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Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training.

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Title: Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training.
Authors: Yang, Wenjie1 (AUTHOR), Chu, Wenchao2 (AUTHOR), Wu, Xingfu3 (AUTHOR), Zhou, Lianlin4 (AUTHOR), Wang, Jiayi1 (AUTHOR), Yang, Hua5 (AUTHOR) hua.yang@hebut.edu.cn, Li, Zirui5 (AUTHOR) lizirui@gmail.com
Source: International Journal of Computer Integrated Manufacturing. Dec2025, Vol. 38 Issue 12, p1697-1715. 19p.
Subject Terms: *TIME series analysis, *MULTIVARIATE analysis, ANOMALY detection (Computer security), LATENT variables, SPATIOTEMPORAL processes, MACHINE learning, FEATURE extraction
Abstract: To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computer Integrated Manufacturing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Wenjie%22">Yang, Wenjie</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chu%2C+Wenchao%22">Chu, Wenchao</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Xingfu%22">Wu, Xingfu</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhou%2C+Lianlin%22">Zhou, Lianlin</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wang%2C+Jiayi%22">Wang, Jiayi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Hua%22">Yang, Hua</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> hua.yang@hebut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Zirui%22">Li, Zirui</searchLink><relatesTo>5</relatesTo> (AUTHOR)<i> lizirui@gmail.com</i>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Integrated+Manufacturing%22">International Journal of Computer Integrated Manufacturing</searchLink>. Dec2025, Vol. 38 Issue 12, p1697-1715. 19p.
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  Data: *<searchLink fieldCode="DE" term="%22TIME+series+analysis%22">TIME series analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22MULTIVARIATE+analysis%22">MULTIVARIATE analysis</searchLink><br /><searchLink fieldCode="DE" term="%22ANOMALY+detection+%28Computer+security%29%22">ANOMALY detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22LATENT+variables%22">LATENT variables</searchLink><br /><searchLink fieldCode="DE" term="%22SPATIOTEMPORAL+processes%22">SPATIOTEMPORAL processes</searchLink><br /><searchLink fieldCode="DE" term="%22MACHINE+learning%22">MACHINE learning</searchLink><br /><searchLink fieldCode="DE" term="%22FEATURE+extraction%22">FEATURE extraction</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: To address the challenges faced in industrial anomaly detection, including data sample imbalance, lack of anomaly labels, and complex spatiotemporal relationships in high-dimensional data, this paper proposes a novel multi-modal time-series anomaly detection model that combines attention mechanisms and adversarial training. In this model, the first step involves utilizing graph attention mechanisms to extract sequence correlation features from multi-modal time-series data, which are then summed with the original data to form a dual-feature-based data representation. Subsequently, a self-supervised learning approach is employed to input this data representation into a variational autoencoder's encoding-decoding network for reconstruction. Anomaly detection is performed by analyzing the error between the input and reconstructed data. The model also employs spatiotemporal attention mechanisms and adversarial training during reconstruction to enhance feature extraction and model generalization. By comparing our proposed model to five commonly used baseline models, we demonstrate its effectiveness in detecting anomalies in scenarios involving high-dimensional data and imbalanced abnormal samples, demonstrating superior anomaly detection performance, as well as excellent performance on real industrial production and processing datasets. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of International Journal of Computer Integrated Manufacturing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/0951192X.2025.2452985
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1697
    Subjects:
      – SubjectFull: TIME series analysis
        Type: general
      – SubjectFull: MULTIVARIATE analysis
        Type: general
      – SubjectFull: ANOMALY detection (Computer security)
        Type: general
      – SubjectFull: LATENT variables
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      – SubjectFull: SPATIOTEMPORAL processes
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      – SubjectFull: MACHINE learning
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      – SubjectFull: FEATURE extraction
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    Titles:
      – TitleFull: Industrial multivariate time-series data anomaly detection incorporating attention mechanisms and adversarial training.
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            NameFull: Yang, Wenjie
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            NameFull: Chu, Wenchao
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            NameFull: Zhou, Lianlin
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            – D: 01
              M: 12
              Text: Dec2025
              Type: published
              Y: 2025
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