Research on Fault Prediction of Reactor Power Measurement Circuit Based on Relevance Vector Machine

Min Yuan; Chen Zhi; Wan Bo; Yang Cheng; Han Wenxing; Yuan Yannan

doi:10.13832/j.jnpe.2022.04.0223

Volume 43 Issue 4

Aug. 2022

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Min Yuan, Chen Zhi, Wan Bo, Yang Cheng, Han Wenxing, Yuan Yannan. Research on Fault Prediction of Reactor Power Measurement Circuit Based on Relevance Vector Machine[J]. Nuclear Power Engineering, 2022, 43(4): 223-229. doi: 10.13832/j.jnpe.2022.04.0223

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Min Yuan, Chen Zhi, Wan Bo, Yang Cheng, Han Wenxing, Yuan Yannan. Research on Fault Prediction of Reactor Power Measurement Circuit Based on Relevance Vector Machine[J]. Nuclear Power Engineering, 2022, 43(4): 223-229. doi: 10.13832/j.jnpe.2022.04.0223

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Research on Fault Prediction of Reactor Power Measurement Circuit Based on Relevance Vector Machine

doi: 10.13832/j.jnpe.2022.04.0223

Science and Technology on Reactor System Design Technology Laboratory, Nuclear Power Institute of China, Chengdu , 610213, China

Received Date: 2022-03-15
Rev Recd Date: 2022-04-01
Publish Date: 2022-08-04

Abstract

Abstract

In order to improve the supportability and maintainability of the nuclear measuring device, taking the reactor power measurement amplifier circuit as the object, this paper predicts the typical faults of the circuit through the multi-kernel relevance vector machine model based on quantum particle swarm optimization algorithm. From the pulse response signal of the power measurement amplifier circuit, the feature information is extracted by the wavelet packet decomposition method, and the Euclidean distance between the feature and the normal state feature of the circuit is calculated as the fault degree indicator of the circuit. Multi-kernel relevance vector machine is selected to establish circuit fault prediction model. The influence of kernel function type and parameter optimization algorithm of relevance vector machine model on the prediction effect of the model is analyzed. The research results show that the multi-kernel relevance vector machine model optimized by quantum particle swarm algorithm has better prediction accuracy for the future running state of the circuit, and can accurately predict the changing law of the fault degree of the circuit.
- Wavelet packet,
- Feature extraction,
- RVM,
- QPSO,
- Fault prediction

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References(18)

References

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