Simulation-Based Fault Detection and Diagnosis for Electric Servomotor Control Rod Drives in BWRX-300
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Published
Oct 19, 2026
Tran C.H. Nguyen
Ark Ifeanyi
Daniel Dos Santos
Nicholas Fullilove
Abhinav Saxena
Jamie Coble
Abstract
GE-Hitachi’s BWRX-300 Small Modular Reactor design adopts innovations from the Advanced Boiling Water Reactor (ABWR), including the use of servomotor-driven fine motion control rod drives (FMCRDs) instead of the hydraulically-driven locking piston control rod drives (LPCRDs) commonly used in current boiling water reactors (BWRs). The FMCRDs provides more precise power control during normal operation and functional backup to the hydraulic SCRAM system during emergency scenarios. To address the challenge of monitoring the novel servomotor-driven FMCRDs in the BWRX-300, for which no operational data exist, this paper develops and validates a simulation-based, two-stage diagnostic framework. A model of the FMRCDs was created and verified in MATLAB/Simulink to generate comprehensive datasets for healthy operation and key fault modes, including stator short circuits and mechanical load failures. From these simulations, diagnostic features were extracted from motor currents and control response signals. The proposed framework first uses Auto-Associative Kernel Regression (AAKR) and a Sequential Probability Ratio Test (SPRT) for robust anomaly detection, achieving an 85.9% true positive rate with a 2.15% false positive rate. Upon detection, a second-stage multi-channel SPRT classifier diagnoses the specific fault, successfully identifying 100% of load faults and 75% of short faults. This study validates that a simulation-driven approach can establish an effective and interpretable predictive health monitoring system for critical reactor components before they are physically deployed, enabling condition-based maintenance from the outset.
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Keywords
Fine Motion Control Rod Drive, Anomaly Detection, Diagnostics, Nuclear Power
References
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Chen, X., Qin, P., Chen, Y., Zhao, J., Li, W., Mao, Y., and Zhao, T. (2022). Inter-turn short-circuit fault diagnosis of pmsm. Electronics, 11(10):1576.
Chen, Y., Liang, S., Li, W., Liang, H., and Wang, C. (2019). Faults and diagnosis methods of permanent magnet synchronous motors: A review. Applied Sciences, 9(10):2116.
Chetouani, Y. (2014). A sequential probability ratio test (sprt) to detect changes and process safety monitoring. Process Safety and Environmental Protection, 92(3):206–214.
Coble, J., Ramuhalli, P., Bond, L., Hines, W., and Upadhyaya, B. (2015). A review of prognostics and health management applications in nuclear power plants. International Journal of Prognostics and Health Management, 6(3).
Cocconcelli, M., Zimroz, R., Rubini, R., and Bartelmus, W. (2012). Stft based approach for ball bearing fault detection in a varying speed motor. In Condition Monitoring of Machinery in Non-Stationary Operations, pages 41–50. Springer.
Cusido, J., Romeral, L., Ortega, J. A., Garcia, A., and Riba, J. (2011). Signal injection as a fault detection technique. Sensors, 11(3):3356–3380.
Di Maio, F., Vagnoli, D., Zio, E., and Zappa, E. (2013). Fault detection in nuclear power plants components by a combination of statistical methods. IEEE Transactions on Reliability, 62(4):833–845.
El Hachemi Benbouzid, M. (2000). A review of induction motors signature analysis as a medium for faults detection. IEEE Transactions on Industrial Electronics, 47(5):984–993.
GE Hitachi Nuclear Energy (2023). Bwrx-300 general description. Technical Report 005N9751, Rev. E, GE Hitachi Nuclear Energy.
Han, D., Zhang, Y., Yu, Y., Tian, J., and Shi, P. (2024). Multisource heterogeneous information fusion fault diagnosis method based on deep neural networks under limited datasets. Applied Soft Computing, 154:111371.
Henao, H., Capolino, G.-A., Fernandez-Cabanas, M., Filippetti, F., Bruzzese, C., Strangas, E., Pusca, R., Estima, J., Riera-Guasp, M., and Hedayati-Kia, S. (2014). Trends in fault diagnosis for electrical machines: A review of diagnostic techniques. IEEE Industrial Electronics Magazine, 8(2):31–42.
Hines, J. W., Seibert, R., and Arndt, S. A. (2006). Technical review of on-line monitoring techniques for performance assessment. Technical Report NUREG/CR-6895, U.S. Nuclear Regulatory Commission.
