A Deep Learning Approach to Within-Bank Fault Detection and Diagnostics of Fine Motion Control Rod Drives



Published Feb 20, 2024
Ark Oluwatobi Ifeanyi Jamie B. Coble Abhinav Saxena


Control rod motion is one of the primary means of regulating the rate of fission in a nuclear reactor core to ensure safe and stable operation. Reactor power distribution and thermal power output can be fine-tuned by adjusting the control rod position. For high-precision control of rod movements, Fine Motion Control Rod Drives (FMCRDs) are often used. The operation of FMCRDs provides a unique opportunity to implement condition monitoring related to the intermittency of motion and the use of control rod banks. This research sets out to detect three types of faults in an electrically driven FMCRD. In addition to detecting faults, this work will attempt to determine both the type of fault and the source of each fault, completing the fault detection and diagnostics (FDD) pipeline on a scarcely researched system. The three types of faults to be investigated are short-circuit faults, ball screw wear faults, and ball screw jam faults. This is a potential advancement to the within-bank FDD of this specific drive system intended for deployment in an advanced nuclear reactor plant. Using encoder-decoder structured convolutional neural networks and autoencoders, the three tested faults were confidently detected and isolated as well as reasonably diagnosed by monitoring the FMCRD servomotor torque.

Abstract 102 | PDF Downloads 138



Anomaly Detection, Autoencoders, Control Rod Drive Mechanism, Servomotors, Small Modular Reactors, Motor Faults

Ahmed, S. F., Alam, M. S. B., Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., . . . Gandomi, A. H. (2023). Deep learning modelling techniques: current progress, applications, advantages, and challenges. Artificial Intelligence Review, 1–97.
AP News. (Accessed: Oct. 19, 2023). 1st small modular nuclear reactor certified for use in US. Retrieved from https://apnews.com/article/us-nuclearregulatory-commission-oregon-climateand-environment-business-designe5c54435f973ca32759afe5904bf96ac
Azizjon, M., Jumabek, A., & Kim, W. (2020). 1d cnn based network intrusion detection with normalization on imbalanced data. In 2020 international conference on artificial intelligence in information and communication (icaiic) (pp. 218–224).
Cartocci, N., Napolitano, M. R., Costante, G., & Fravolini, M. L. (2021). A comprehensive case study of data-driven methods for robust aircraft sensor fault isolation. Sensors, 21(5), 1645.
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Chandola, V., & Banerjee, A. (n.d.). V., k.(2009). anomaly detection: A survey. ACM Computing survey, 41.
Chen, Z., Yeo, C. K., Lee, B. S., & Lau, C. T. (2018). Autoencoder-based network anomaly detection. In 2018 wireless telecommunications symposium (wts) (pp. 1–5).
Energy.gov. (Accessed: Oct. 19, 2023). NRC Certifies First U.S. Small Modular Reactor Design. Retrieved from https://www.energy.gov/ne/articles/nrc-certifies-first-us-small-modular-reactor-design
Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1d cnn classifier. Journal of Signal Processing Systems, 91, 179–189.
Fullilove, N., Dos Santos, D., Saxena, A., & Coble, J. (2022). Leveraging within-bank comparison for anomaly detection, diagnostics, and prognostics in advanced nuclear power plants. In Annual conference of the phm society (Vol. 14).
Givnan, S., Chalmers, C., Fergus, P., Ortega-Martorell, S., & Whalley, T. (2022). Anomaly detection using autoencoder reconstruction upon industrial motors. Sensors, 22(9), 3166.
Goodge, A., Hooi, B., Ng, S. K., & Ng, W. S. (2021). Robustness of autoencoders for anomaly detection under adversarial impact. In Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence (pp. 1244–1250).
Ifeanyi, A., Saxena, A., & Coble, J. (2023). Within-bank condition monitoring and fault detection of fine motion control rod drives. 13th Nuclear Plant Instrumentation, Control & Human-Machine Interface Technologies (NPIC&HMIT 2023), 66(8), 1053–1062.
Jie, Z., Cuiyun, P., Pan, H., & Caixue, L. (2019). Research on current monitoring and fault diagnosis technology for control rod drive mechanism. 核动力工程, 40(1), 172–175.
Jung, J.-H., Lee, J.-J., & Kwon, B.-H. (2006). Online diagnosis of induction motors using mcsa. IEEE Transactions on Industrial Electronics, 53(6), 1842–1852.
Kowalski, C. T., & Orlowska-Kowalska, T. (2003). Neural networks application for induction motor faults diagnosis. Mathematics and computers in simulation, 63(3-5), 435–448.
Liang, X., Duan, F., Bennett, I., & Mba, D. (2020). A sparse autoencoder-based unsupervised scheme for pump fault detection and isolation. Applied Sciences, 10(19), 6789.
Louis, H. K., Refeat, R. M., & Hassan, M. I. (2021). Control rod shadowing effect in pwr core utilizing uraniagadolinia fuel. Progress in Nuclear Energy, 142, 103993.
Mehala, N., & Dahiya, R. (2007). Motor current signature analysis and its applications in induction motor fault diagnosis. International journal of systems applications, engineering & development, 2(1), 29–35.
Messaoudi, M., & Sbita, L. (2010). Multiple faults diagnosis in induction motor using the mcsa method. International Journal of Signal & Image Processing, 1(3).
Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement, 163, 107929.
Shim, J., Lim, G. C., & Ha, J.-I. (2022). Unsupervised anomaly detection for electric drives based on variational auto-encoder. In 2022 ieee applied power electronics conference and exposition (apec) (pp. 1703–1708).
Subramanian, A., Saxena, A., & Coble, J. (2023). Servomotor dataset: Modeling health in mechanisms with typically intermittent operation. In Annual conference of the phm society (Vol. 15). doi: 10.36001/phmconf.2023.v15i1.3580
Tang, W., Long, G., Liu, L., Zhou, T., Jiang, J., & Blumenstein, M. (2020). Rethinking 1d-cnn for time series classification: A stronger baseline. arXiv preprint arXiv:2002.10061, 1–7.
Vincent, P., Larochelle, H., Bengio, Y., & Manzagol, P.-A. (2008). Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on machine learning (pp. 1096–1103).
Yan, Z., Yao, Y., Huang, T.-B., & Wong, Y.-S. (2018). Reconstruction-based multivariate process fault isolation using bayesian lasso. Industrial & Engineering Chemistry Research, 57(30), 9779–9787.
Yin, X.-X., Sun, L., Fu, Y., Lu, R., Zhang, Y., et al. (2022). U-net-based medical image segmentation. Journal of Healthcare Engineering, 2022.
Zhongming, Y., & Bin, W. (2000). A review on induction motor online fault diagnosis. In Proceedings ipemc 2000. third international power electronics and motion control conference (ieee cat. no. 00ex435) (Vol. 3, pp. 1353–1358).
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