Dataset for Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous framework

This is the dataset related to the publication titled: Milling cutter fault diagnosis using unsupervised learning on small data: A robust and autonomous frameworkPublished 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 111-117The 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