Sensor anomaly detection for small modular reactors utilizing improved autoencoder - Zhang, Bowen; Zhu, Hao; Cheng, Shouyu; Ma, Haochen (Nuclear engineering and design, 2024)
•Correction of Nomenclature and Grammar: The entire manuscript was thoroughly checked, and proprietary nomenclature errors and grammar mistakes pointed out by the reviewers were rectified.•Redesigned Clearer Figures: The figures were redesigned to improve clarity and facilitate easier comprehension for readers.•Elimination of Redundant Content: Extensive efforts were made to remove redundant or repetitive content that did not contribute substantially to the core message of the manuscript.•Addition of Threshold Setting Explanation in Section 4.3.3: In response to reviewer comments, a relevant explanation regarding the setting of the constant threshold was included in Section 4.3.3.•Rewriting of Conclusion Section: The conclusion section underwent a comprehensive rewrite to improve clarity and conciseness. The anomaly detection of widely distributed sensors in small modular reactors (SMRs) is critical to the safe operation of systems. Existing methods of sensor anomaly detection for transient conditions are less studied and have some shortcomings, necessitating further research in this context. Therefore, this paper presents an unsupervised deep-learning framework for a gated recurrent autoencoder with the Luong attentional mechanism and the residual connection, which is a multiple-input and multiple-output model. A single model can monitor SMR systems equipped with many sensors, and the training of the model does not require an anomaly label. The general threshold-setting method of the autoencoder for anomaly detection is also improved. The Bayesian estimation algorithm sets a dynamic threshold value in the residual evaluation stage. Finally, simulation tests are performed on experimental data sets, and the residual series obtained from the output of this framework are examined for anomalous sensors using Bayesian estimation methods. The mean squared error (MSE), that is, the average squared difference between the reconstructed value and the actual standard value, is used as the evaluation index. The experimental results demonstrate that the MSE of the improved autoencoder is smaller than that of the unimproved model in both transient and steady-state conditions. In addition, the dynamic threshold method reduces the false alarm rate of anomaly detection.