A clear need for automatic anomaly detection applied to automotive testing has emerged as more and more attention is paid to the data recorded and manual evaluation by humans reaches its capacity. Such real-world data is massive, diverse, multivariate and temporal in nature, therefore requiring modelling of the testee behaviour. We propose a variational autoencoder with multi-head attention (MA-VAE), which, when trained on unlabelled data, not only provides very few false positives but also manages to detect the majority of the anomalies presented. In addition to that, the approach offers a novel way to avoid the bypass phenomenon, an undesirable behaviour investigated in literature. Lastly, the approach also introduces a new method to remap individual windows to a continuous time series. The results are presented in the context of a real-world industrial data set and several experiments are undertaken to further investigate certain aspects of the proposed model. When configured properly, it is 9% of the time wrong when an anomaly is flagged and discovers 67% of the anomalies present. Also, MA-VAE has the potential to perform well with only a fraction of the training and validation subset, however, to extract it, a more sophisticated threshold estimation method is required.

翻译：随着人们对记录数据的关注度日益提升，且人工评估能力已达极限，汽车测试领域对自动异常检测的需求愈发明确。此类真实数据具有大规模、多样化、多变量及时间序列特性，因此需要对被测对象的行为进行建模。我们提出了一种带多头注意力的变分自编码器（MA-VAE），该模型在无标注数据上训练时，不仅能提供极少的误报，还能成功检测出大多数异常。此外，该方法提供了一种新颖方式以避免文献中探讨的不良行为——"绕过现象"。最后，该方法还引入了一种将独立窗口重新映射为连续时间序列的新技术。基于真实工业数据集展示了实验结果，并开展了多项实验以进一步探究所提模型的特定方面。在合理配置下，当异常被标记时，模型误判概率仅为9%，并能发现67%的异常。同时，MA-VAE仅需训练和验证子集的一小部分即可表现出良好性能，但为实现该潜力，需采用更先进的阈值估计方法。