Tennessee Eastman process

data category: Industrial fault and prognostics benchmarks · 0 papers in the last 7 days

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Bearing, engine, battery and process data used to compare fault-diagnosis and remaining-life models.

Names searched for: Tennessee Eastman

Key findings#

From Nov 2023 to Oct 2026, 57 archived papers mentioned Tennessee Eastman process in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in May 2025 (4).
Tennessee Eastman process was mentioned in 2 papers in the last 30 days (2026-10-07 to 2026-11-05), ranking #127 of the 246 datasets mentioned.
By journal category: AI (2).
Most of them appeared in Expert Systems with Applications (1) and Pattern Recognition (1).
From 2017 to 2026 (so far), Tennessee Eastman process was mentioned in 124 papers in the tracked journals, most of them in AI (110), information systems (9) and operations research (5).
16 of them were published in 2025, against 6 in 2017.
Since 2023, the institutions with the most of these papers were Central South University (8), Zhejiang University (6) and Beijing University of Chemical Technology (5).
The journals publishing most of them were Expert Systems with Applications (14), IEEE Transactions on Neural Networks and Learning Systems (9) and Knowledge-Based Systems (7).
The most-cited of them is “A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems” (Artificial Intelligence Review, 2021), with 404 citations.

Mentions over 36 months#

Papers mentioning it by publication month, Nov 2023 to Oct 2026 (titles, abstracts and keywords; the full texts of earlier months are still being searched)
Trend	Nov 23–Oct 24	Nov 24–Oct 25	Nov 25–Oct 26	36 months
	18	19	20	57

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	5	4	6	7	12	16	12	14	16	18
Information Systems	0	1	1	0	1	3	1	2	0	0
Operations Research	1	0	0	0	2	0	0	2	0	0
All categories	6	5	7	7	15	19	13	18	16	18

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	Expert Systems with Applications	14
2	IEEE Transactions on Neural Networks and Learning Systems	9
3	Knowledge-Based Systems	7
4	Neural Computing and Applications	6
5	Neural Networks	5
6	IEEE Transactions on Cybernetics	5
7	Information Sciences	4
8	Applied Intelligence	3
9	IEEE Transactions on Pattern Analysis and Machine Intelligence	2
10	Artificial Intelligence Review	2

Institutions with the most papers, since 2023
#	Institution	Papers
1	Central South University	8
2	Zhejiang University	6
3	Beijing University of Chemical Technology	5
4	East China University of Science and Technology	5
5	Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education	4
6	Northeastern University	4
7	China University of Petroleum, Beijing	3
8	Southeast University	3
9	State Key Laboratory of Industrial Control Technology	3
10	State Key Laboratory of Synthetical Automation for Process Industries	3

Countries with the most papers, since 2023
#	Country	Papers
1	China	49
2	United Kingdom	5
3	Hong Kong	4
4	Canada	3
5	Saudi Arabia	3
6	United States	3
7	Germany	2
8	France	2
9	Malaysia	2
10	Pakistan	2

Keywords with the most papers, since 2023
#	Keyword	Papers
1	Tennessee Eastman process	56
2	industrial process monitoring	19
3	fault detection	13
4	fault diagnosis	12
5	process monitoring	8
6	anomaly detection	5
7	feature selection	5
8	few-shot learning	5
9	continuous stirred tank reactor	4
10	industrial fault diagnosis	4

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	404	6.0	A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems	Artificial Intelligence Review	2021-04-02	Ting Huang, Qiang Zhang, Xiaoan Tang et al.
2	170	2.1	A novel intrusion detection system based on an optimal hybrid kernel extreme learning machine	Knowledge-Based Systems	2020-02-13	Lu Lv, Wenhai Wang, Zeyin Zhang et al.
3	139	5.1	Small data challenges for intelligent prognostics and health management: a review	Artificial Intelligence Review	2024-07-23	Chuanjiang Li, Shaobo Li, Yixiong Feng et al.
4	124	1.6	Local–Global Modeling and Distributed Computing Framework for Nonlinear Plant-Wide Process Monitoring With Industrial Big Data	IEEE Transactions on Neural Networks and Learning Systems	2020-04-21	Qingchao Jiang, Shifu Yan, Hui Cheng et al.
5	114	1.7	Fault detection in Tennessee Eastman process with temporal deep learning models	Journal of Industrial Information Integration	2021-03-11	Ildar Lomov, Mark Lyubimov, Ilya Makarov et al.

Archive counts by publication year: a paper counts when its title, abstract or keywords name the dataset, or when OpenAlex's full-text search finds it there (searched for the last 30 days' papers and, by a pass over the past years, for older ones). Names added to the list later are looked for in older papers as the archive is read again.

Papers mentioning Tennessee Eastman process in the last 30 days, newest first.
Date	Title	Journal	Authors	Affiliations
2026-10-17	A hybrid expert-data causal Bayesian network approach for fault diagnosis in complex industrial processes	Expert Systems with Applications	Francesco Giuseppe Ciampi, Rima Oulhaj, Thierno Diallo	Institut Supérieur de Mécanique de Paris
2026-10-11	Graph-Guided Retrieval-Augmented Causal Discovery with LLM reasoning for nonlinear chemical processes	Pattern Recognition	Xiaodong Qin, Junxiao Ren, Qiang Li et al.	Southwest University of Science and Technology; Huafon Group (China)

Cite this page

Scholar4AI. (2026). Tennessee Eastman process. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/tennessee-eastman-process

@misc{scholar4ai-datasets-tennessee-eastman-process,
  author = {{Scholar4AI}},
  title = {Tennessee Eastman process},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/tennessee-eastman-process}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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