Deep Anomaly Detection on Tennessee Eastman Process Data

2023/03/10 by Fabian Hartung, Hartung, Fabian, Billy Joe Franks +33 · 4 citations
Computer Science · Engineering · #Advanced Data Processing Techniques #Anomaly Detection Techniques and Applications #FOS: Computer and information sciences #Fault Detection and Control Systems #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2303.05904

openalex publication_date 2023/03/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Cited by

• RBC-AD: conformal anomaly detection with explicit false-alarm control for the Tennessee Eastman Process
• Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
• Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturing
• Federated Learning from Molecules to Processes: A Perspective

Related

• Deep Anomaly Detection on Tennessee Eastman Process Data
• Three-layer Approach to Detect Anomalies in Industrial Environments\n based on Machine Learning
• Modern Anomaly Detection Methods in Industry: A Comparative Analysis of Machine Learning Algorithms and Their Application to Improve the Efficiency of Manufacturing Processes
• Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
• Deep Learning based pipeline for anomaly detection and quality enhancement in industrial binder jetting processes