---
title: "Industrial fault and prognostics benchmarks datasets"
description: "Datasets in the category “Industrial fault and prognostics benchmarks” were mentioned in 2 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #54 of the 58 data categories mentioned."
url: https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks
markdown_url: https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "Datasets in the category “Industrial fault and prognostics benchmarks” were mentioned in 2 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #54 of the 58 data categories mentioned."
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-10-26T14:52:47+09:00
window: 2026-09-27 to 2026-10-26
source: OpenAlex (CC0)
---

# Industrial fault and prognostics benchmarks

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

- 0 papers in the last 7 days
- 2 of its 7 datasets mentioned

Back to the ranking: https://scholar4ai.com/rankings/datasets.md

## Key findings

- Datasets in the category “Industrial fault and prognostics benchmarks” were mentioned in 2 papers in the last 30 days (2026-09-27 to 2026-10-26), ranking #54 of the 58 data categories mentioned.
- The most-mentioned were Tennessee Eastman process (2) and CWRU bearing data (1).
- By journal category: AI (2).
- Most of them appeared in Pattern Recognition (1) and Applied Intelligence (1).

## Datasets

*Datasets in this category · 2026-09-27 – 2026-10-26 (30 days)*

| Dataset | Papers | Last 7 days | Names searched for |
|---|---:|---:|---|
| Tennessee Eastman process | 2 | 0 | Tennessee Eastman |
| CWRU bearing data | 1 | 0 | CWRU, Case Western Reserve University bearing |
| Battery ageing data (NASA, CALCE) | 0 | 0 | NASA battery, CALCE |
| NASA C-MAPSS turbofan data | 0 | 0 | C-MAPSS, CMAPSS, Commercial Modular Aero-Propulsion System Simulation |
| Paderborn bearing data | 0 | 0 | Paderborn bearing, Paderborn University bearing |
| PRONOSTIA / PHM 2012 bearings | 0 | 0 | PRONOSTIA, FEMTO-ST bearing, PHM 2012 |
| XJTU-SY bearings | 0 | 0 | XJTU-SY |

## Papers

*Papers mentioning these datasets, newest first.*

| Date | Title | Journal | Authors | Affiliations | Datasets mentioned |
|---|---|---|---|---|---|
| 2026-10-11 | [Graph-Guided Retrieval-Augmented Causal Discovery with LLM reasoning for nonlinear chemical processes](https://doi.org/10.1016/j.patcog.2026.115124) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Xiaodong Qin, Junxiao Ren, Qiang Li et al. | Southwest University of Science and Technology; Huafon Group (China) | Tennessee Eastman process |
| 2026-09-30 | [Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence](https://doi.org/10.1007/s10489-026-07491-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Daniel F. Goncalves, Aldemir Ap Cavalini, Valder Steffen | Universidade Federal de Uberlândia | CWRU bearing data, Tennessee Eastman process |

## Cite this page

Scholar4AI. (2026). Industrial fault and prognostics benchmarks. Journal Research Rankings. Retrieved October 26, 2026, from https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks

```bibtex
@misc{scholar4ai-datasets-industrial-fault-and-prognostics-benchmarks,
  author = {{Scholar4AI}},
  title = {Industrial fault and prognostics benchmarks},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/datasets/industrial-fault-and-prognostics-benchmarks}},
  urldate = {2026-10-26},
  note = {Journal Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).