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Abstract
Digital twin technology is being applied in various fields, particularly in Industrial Internet of Things. Fault detection and predictive maintenance function is an important application of digital twin. Anomaly detection of time series data collected by various sensors is one of the main technical means to realize predictive maintenance and fault detection function. At present, anomaly detection in digital twin systems faces some problems: the lack of abnormally labeled data, the difficulty in capturing the correlation and temporal dependence between sensor data, and the difficulty in processing multi-variable and complex sensor data. With the development of data processing technology and artificial intelligence, various new algorithms for time series anomaly detection have emerged in an endless stream, which brings new technical solutions to the anomaly detection of digital twin systems. This paper presents a deep learning-based anomaly detection solution for digital twin systems, offering new perspectives and advanced methods for enhancing anomaly detection in digital twin systems.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Zeyin Su "Application of anomaly detection based on deep learning in digital twin", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132100Z (11 July 2024); https://doi.org/10.1117/12.3034763
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KEYWORDS
Data modeling
Detection and tracking algorithms
Sensors
Performance modeling
Education and training
Deep learning
Evolutionary algorithms
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