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

TL;DR

This paper introduces CAE-M, a deep learning model that effectively detects anomalies in multi-sensor time-series data by capturing spatial-temporal dependencies and distinguishing noisy data, outperforming existing methods.

Contribution

The paper proposes a novel deep learning framework combining convolutional autoencoders and memory networks to jointly address spatial-temporal correlation and noise in unsupervised anomaly detection.

Findings

CAE-M outperforms state-of-the-art methods on HAR and HC datasets.

The model effectively distinguishes noisy data from normal and abnormal signals.

Joint optimization of subnetworks improves detection accuracy.

Abstract

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.