Robust deep auto-encoding network for real-time anomaly detection at nuclear power plants

Abstract

Detecting anomaly conditions in nuclear reactor is a critical issue in safety management of Nuclear Power Plants (NPPs). Conventionally, the operating status are monitored in transient data with pre-designed labels by human operators or basic diagnosis systems. Nowadays, continuous time series data from multi-sensors are increasingly collected and emerging unlabeled abnormal status are monitored during the operation, making it challenging to capture both spatial and temporal dependency at each time steps without supervised labels. In this paper, a robust unsupervised Multi-Variate Convolutional GRU Encoder-Dncoder (MVCGED) method is proposed to perform anomaly detection and fault diagnosis in multi-sensor operation time series data. Specifically, MVCGED first construct each time steps into signature matrices to maintain both spatial and temporal features via sliding windows with inner-correlation and forget mechanism. Subsequently, A CNN feature extraction network, CNN-based GRU encoding network and CNN decoding network are implemented successively to capture and reconstruct the hidden patterns of the signature matrices. Finally, the reconstruction loss are further utilized to detect anomalies and diagnose faults. Extensive empirical studies based on PCTRAN nuclear power plant operation data demonstrate that MVCGED outperforms commonly-used baseline methods.