineering application of AI.
The sensor signals collected by the nuclear reactor thermal hydraulic experimental system are mixed with complex noise information. The uncertainty of sensor data directly affects the analysis and evaluation effect of machine learning algorithms on the operating status of the experimental system. Traditional denoising methods have poor adaptability to different types of noisy data, are highly dependent on researchers and have high design cost. This paper proposes a data optimization technology based on a hybrid adaptive real-time denoising (HART) model, which can realize data distribution self-adaptation and algorithm hyperparameter self-optimization according to the characteristics of noisy source data. Through the joint denoising algorithm with Variational Mode Decomposition (VMD) algorithm as the core, the partition denoising of source data is realized. In addition, the practical application of the source data of thermal hydraulic experiments in the nuclear field and the verification of simulation and noise addition experiments have been completed. The results show that the denoising optimization model proposed in this paper has the characteristics of good adaptability, self-optimization and real-time processing for different sensor signals. It can effectively remove noise information, restrain data uncertainty, and provide high-quality data source for experimental data analysis based on machine learning. At the same time, the optimization technology can be further applied to the optimization of sensor data of nuclear power plants (NPP). |
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ISSN: | 0029-5493 |
DOI: | 10.1016/j.nucengdes.2024.113176 |