Self-Supervised Time-Series Anomaly Detection Using Learnable Data Augmentation
arxiv(2024)
Abstract
Continuous efforts are being made to advance anomaly detection in various
manufacturing processes to increase the productivity and safety of industrial
sites. Deep learning replaced rule-based methods and recently emerged as a
promising method for anomaly detection in diverse industries. However, in the
real world, the scarcity of abnormal data and difficulties in obtaining labeled
data create limitations in the training of detection models. In this study, we
addressed these shortcomings by proposing a learnable data augmentation-based
time-series anomaly detection (LATAD) technique that is trained in a
self-supervised manner. LATAD extracts discriminative features from time-series
data through contrastive learning. At the same time, learnable data
augmentation produces challenging negative samples to enhance learning
efficiency. We measured anomaly scores of the proposed technique based on
latent feature similarities. As per the results, LATAD exhibited comparable or
improved performance to the state-of-the-art anomaly detection assessments on
several benchmark datasets and provided a gradient-based diagnosis technique to
help identify root causes.
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