Anomaly Detection for Asynchronous Multivariate Time Series of Nuclear Power Plants Using a Temporal-Spatial Transformer

Author:

Yi Shuang12ORCID,Zheng Sheng2,Yang Senquan34,Zhou Guangrong2,Cai Jiajun12

Affiliation:

1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China

2. College of Science, China Three Gorges University, Yichang 443002, China

3. China Nuclear Power Operation Technology Corporation, Ltd., Wuhan 430074, China

4. China Nuclear Industry Key Laboratory of Simulation Technology, Wuhan 430074, China

Abstract

Industrial process monitoring is a critical application of multivariate time-series (MTS) anomaly detection, especially crucial for safety-critical systems such as nuclear power plants (NPPs). However, some current data-driven process monitoring approaches may not fully capitalize on the temporal-spatial correlations inherent in operational MTS data. Particularly, asynchronous time-lagged correlations may exist among variables in actual NPPs, which further complicates this challenge. In this work, a reconstruction-based MTS anomaly detection approach based on a temporal-spatial transformer is proposed. It employs a two-stage temporal-spatial attention mechanism combined with a multi-scale strategy to learn the dependencies within normal operational data at various scales, thereby facilitating the extraction of temporal-spatial correlations from asynchronous MTS. Experiments on simulated datasets and real NPP datasets demonstrate that the proposed model possesses stronger feature learning capabilities, as evidenced by its improved performance in signal reconstruction and anomaly detection for asynchronous MTS data. Moreover, the proposed TS-Trans model enables earlier detection of anomalous events, which holds significant importance for enhancing operational safety and reducing potential losses in NPPs.

Funder

The CNNC Key Laboratory of Nuclear Industry Simulation Technology External Open Fund Project.

Publisher

MDPI AG

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