Hu, G., Zhou, T., and Liu, Q. (2021). Data-driven machine learning for fault detection and diagnosis in nuclear power plants: A review. Frontiers in Energy Research, 9.
Huang, X., Zhang, J., Wu, Y., Xu, Z., and Cheng, M. (2025). Speed signature analysis for servo motor bearing fault detection under dynamic load conditions. IEEE Transactions on Power Electronics, 40(5):7275–7285.
Ifeanyi, A. O., Dos Santos, D., Saxena, A., and Coble, J. (2024). Fault detection and isolation in simulated batch operation of fine motion control rod drives. Nuclear Technology, 210(12):2387–2403.
International Atomic Energy Agency (1997). Status of advanced light water reactor designs 1996. Technical Report IAEA-TECDOC-968, International Atomic Energy Agency, Vienna. See section: Advanced Boiling Water Reactor (ABWR).
International Atomic Energy Agency (2009). Passive safety systems and natural circulation in water-cooled nuclear power plants. Technical Report IAEA-TECDOC-1624, IAEA, Vienna.
International Atomic Energy Agency (2020). Status report – abwr (geh, hgne and toshiba). Advanced Reactors Information System (ARIS), https://aris.iaea.org.
Isermann, R. (2011). Fault-Diagnosis Systems: An Introduction from Fault Detection to Fault Tolerance. Springer, Berlin.
Jardine, A. K. S., Lin, D., and Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7):1483–1510.
Ji, D.-Y., Wang, B., Inoue, H., and Kanemaru, M. (2025). Motor fault detection with a hybrid physics-based and data-driven method. In Proceedings of the 2025 IEEE International Electric Machines and Drives Conference (IEMDC), pages 1178–1183. IEEE.
Ji, M., Peng, G., He, J., Liu, S., Chen, Z., and Li, S. (2021). A two-stage, intelligent bearing-fault-diagnosis method using order-tracking and a one-dimensional convolutional neural network with variable speeds. Sensors, 21(3):675.
Jung, S., Kim, J., and Kim, S. (2025). A hybrid fault detection method of independent component analysis and auto-associative kernel regression for process monitoring in power plant. IEEE Access, 13:39135–39151.
Kelley, J. and Hagan, M. (2020). New fault diagnosis procedure and demonstration on hydraulic servo-motor for single faults. IEEE/ASME Transactions on Mechatronics, 25(3):1499–1509.
Kim, Y., Bae, H., Kim, S., and Vachtsevanos, G. (2008). Fault diagnosis of ac servo motor with current signals based on wavelet decomposition and template matching methods. In Proceedings of the 17th IFAC World Congress, volume 41 of IFAC Proceedings Volumes, pages 7239–7244.
Knief, R. A. (2008). Nuclear Engineering: Theory and Technology of Commercial Nuclear Power. American Nuclear Society, 2 edition.
Krause, P. C., Wasynczuk, O., and Sudhoff, S. D. (2015). Analysis of Electric Machinery and Drive Systems. IEEE Press.
Krichen, M., Elbouchikhi, E., Benhadj, N., Chaieb, M., Benbouzid, M., and Neji, R. (2020). Motor current signature analysis-based permanent magnet synchronous motor demagnetization characterization and detection. Machines, 8(3):35.
Kumar, R. R., Andriollo, M., Cirrincione, G., Cirrincione, M., and Tortella, A. (2022). A comprehensive review of conventional and intelligence-based approaches for the fault diagnosis and condition monitoring of induction motors. Energies, 15(23):8938.
Lee, H., Raouf, I., Song, J., Kim, H. S., and Lee, S. (2023). Prognostics and health management of the robotic servomotor under variable operating conditions. Mathematics, 11(2):398.
Li, W., Peng, M., Yang, M., Xia, G., Wang, H., Jiang, N., and Ma, Z. (2017). Design of comprehensive diagnosis system in nuclear power plant. Annals of Nuclear Energy, 109:92–102.
Li, X., Zhang, W., and Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multiscale feature extraction. Reliability Engineering & System Safety, 182:208–218.
Liu, J., Inoue, M., and Kanemaru, M. (2022). Robust motor current signature analysis (mcsa)-based fault detection under varying operating conditions. Technical Report TR2022-150, Mitsubishi Electric Research Laboratories.
