This paper provides the first comprehensive evaluation and analysis of modern (deep-learning) unsupervised anomaly detection methods for chemical process data. We focus on the Tennessee Eastman process dataset, which has been a standard litmus test to benchmark anomaly detection methods for nearly three decades. Our extensive study will facilitate choosing appropriate anomaly detection methods in industrial applications.

翻译：本文首次对现代（深度学习）无监督异常检测方法在化工过程数据上的表现进行了全面的评估与分析。我们聚焦于田纳西伊士曼过程数据集，该数据集近三十年来一直是用于基准测试异常检测方法的标准试金石。本项广泛研究将有助于在工业应用场景中选择合适的异常检测方法。