- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | WANG Fanyu, WANG Dongwei, DENG Qiang, ZHAO Yang, YAN Hao, CHEN Qi. Temperature Prediction of Key Chips in Nuclear Power Instrumentation and Control System Based on Machine Learning[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20240332
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To investigate the thermal characteristics inside the control and protection cabinet of nuclear safety-class instrumentation and control (I&C) systems and the variation patterns of the steady-state temperature (SST) of key chips (CPU and field programmable gate array (FPGA)), experimental studies were conducted on the cabinet under different ambient temperatures. The finite element method was employed to simulate the experimental process, and the accuracy of the numerical model was validated by comparing the experimental results. Furthermore, the SST values of CPU and FPGA under 100 sets of random working conditions were calculated by the finite element model, and the SST values of CPU and FPGA under different working conditions were learned and predicted using four algorithms of multi-output support vector regression (M-SVR), extreme gradient boosting (XGBoost), artificial neural network (ANN), and Bayesian ridge regression (BRR). Results show that when the ambient temperature is 20 ℃, the SST of CPU and FPGA is 37.5 ℃ and 33.5 ℃, respectively. When the ambient temperature is 55 ℃, the SST of the CPU and FPGA rises to 72 ℃ and 68 ℃, respectively. Finite element analysis can well simulate the test phenomenon, and the calculated chip SSTs are in good agreement with the experimental results. All the four algorithm models can be used to predict chip SST, among which the ANN algorithm exhibits the best prediction performance on the test set. It has a mean squared error (MSE) less than 0.15% and an
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