Deep Anomaly Detection on Tennessee Eastman Process Data

2023/03/10 by Hartung, Fabian, Franks, Billy Joe, Michels, Tobias +15 · 3 citations
#FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2303.05904

Abstract

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.

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• RBC-AD: conformal anomaly detection with explicit false-alarm control for the Tennessee Eastman Process
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