Published December 29, 2023
| Version v1
Dataset
Open
Dataset for Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework
Authors/Creators
-
1.
Institute of Fluid Flow-Machinery
-
2.
ABB (India)
Description
This is the dataset related to the publication titled:
Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework
Published in the journal Eksploatacja i Niezawodność – Maintenance and Reliability
DoI: 10.17531/ein/178274
The training and test data have been uploaded in sets of 5000 data points each in form of a structure named cases or A and numbered from 111-117
The number and the tool condition are in the table below
|
111 |
Normal insert with no defects |
TN |
|
112 |
Wear at flank face |
TWFC |
|
113 |
Wear at nose radius |
TWNSR |
|
114 |
Notch wear |
TWNT |
|
115 |
Crater wear |
TWCT |
|
116 |
Fracture of cutting edge |
TFCE |
|
117 |
Built-up cutting edge |
TBUE |
Notes
The research was supported by the project "Guided waves based reference-free SHM using fiber Bragg grating sensors (REF-FREE)" (2020/39/D/ST8/00188) granted by National Science Center, Poland.
Files
Files
(13.9 MB)
| Name | Size | Download all |
|---|---|---|
|
md5:72bab6aed6c9aa8e9cb9ed961d98eb7c
|
3.6 MB | Download |
|
md5:7601ebb81802dc319cc643ec465b5f22
|
5.3 MB | Download |
|
md5:c07590c94e3fc0cb95f9e71949ae677a
|
4.9 MB | Download |
Additional details
Related works
- Is supplemented by
- Publication: 10.17531/ein/178274 (DOI)