Anomaly detection of rotary kiln sensor time series data based on deep learning

Kuiqiang Dong; Xiaohong Wang; Hongliang Yu

doi:10.1117/12.3009271

16 October 2023 Anomaly detection of rotary kiln sensor time series data based on deep learning

Kuiqiang Dong, Xiaohong Wang, Hongliang Yu

Proceedings Volume 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023); 128032W (2023) https://doi.org/10.1117/12.3009271
Event: 2023 5th International Conference on Artificial Intelligence and Computer Science (AICS 2023), 2023, Wuhan, China

Abstract

When establishing a visualization platform for rotary kiln calcination systems, it was found that the abnormality of industrial sensors needed to be detected in order to achieve timely repair of sensors and data correction. This paper proposes a new LSTM-SRU neural network based on multi-scale time-series fusion for anomaly detection. Firstly, a multi-scale time-series feature fusion mechanism is established based on the data, such as long-term periodicity and short-term volatility. Then, based on the prediction error, the optimal threshold is used to detect potential abnormal data. The algorithm is validated on an actual rotary kiln sensor dataset and demonstrates superiority over baseline methods such as LSTM and GRU.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kuiqiang Dong, Xiaohong Wang, and Hongliang Yu "Anomaly detection of rotary kiln sensor time series data based on deep learning", Proc. SPIE 12803, Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023), 128032W (16 October 2023); https://doi.org/10.1117/12.3009271

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