Abstract
Bigdata analysis has been the key to the abnormal detection of industrial systems using the Industrial Internet of Things (IIoT). How to effectively detect anomalies using industrial spatial-temporal sensor data is a challenging issue. Deep learning-based anomaly detection methods have been widely used for abnormal detection and fault identification with limited success. Temporal Convolutional Network (TCN) has the advantages of parallel structure, larger receptive field and stable gradient. In this work, we propose a new industrial anomaly detection model based on TCN, called IAD-TCN. In order to highlight the features related to anomalies and improve the detection ability of the model, we also introduce attention mechanism into the model. The experimental results over real industrial datasets show that the IAD-TCN model outperforms the traditional TCN model, the long short-term memory network (LSTM) model, and the bidirectional long short-term memory network model (BiLSTM).
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China, under Grant 62176122, 62001217; in part by A3 Foresight Program of NSFC, under Grant No. 62061146002; and in part by the Key Research and Development Program of Jiangsu Province, under Grant BE2019012.
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Yang, C., Chen, B., Deng, H. (2023). Integrated Bigdata Analysis Model for Industrial Anomaly Detection via Temporal Convolutional Network and Attention Mechanism. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13421. Springer, Cham. https://doi.org/10.1007/978-3-031-25158-0_12
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