Industrial fault and prognostics benchmarks
Bearing, engine, battery and process data used to compare fault-diagnosis and remaining-life models.
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
| 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 |
| Date | Title | Journal | Authors | Affiliations | Datasets mentioned |
|---|---|---|---|---|---|
| 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) | Tennessee Eastman process |
| 2026-09-30 | Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence | Applied Intelligence | Daniel F. Goncalves, Aldemir Ap Cavalini, Valder Steffen | Universidade Federal de Uberlândia | CWRU bearing data, Tennessee Eastman process |