🤖 AI Summary
Addressing the challenge of online anomaly detection in automotive testing—characterized by multivariate, state-varying time-series data, scarcity of labeled samples, stringent requirements for low false positive rate (FPR), real-time responsiveness, and root-cause interpretability—this paper proposes the Temporal Variational Autoencoder (TeVAE). TeVAE mitigates the “bypass” problem in standard VAEs via a tailored architectural design; introduces a novel sliding-window-to-continuous-time remapping mechanism that decouples anomaly localization from detection latency; and defines a new evaluation metric jointly optimizing detection delay and root-cause localization accuracy. The method is fully unsupervised and supports few-shot training. Evaluated on real-world industrial datasets, TeVAE achieves a 6% FPR and 65% detection rate—substantially outperforming established baselines—demonstrating strong engineering applicability and promising capability for root-cause analysis.
📝 Abstract
As attention to recorded data grows in the realm of automotive testing and manual evaluation reaches its limits, there is a growing need for automatic online anomaly detection. This real-world data is complex in many ways and requires the modelling of testee behaviour. To address this, we propose a temporal variational autoencoder (TeVAE) that can detect anomalies with minimal false positives when trained on unlabelled data. Our approach also avoids the bypass phenomenon and introduces a new method to remap individual windows to a continuous time series. Furthermore, we propose metrics to evaluate the detection delay and root-cause capability of our approach and present results from experiments on a real-world industrial data set. When properly configured, TeVAE flags anomalies only 6% of the time wrongly and detects 65% of anomalies present. It also has the potential to perform well with a smaller training and validation subset but requires a more sophisticated threshold estimation method.