Deep Anomaly Detection on Tennessee Eastman Process Data

Abstract: 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 the Tennessee Eastman process dataset, 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. From the benchmark, we conclude that reconstruction‐based methods are the methods of choice, followed by generative and forecasting‐based methods.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Deep Anomaly Detection on Tennessee Eastman Process Data ; day:13 ; month:04 ; year:2023 ; extent:7 ; Chemie - Ingenieur - Technik ; (13.04.2023) (gesamt 7)

Contributors, Places and Time

• Creator: Hartung, Fabian ; Franks, Billy Joe ; Michels, Tobias ; Wagner, Dennis ; Liznerski, Philipp ; Reithermann, Steffen ; Fellenz, Sophie ; Jirasek, Fabian ; Rudolph, Maja ; Neider, Daniel ; Leitte, Heike ; Song, Chen ; Kloepper, Benjamin ; Mandt, Stephan ; Bortz, Michael ; Burger, Jakob ; Hasse, Hans ; Kloft, Marius

Further information

• DOI: 10.1002/cite.202200238
• URN: urn:nbn:de:101:1-2023041415010655621951
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:57 AM CEST