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.

翻译：随着汽车测试领域对记录数据的关注日益增长，手动评估已达到其极限，对自动在线异常检测的需求日益迫切。这些真实世界数据在多方面具有复杂性，需要对被测对象行为进行建模。为此，我们提出了一种时序变分自编码器（TeVAE），该模型在未标记数据上训练时能以最小误报率检测异常。我们的方法还避免了旁路现象，并引入了一种将独立时间窗口重新映射到连续时间序列的新方法。此外，我们提出了评估检测延迟与根因分析能力的指标，并展示了在真实工业数据集上的实验结果。经适当配置后，TeVAE 仅以 6% 的错误率标记异常，并能检测出 65% 的现存异常。该方法在较小的训练和验证子集上亦具备良好性能潜力，但需要更复杂的阈值估计方法。