Integrated Bigdata Analysis Model for Industrial Anomaly Detection via Temporal Convolutional Network and Attention Mechanism

被引:0
作者
Yang, Chenze [1 ]
Chen, Bing [1 ]
Deng, Hai [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Nanjing, Peoples R China
来源
WEB AND BIG DATA, PT I, APWEB-WAIM 2022 | 2023年 / 13421卷
基金
中国国家自然科学基金;
关键词
Anomaly detection; Big data; Industrial Internet of Things (IIoT); Temporal convolutional network; Attention mechanism;
D O I
10.1007/978-3-031-25158-0_12
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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).
引用
收藏
页码:150 / 160
页数:11
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