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

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...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 35; no. 2; pp. 2118 - 2132
Main Authors: Zhang, Yuxin, Chen, Yiqiang, Wang, Jindong, Pan, Zhiwen
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Anomalies
Anomaly detection
attention based BiLSTM
Autoregressive models
Autoregressive processes
CAD
Computational modeling
Computer aided design
Control systems
convolutional autoencoder
Data models
Deep learning
Forecasting
Human activity recognition
Image reconstruction
Industrial electronics
Machine learning
multi-sensor time series
Multivariate analysis
Predictive models
Sensors
Time dependence
Time series
Unsupervised anomaly detection
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary: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.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2021.3102110