NASA C-MAPSS turbofan data

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: C-MAPSS, CMAPSS, Commercial Modular Aero-Propulsion System Simulation

Key findings#

From Nov 2023 to Oct 2026, 51 archived papers mentioned NASA C-MAPSS turbofan data in their title, abstract or keywords (the search of earlier months' full texts is still under way), most of them in Nov 2025 (6).
NASA C-MAPSS turbofan data was mentioned in 1 paper in the last 30 days (2026-10-07 to 2026-11-05), ranking #165 of the 246 datasets mentioned.
By journal category: AI (1).
Most of them appeared in Expert Systems with Applications (1).
From 2017 to 2026 (so far), NASA C-MAPSS turbofan data was mentioned in 70 papers in the tracked journals, most of them in AI (50), operations research (18) and statistics (1).
21 of them were published in 2025, against 0 in 2017.
Since 2023, the institutions with the most of these papers were Beihang University (4), Nanjing University of Aeronautics and Astronautics (3) and Agency for Science, Technology and Research (2).
The journals publishing most of them were Computers & Industrial Engineering (14), Expert Systems with Applications (12) and Artificial Intelligence Review (3).
The most-cited of them is “Autoencoders and their applications in machine learning: a survey” (Artificial Intelligence Review, 2024), with 613 citations.
Papers use NASA C-MAPSS turbofan data most often with CIFAR-10 / CIFAR-100 (3); it has 1 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST.

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
	10	16	25	51

Papers per year#

20172026 (so far)

Papers per publication year and category
Category	2019	2020	2021	2022	2023	2024	2025	2026 (so far)
AI	2	2	5	2	2	9	15	13
Operations Research	0	1	0	2	2	2	6	5
Statistics	0	0	0	1	0	0	0	0
Information Systems	0	0	0	0	1	0	0	0
All categories	2	3	5	5	5	11	21	18

Where the papers come from#

Journals with the most papers, since 2023
#	Journal	Papers
1	Computers & Industrial Engineering	14
2	Expert Systems with Applications	12
3	Artificial Intelligence Review	3
4	Neural Computing and Applications	3
5	Knowledge-Based Systems	3
6	Information Fusion	3
7	IEEE Transactions on Cybernetics	3
8	IEEE Transactions on Artificial Intelligence	3
9	Applied Intelligence	2
10	IEEE Transactions on Neural Networks and Learning Systems	2

Institutions with the most papers, since 2023
#	Institution	Papers
1	Beihang University	4
2	Nanjing University of Aeronautics and Astronautics	3
3	Agency for Science, Technology and Research	2
4	China University of Mining and Technology	2
5	Chongqing University	2
6	Guizhou University	2
7	Huazhong University of Science and Technology	2
8	Liverpool John Moores University	2
9	National University of Singapore	2
10	North University of China	2

Countries with the most papers, since 2023
#	Country	Papers
1	China	32
2	United Kingdom	6
3	Canada	4
4	United States	4
5	France	3
6	Singapore	3
7	Australia	2
8	Spain	2
9	Hong Kong	2
10	Italy	2

Keywords with the most papers, since 2023
#	Keyword	Papers
1	C-MAPSS dataset	35
2	remaining useful life prediction	34
3	predictive maintenance	8
4	prognostics and health management	8
5	prognostics	5
6	N-CMAPSS	4
7	aircraft engines	4
8	semi-supervised learning	4
9	uncertainty quantification	4
10	Bayesian optimization	3

Most-cited papers#

Most-cited papers since 2017
#	Citations	Per month	Title	Journal	Published	Authors
1	613	18.5	Autoencoders and their applications in machine learning: a survey	Artificial Intelligence Review	2024-02-03	Kamal Berahmand, Fatemeh Daneshfar, Elaheh Sadat Salehi et al.
2	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.
3	121	3.3	Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life	Expert Systems with Applications	2023-10-04	Feifan Xiang, Yiming Zhang, Shuyou Zhang et al.
4	76	1.0	A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction	IEEE Transactions on Neural Networks and Learning Systems	2020-03-11	Yuanyuan Gao, Yuxin Wen, Jianguo Wu
5	74	1.0	A Shape-Constrained Neural Data Fusion Network for Health Index Construction and Residual Life Prediction	IEEE Transactions on Neural Networks and Learning Systems	2020-10-07	Zhen Li, Jianguo Wu, Xiaowei Yue

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.

Used together with#

Papers use NASA C-MAPSS turbofan data most often with CIFAR-10 / CIFAR-100 (3); it has 1 partners in the code and data network, in the group around ImageNet, CIFAR-10 / CIFAR-100, MNIST / Fashion-MNIST. The whole network →

What papers use NASA C-MAPSS turbofan data together with (at least 3 papers; lift 1 = chance)
Used with	Type	Papers using both	Lift
CIFAR-10 / CIFAR-100	Dataset	3	1.5

Papers mentioning NASA C-MAPSS turbofan data in the last 30 days, newest first.
Date	Title	Journal	Authors	Affiliations
2026-10-28	Multi-scale adaptive feature fusion network for aeroengine EGT degradation assessment in fleet-level health management systems	Expert Systems with Applications	Qiao Liu, Yiyang Liu, Yu-Kai Fu	Chinese Academy of Sciences

Cite this page

Scholar4AI. (2026). NASA C-MAPSS turbofan data. Journal and Conference Research Rankings. Retrieved November 5, 2026, from https://scholar4ai.com/datasets/nasa-c-mapss-turbofan-data

@misc{scholar4ai-datasets-nasa-c-mapss-turbofan-data,
  author = {{Scholar4AI}},
  title = {NASA C-MAPSS turbofan data},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/nasa-c-mapss-turbofan-data}},
  urldate = {2026-11-05},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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