Diagnosis method for the break size of marine nuclear power plant by variable interval classification and probabilistic neural networks

Xinxin Liu; Lei Yu; Xiaolong Wang; Jianli Hao; Hongguang Xiao

doi:10.1117/12.2673525

22 May 2023 Diagnosis method for the break size of marine nuclear power plant by variable interval classification and probabilistic neural networks

Xinxin Liu, Lei Yu, Xiaolong Wang, Jianli Hao, Hongguang Xiao

Author Affiliations +

Proceedings Volume 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022); 126400P (2023) https://doi.org/10.1117/12.2673525
Event: International Conference on Internet of Things and Machine Learning (IoTML 2022), 2022, Harbin, China

Abstract

If the Probabilistic Neural Networks (PNN) based on the classification method of equal size interval in training data is used to diagnose the break size of the marine nuclear power plant, the accuracy of the diagnosis results is low when the break size is small. Therefore, a diagnosis method for the break size of a marine nuclear power plant based on the classification method of variable size interval and the PNN is proposed. First, the break sizes are classified according to the variable size interval. Then the data under different break sizes is generated, and the PNN is used to learn it. Next, the corresponding operation data is generated as the real-time data. Finally, the PNN model is used to diagnose the break size. The above processes are repeated with three different variable size interval classification methods, including the break size increasing proportionally, the break size interval increasing proportionally, and the break size interval increasing by arithmetic progression. The diagnosis results are compared with the classification method of equal size interval. And finally, the different classification methods of break size are combined for analysis. The results show that the use of variable size interval classification method that the break size interval changing according to arithmetic progression can increase the accuracy of diagnosis results by 1.21%, and combining it with the classification method of equal size interval can significantly increase the accuracy by 7.21%.

Citation Download Citation

Xinxin Liu, Lei Yu, Xiaolong Wang, Jianli Hao, and Hongguang Xiao "Diagnosis method for the break size of marine nuclear power plant by variable interval classification and probabilistic neural networks", Proc. SPIE 12640, International Conference on Internet of Things and Machine Learning (IoTML 2022), 126400P (22 May 2023); https://doi.org/10.1117/12.2673525

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