Journal
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
Volume 19, Issue 3, Pages 1960-1970Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TASE.2021.3069003
Keywords
Pipelines; Monitoring; Data models; Oils; Spatiotemporal phenomena; Data acquisition; Transportation; Spatiotemporal dependencies; data recovery; generative adversarial networks (GANs); hierarchical framework; pipeline network
Categories
Funding
- National Key Research and Development Program of China [2018YFA0702200]
- National Natural Science Foundation of China [61773109, 62073064, 61627809, 61621004]
- Liaoning Revitalization Talents Program [XLYC1807009, XLYC1801005]
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This article proposes a hierarchical data recovery method based on generative adversarial networks (GANs) for real-time status monitoring of pipeline networks. The method comprises a hierarchical data recovery framework, a joint attention module, and macromicrodual discriminators to handle incomplete pressure data and ensure efficient recovery performance.
In the real-time status monitoring of pipeline network, incomplete pressure data are unavoidable due to some device or communication errors. To solve this problem, a hierarchical data recovery method based on generative adversarial networks (GANs) is proposed in this article. First, a hierarchical data recovery framework is proposed to handle different numbers of incomplete data due to the structure of the semicentral pipeline network. Second, a joint attention module is presented to capture both interior nature and correlation relationships of multivariate pressure series and further guarantee the consistency of pressure data. Third, the macromicrodual discriminators are proposed to evaluate the recovery result through the combination of the local and global variation in temporal and spatial dependencies. Based on the novel structures, the proposed model is able to recover incomplete data with abnormal fluctuation values, unreasonable fixed values, or missing values. Finally, under a series of data recovery experiments, the efficiency of the proposed method is evaluated. Experimental results demonstrate that the proposed method is a practical way to ensure data recovery performance in the pipeline network.
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