Efficient anomaly detection and diagnosis in multivariate time-series data is of great importance for modern industrial applications. However, building a system that is able to quickly and accurately pinpoint anomalous observations is a challenging problem. This is due to the lack of anomaly labels, high data volatility and the demands of ultra-low inference times in modern applications. Despite the recent developments of deep learning approaches for anomaly detection, only a few of them can address all of these challenges. In this paper, we propose TranAD, a deep transformer network based anomaly detection and diagnosis model which uses attention-based sequence encoders to swiftly perform inference with the knowledge of the broader temporal trends in the data. TranAD uses focus score-based self-conditioning to enable robust multi-modal feature extraction and adversarial training to gain stability. Additionally, model-agnostic meta learning (MAML) allows us to train the model using limited data. Extensive empirical studies on six publicly available datasets demonstrate that TranAD can outperform state-of-the-art baseline methods in detection and diagnosis performance with data and time-efficient training. Specifically, TranAD increases F1 scores by up to 17%, reducing training times by up to 99% compared to the baselines.
翻译:多变时间序列数据中异常现象的有效检测和诊断对于现代工业应用非常重要。然而,建立一个能够快速准确地准确定位异常观测的系统是一个具有挑战性的问题。这是因为缺乏异常标签,数据波动性高,现代应用中极低的推断时间的要求也非常低。尽管最近出现了关于异常现象检测的深层次学习方法的发展,但只有其中少数几个能够应对所有这些挑战。在本文件中,我们提议建立一个基于异常现象检测和诊断的深层变异网络,它利用基于关注的序列编码器迅速根据数据中更广泛的时间趋势知识进行推断。TranAD利用基于分数的自调法,使基于分数的自我调控能实现稳健的多模式特征提取和对抗性培训以获得稳定性。此外,模型-认知性元学习(MAML)让我们利用有限的数据对模型进行培训。对六套公开提供的数据集进行广泛的实证研究表明,TRanAD在检测和诊断性能方面可以超越最先进的基线方法,并借助数据和具有时间效率的培训。具体地说,TranAD利用基于分分数的评分法,将基准数提高到17。Tran-AD将基准比基准比F1调提高至17。