Author
Listed:
- Fanjie Meng
(School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China)
- Liwei Ma
(School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, China)
- Yixin Chen
(Key Laboratory of Expressway Construction Machinery of Shaanxi Province, Chang’an University, Xi’an 710064, China)
- Wangpeng He
(School of Aerospace Science and Technology, Xidian University, Xi’an 710071, China)
- Zhaoqiang Wang
(High-Tech Institute of Xi’an, Xi’an 710025, China)
- Yu Wang
(School of Mechanical Engineering, Xi’an Jiao Tong University, Xi’an 710049, China)
Abstract
With the rapid development of sensor technology, the anomaly detection of multi-source time series data becomes more and more important. Traditional anomaly detection methods deal with the temporal and spatial information in the data independently, and fail to make full use of the potential of spatio-temporal information. To address this issue, this paper proposes a novel integration method that combines sensor embeddings and temporal representation networks, effectively exploiting spatio-temporal dynamics. In addition, the graph neural network is introduced to skillfully simulate the complexity of multi-source heterogeneous data. By applying a dual loss function—consisting of a reconstruction loss and a prediction loss—we further improve the accuracy of anomaly detection. This strategy not only promotes the ability to learn normal behavior patterns from historical data, but also significantly improves the predictive ability of the model, making anomaly detection more accurate. Experimental results on four multi-source sensor datasets show that our proposed method performs better than the existing models. In addition, our approach enhances the ability to interpret anomaly detection by analyzing the sensors associated with the detected anomalies.
Suggested Citation
Fanjie Meng & Liwei Ma & Yixin Chen & Wangpeng He & Zhaoqiang Wang & Yu Wang, 2024.
"Integrating Sensor Embeddings with Variant Transformer Graph Networks for Enhanced Anomaly Detection in Multi-Source Data,"
Mathematics, MDPI, vol. 12(17), pages 1-14, August.
Handle:
RePEc:gam:jmathe:v:12:y:2024:i:17:p:2612-:d:1462830
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:17:p:2612-:d:1462830. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.