MathWorks (2023a). Ac6 – pm synchronous 3hp motor drive. https://www.mathworks.com/help/sps/powersys/ref/pmsynchronousmotordrive.html. MathWorks (2023b). Simscape User’s Guide. Accessed: 2023.
Messaoudi, M. and Sbita, L. (2010). Multiple faults diagnosis in induction motor using the mcsa method. International Journal of Signal Image Processing, 1(3).
Milfont, L. D., Torllone de Carvalho Ferreira, G., and Giesbrecht, M. (2024). Fault diagnosis in pmsm motors: A review of techniques based on mathematical models. In Proceedings of the IECON 2024 – 50th Annual Conference of the IEEE Industrial Electronics Society, pages 1–6. IEEE.
Miljkovíc, D. (2015). Brief review of motor current signature analysis. HDKBR Info Magazin, 5(1):14–26.
Min, H., Kim, H., Sohn, S., and Bae, Y. (2017). A study on the application of aakr based early warning system for ice. PHM Society Asia-Pacific Conference, 1(1).
Moustafa,W., Cousinard, O., Bolaers, F., Sghir, K., and Dron, J.-P. (2014). Low-speed bearings fault detection and size estimation using instantaneous angular speed. Journal of Vibration and Control, 22(15):3413–3425.
Nandi, S., Toliyat, H. A., and Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors— a review. IEEE Transactions on Energy Conversion, 20(4):719–729.
OECD Nuclear Energy Agency (2021). Small Modular Reactors: Challenges and Opportunities. OECD Publishing, Paris.
Papananias, M., Sztendel, S., and Pislaru, C. (2015). Development of a novel multibody mechatronic model for five-axis cnc machine tool. In Proceedings of the EUSPEN Conference.
Pei, D., Yue, J., and Jiao, J. (2023). A novel method for bearing fault diagnosis under variable speed based on envelope spectrum fault characteristic frequency band identification. Sensors, 23(9):4338.
Peng, Z. K., Jackson, M. R., Rongong, J. A., Chu, F. L., and Parkin, R. M. (2009). On the energy leakage of discrete wavelet transform. Mechanical Systems and Signal Processing, 23(2):330–343.
Pietrzak, P. and Wolkiewicz, M. (2021). Comparison of selected methods for the stator winding condition monitoring of a pmsm using the stator phase currents. Energies, 14(6):1630.
Pietrzak, P. and Wolkiewicz, M. (2022). Stator winding fault detection of permanent magnet synchronous motors based on the short-time fourier transform. Power Electronics and Drives, 7(1):112–133.
Qi, B., Liang, J., and Tong, J. (2023). Fault diagnosis techniques for nuclear power plants: A review from the artificial intelligence perspective. Energies, 16(4):1850.
Qin, S. J. (2003). Statistical process monitoring: Basics and beyond. Journal of Chemometrics, 17(8–9):480.
Qiu, D., Sun, M., Fan, H., Shao, J., Lin, Y., Huo, Z., and Zhu, J. (2021). Servo dc motor fault diagnosis driven by multi-source heterogeneous data. In 2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS), pages 55–59.
Randall, R. B. (2011). Vibration-Based Condition Monitoring: Industrial, Aerospace and Automotive Applications. John Wiley & Sons, Chichester, UK.
Raouf, I., Kumar, P., and Kim, H. S. (2024). Deep learning-based fault diagnosis of servo motor bearing using the attention-guided feature aggregation network. Expert Systems with Applications, 258:125137.
Reddy, B., Gursel, E., Daniels, K., Khojandi, A., Coble, J. B., Agarwal, V., Boring, R., Yadav, V., and Madadi, M. (2024). Uncertainty-aware and explainable human error detection in the operation of nuclear power plants. Nuclear Technology, 210(12):2312–2330.
Roura, J. and Romeral Martínez, J. L. (2011). Transient analysis and motor fault detection using the wavelet transform. In Galperin, M., editor, Discrete Wavelet Transforms: Theory and Applications, pages 19–61.
Shao, H., Jiang, H., Wang, F., and Zhao, H. (2017). An enhancement deep feature fusion method for rotating machinery fault diagnosis. Knowledge-Based Systems, 119:200–220.
Sobie, C., Freitas, C., and Nicolai, M. (2018). Simulation-driven machine learning: Bearing fault classification. Mechanical Systems and Signal Processing, 99:403–419.
Subramanian, A., Saxena, A., and Coble, J. (2023). Servomotor dataset: Modeling health in mechanisms with typically intermittent operation. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, volume 15.
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