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Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals

文献类型：期刊论文

作者Zhang, Yuxin3,4,5; Chen, Yiqiang2,3,4; Wang, Jindong1; Pan, Zhiwen3,4
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023-02-01
卷号35期号:2页码:2118-2132
关键词Anomaly detection Predictive models Data models Autoregressive processes Image reconstruction Forecasting Computational modeling Unsupervised anomaly detection multi-sensor time series convolutional autoencoder attention based BiLSTM
ISSN号1041-4347
DOI10.1109/TKDE.2021.3102110
英文摘要Nowadays, multi-sensor technologies are applied in many fields, e.g., Health Care (HC), Human Activity Recognition (HAR), and Industrial Control System (ICS). These sensors can generate a substantial amount of multivariate time-series data. Unsupervised anomaly detection on multi-sensor time-series data has been proven critical in machine learning researches. The key challenge is to discover generalized normal patterns by capturing spatial-temporal correlation in multi-sensor data. Beyond this challenge, the noisy data is often intertwined with the training data, which is likely to mislead the model by making it hard to distinguish between the normal, abnormal, and noisy data. Few of previous researches can jointly address these two challenges. In this paper, we propose a novel deep learning-based anomaly detection algorithm called Deep Convolutional Autoencoding Memory network (CAE-M). We first build a Deep Convolutional Autoencoder to characterize spatial dependence of multi-sensor data with a Maximum Mean Discrepancy (MMD) to better distinguish between the noisy, normal, and abnormal data. Then, we construct a Memory Network consisting of linear (Autoregressive Model) and non-linear predictions (Bidirectional LSTM with Attention) to capture temporal dependence from time-series data. Finally, CAE-M jointly optimizes these two subnetworks. We empirically compare the proposed approach with several state-of-the-art anomaly detection methods on HAR and HC datasets. Experimental results demonstrate that our proposed model outperforms these existing methods.
资助项目Key-Area Research and Development Program of Guangdong Province[2019B010109001] ; Science and Technology Service Network Initiative ; Chinese Academy of Sciences[KFJ-STS-QYZD-2021-11-001] ; Natural Science Foundation of China[61972383] ; Natural Science Foundation of China[61902377] ; Natural Science Foundation of China[61902379]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000914161200075
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/19978]  
专题中国科学院计算技术研究所期刊论文
通讯作者Chen, Yiqiang
作者单位1.Microsoft Res Asia, Beijing 100080, Peoples R China
2.Peng Cheng Lab PCL, Shenzhen 518066, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100864, Peoples R China
5.Global Energy Interconnect Dev & Cooperat Org, Beijing 100031, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Yuxin,Chen, Yiqiang,Wang, Jindong,et al. Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(2):2118-2132.
APA Zhang, Yuxin,Chen, Yiqiang,Wang, Jindong,&Pan, Zhiwen.(2023).Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(2),2118-2132.
MLA Zhang, Yuxin,et al."Unsupervised Deep Anomaly Detection for Multi-Sensor Time-Series Signals".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.2(2023):2118-2132.

入库方式： OAI收割

来源：计算技术研究所

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