mlsanityData to 14 Oct 2026

Deep Anomaly Detection on Tennessee Eastman Process Data

arXiv 2303.05904 ↗First posted 10 Mar 2023cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 52 references that are in the map. First posted on arXiv 10 Mar 2023.

Builds on

22 of 52 references.

References · 22

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  2. 2018Deep Anomaly Detection with Outlier Exposurecited by 942
  3. 2020A Unifying Review of Deep and Shallow Anomaly Detectioncited by 425
  4. 2017Future Frame Prediction for Anomaly Detection -- A New Baselinecited by 391
  5. 2021Graph Neural Network-Based Anomaly Detection in Multivariate Time Seriescited by 301
  6. 2016Learning Temporal Regularity in Video Sequencescited by 254
  7. 2018Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applicationscited by 224
  8. 2016LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detectioncited by 215
  9. 2017A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencodercited by 209
  10. 2018Adversarially Learned One-Class Classifier for Novelty Detectioncited by 202
  11. 2018A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Datacited by 182
  12. 2019MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networkscited by 176
10 more
  1. 2020Multivariate Time-series Anomaly Detection via Graph Attention Networkcited by 167
  2. 2020DROCC: Deep Robust One-Class Classificationcited by 121
  3. 2021Neural Transformation Learning for Deep Anomaly Detection Beyond Imagescited by 99
  4. 2021Towards a Rigorous Evaluation of Time-series Anomaly Detectioncited by 97
  5. 2020TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networkscited by 55
  6. 2021Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encodercited by 39
  7. 2022Exposing Outlier Exposure: What Can Be Learned From Few, One, and Zero Outlier Imagescited by 35
  8. 2016Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Modelcited by 34
  9. 2016Variational Inference for On-line Anomaly Detection in High-Dimensional Time Seriescited by 12
  10. 2022Is it worth it? Comparing six deep and classical methods for unsupervised anomaly detection in time seriescited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 3; 2026: 1.

5 papers.

  1. 2025Federated Learning from Molecules to Processes: A Perspectivecited by 3
  2. 2024Reimagining Anomalies: What If Anomalies Were Normal?cited by 2
  3. 2025Batch Distillation Data for Developing Machine Learning Anomaly Detection Methodscited by 1
  4. 2025Quantum enhanced ensemble GANs for anomaly detection in continuous biomanufacturingcited by 0
  5. 2026Automated Batch Distillation Process Simulation for a Large Hybrid Dataset for Deep Anomaly Detectioncited by 0