Deep Anomaly Detection on Tennessee Eastman Process Data

This paper provides the first comprehensive evaluation and analysis of modern (deep-learning-based) unsupervised anomaly detection methods for chemical process data. We focus on Tennessee Eastman dataset, a standard litmus test to benchmark nearly three decades. Our extensive study will facilitate choosing appropriate in industrial applications. From benchmark, we conclude that reconstruction-based are choice, followed by generative forecasting-based methods.