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Research on Fault Prediction of Nuclear Safety Class Crucial Circuit Based on SDAE- MKRVM

Published: 31 July 2024 Publication History

Abstract

To improve the reliability and maintainability of the nuclear safety class DCS system, this paper conducts a study on fault prediction of key components in the output circuit of the nuclear safety class signal conditioning module. To address the issues of insufficient feature extraction for minor offset fault characteristics and low accuracy of fault prediction, we proposed a prediction model based on stacked denoising autoencoder (SDAE) feature extraction and improved multi-kernel relevance vector machine (MKRVM) model. Therefore, fault simulation modeling is performed for key components of the signal output circuit to obtain fault datasets of key components, and the SDAE model is used to extract fault features. And the fault degree index is obtained by calculating the Pearson correlation coefficient between these fault features.The fault prediction model based on MKRVM is established, and the combination coefficients of the kernel functions in the MKRVM model are optimized using adaptive gray wolf optimization algorithm (AGWO). The prediction performance evaluation indicators are used to evaluate the prediction results of the AGWO-MKRVM model, RVM model. The results show that the MKRVM model optimized by AGWO has better prediction accuracy for the faults of the circuit critical components, moreover, can accurately and stably predict the fault trend of the circuit.

References

[1]
Zhao S, Chen S, Yang F, A composite failure precursor for condition monitoring and remaining useful life prediction of discrete power devices [J]. IEEE Transactions on Industrial Informatics, 2020, 17(1): 688-698.
[2]
Cheng Y, Wang C, Wu J, Multi-dimensional recurrent neural network for remaining useful life prediction under variable operating conditions and multiple fault modes [J]. Applied Soft Computing, 2022, 118: 108507.
[3]
Rathnapriya S, Manikandan V. Remaining useful life prediction of analog circuit using improved unscented particle filter [J]. Journal of Electronic Testing, 2020, 36: 169-181.
[4]
Hu W, Zhu X, Fan H, A novel method for analog circuit fault prediction based on IAALO-SVM [C]//12th International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE 2022). IET, 2022, 2022: 1676-1682.
[5]
WANG Li,SHI Lichao. Fault prediction for analog circuits based on improved correlation vector machines [J]. Computer Applications and Software, 2023, 40(03):52-60.
[6]
Chaolong Zhang, Yigang He, Lifen Yuan, A Novel Approach for Analog Circuit Fault Prognostics Based on Improved RVM [J]. Journal of Electronic Testing, 2014, 30(3): 343-356.
[7]
Chaolong Z,Yigang H,Lifeng Y, Analog Circuit Incipient Fault Diagnosis Method Using DBN Based Features Extraction [J]. IEEE Access, 2018, 6.
[8]
Du X, Jia L, Haq I U. Fault diagnosis based on SPBO-SDAE and transformer neural network for rotating machinery [J]. Measurement, 2022, 188: 110545.
[9]
Vincent P, Larochelle H, Bengio Y, Extracting and composing robust features with denoising autoencoders [C]//Proceedings of the 25th international conference on Machine learning. 2008: 1096-1103.
[10]
Vincent P, Larochelle H, Lajoie I, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion [J]. Journal of machine learning research, 2010, 11(12).
[11]
Tipping M. The relevance vector machine [J]. Advances in neural information processing systems, 1999, 12.
[12]
Min Y, Chen Z, Wan B, Fault prediction study of reactor power measurement circuit based on correlation vector machine [J]. Nuclear Power Engineering, 2022, 43(04): 223-229.
[13]
Wang J, Feng G, Yi C, Electronic circuit fault diagnosis based on an ACO-RVM classifier with a new nonlinearly mixed kernel function [C]//2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019: 332-337.
[14]
Zhang C, He Y, Yuan L, A multiple heterogeneous kernel RVM approach for analog circuit fault prognostic [J]. Cluster Computing, 2019, 22: 3849-3861.
[15]
Meidani K, Hemmasian A P, Mirjalili S, Adaptive grey wolf optimizer [J]. Neural Computing and Applications, 2022, 34(10): 7711-7731.
[16]
Binu D, Kariyappa B S. Rider-deep-LSTM network for hybrid distance score-based fault prediction in analog circuits [J]. IEEE Transactions on Industrial Electronics, 2020, 68(10): 10097-10106.

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PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
January 2024
969 pages
ISBN:9798400716638
DOI:10.1145/3674225
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Association for Computing Machinery

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Published: 31 July 2024

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