ECOC-based integrated learning method for fault diagnosis in nuclear power plants

[1] Shahriari, B., Swersky, K., Wang, Z., et al. (2016). Taking the Human Out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE, 104(1), 148-175. Search in Google Scholar

[2] Chi, Y. (2022). Frame Structure Fault Diagnosis Based on a High-Precision Convolution Neural Network. Sensors, 22. Search in Google Scholar

[3] Ling, J., Liu, G. J., Li, J. L., et al. (2020). Fault prediction method for nuclear power machinery based on Bayesian PPCA recurrent neural network model. Nuclear Science and Techniques, 31(8), 75. Search in Google Scholar

[4] Marmouch, S., Aroui, T., Koubaa, Y. (2021). Statistical Neural Networks for Induction Machine Fault Diagnosis and Features Processing Based on Principal Component Analysis. IEEJ Transactions on Electrical and Electronic Engineering, 16(2), 307-314. Search in Google Scholar

[5] Zhang, Q., Ma, Q., Liu, M., et al. (2020). Research on the software reliability quantitative evaluation of nuclear power plant digital control system based on non-homogeneous poisson process model. Annals of Nuclear Energy, 144, 107589. Search in Google Scholar

[6] Shin, H., Jung, S. H., Choi, Y. R., et al. (2018). Development of a shared remote control robot for aerial work in nuclear power plants. Nuclear Engineering and Technology, 50(4), 613-618. Search in Google Scholar

[7] Hengra, N. H., Ilyas, M., & Inayat, M. H. (2022). Severe accident analysis of the qinshan nuclear power plant and evaluation of boundary conditions for ex-vessel heat transfer. Progress in Nuclear Energy, 143(9), 104032. Search in Google Scholar

[8] Wang, Y. (2022). A convolutional neural network method based on Adam optimizer with power-exponential learning rate for bearing fault diagnosis. Journal of Vibroengineering, 24(4), 13. Search in Google Scholar

[9] Ye, X., Yan, J., Wang, Y., et al. (2022). A Novel Capsule Convolutional Neural Network with Attention Mechanism for High-Voltage Circuit Breaker Fault Diagnosis. Electric Power Systems Research, (Aug.), 209. Search in Google Scholar

[10] Tang, S., Zhu, Y., Yuan, S. (2022). A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images. Advanced Engineering Informatics, 52, 101554-. Search in Google Scholar

[11] Jia, L., Gao, Q., Liu, Z., et al. (2020). Multidisciplinary fault diagnosis of complex engineering systems: A case study of nuclear power plants. International Journal of Industrial Ergonomics, 80, 103060. Search in Google Scholar

[12] Kamel, K., Kamel, E. (2019). Process control ladder logic trouble shooting techniques fundamentals. IRO Journal on Sustainable Wireless Systems, 1(4), 206-241. Search in Google Scholar

[13] Kim, B., Lee, J. S., Ahn, Y. H. (2020). Fuzzy based risk assessment for decommissioning concrete bioshield structures in nuclear power plants: structural risks and worker safety. Applied Sciences, 10(7), 2614. Search in Google Scholar

[14] Du, T., Zhang, H., Wang, L. (2019). Analogue circuit fault diagnosis based on convolution neural network. Electronics Letters, 55(24). Search in Google Scholar

[15] Liu, S., Zhang, Z., Guan, C., et al. (2021). An improved fireworks algorithm for the constrained single-row facility layout problem. International Journal of Production Research, 59(8), 2309-2327. Search in Google Scholar

[16] Zhao, X., Wang, X., Golay, M. W. (2023). Bayesian Network–Based Fault Diagnostic System for Nuclear Power Plant Assets. Nuclear Technology, 1-18. Search in Google Scholar

[17] Yu, Y., Guo, F., Lu, W., et al. (2022). Real-time modeling of transient behavior of the primary coolant system in pool-type sodium cooled fast reactor. Annals of Nuclear Energy, 173, 109101-. Search in Google Scholar

[18] Alsanousie, A. A., Elsamni, O. A., Attia, A. E., et al. (2021). Transient and troubleshoots management of aged small-scale steam power plants using Aspen Plus Dynamics. Energy, 223, 120079. Search in Google Scholar