Affiliation:
1. Beijing Key Lab of Petroleum Data Mining Department of Computer Science and Technology, China University of Petroleum, Beijing 102249, China
Abstract
Due to the complexity of the oil and gas station system, the operational data, with various temporal dependencies and inter-metric dependencies, has the characteristics of diverse patterns, variable working conditions and imbalance, which brings great challenges to multivariate time series anomaly detection. Moreover, the time-series reconstruction information of data from digital twin space can be used to identify and interpret anomalies. Therefore, this paper proposes a digital twin-driven MTAD-GAN (Multivariate Time Series Data Anomaly Detection with GAN) oil and gas station anomaly detection method. Firstly, the operational framework consisting of digital twin model, virtual-real synchronization algorithm, anomaly detection strategy and realistic station is constructed, and an efficient virtual-real mapping is achieved by embedding a stochastic Petri net (SPN) to describe the station-operating logic of behavior. Secondly, based on the potential correlation and complementarity among time series variables, we present a MTAD-GAN anomaly detection method to reconstruct the error of multivariate time series by combining mechanism of knowledge graph attention and temporal Hawkes attention to judge the abnormal samples by a given threshold. The experimental results show that the digital twin-driven anomaly detection method can achieve accurate identification of anomalous data with complex patterns, and the performance of MTAD-GAN anomaly detection is improved by about 2.6% compared with other methods based on machine learning and deep learning, which proves the effectiveness of the method.
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Cited by
17 articles.
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