Table 4 Publications about the application of CNN in fault diagnosis
From: Challenges and Opportunities of AI-Enabled Monitoring, Diagnosis & Prognosis: A Review
Dataset Name | Research Object | Description |
|---|---|---|
C-MAPSS [321] | Simulated Turbofan engine | Engine degradation simulation was carried out using C-MAPSS. Four different sets were simulated under different combinations of operational conditions and fault modes. The dataset was provided by the PCoE at NASA Ames |
Milling Dataset [322] | Actual milling insert | Experiments on a milling machine for different speeds, feeds, and depth of cut. The dataset was provided by the BEST lab at UC Berkeley |
IMS Dataset [68] | Actual bearings | The dataset with bearing run-to-failure experiments was provided by the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati |
FEMTO Dataset [323] | Actual bearings | Experiments on bearing accelerated life tests provided by FEMTO-ST Institute, Besancon, France |
XJTU-SY [324] | Actual bearings | Experiments on bearing accelerated life tests containing complete run-to-failure data of 15 rolling element bearings. The dataset was provided by the Xi’an Jiaotong University and the Changxing Sumyoung Technology Company |