Abstract
Modern manufacturing processes are influenced by smart factories, which collect data from multiple sensors in real time. However, detecting anomalies is challenging due to their irregular and complex patterns, often resulting in unreliable labeling that depends on the engineer's expertise. Moreover, in these processes, where sensors are interconnected, fluctuations in one variable can potentially influence subsequent variables. The proposed method uses cross-variable attention to reflect the relationship between variables at different time points. It also utilizes contrastive learning to extract a representation of only normal data that can be distinguished from anomalies without labels. Experimental results on multivariate time series data demonstrate that the proposed method outperforms existing models in anomaly detection.
Original language | English |
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Title of host publication | 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 638-643 |
Number of pages | 6 |
ISBN (Electronic) | 9798350344349 |
DOIs | |
Publication status | Published - 2024 |
Event | 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 - Osaka, Japan Duration: 2024 Feb 19 → 2024 Feb 22 |
Publication series
Name | 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 |
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Conference
Conference | 6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024 |
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Country/Territory | Japan |
City | Osaka |
Period | 24/2/19 → 24/2/22 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
Keywords
- Anomaly Detection
- Contrastive Learning
- Cross-Variable Attention
- Multivariate Time Series
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Information Systems
- Safety, Risk, Reliability and Quality
- Health Informatics