  This paper describes a machine learning approach to determine the abrasive
belt wear of wide belt sanders used in industrial processes based on acoustic
data, regardless of the sanding process-related parameters, Feed speed, Grit
Size, and Type of material. Our approach utilizes Decision Tree, Random Forest,
k-nearest Neighbors, and Neural network Classifiers to detect the belt wear
from Spectrograms, Mel Spectrograms, MFCC, IMFCC, and LFCC, yielding an
accuracy of up to 86.1% on five levels of belt wear. A 96% accuracy could be
achieved with different Decision Tree Classifiers specialized in different
sanding parameter configurations. The classifiers could also determine with an
accuracy of 97% if the machine is currently sanding or is idle and with an
accuracy of 98.4% and 98.8% detect the sanding parameters Feed speed and Grit
Size. We can show that low-dimensional mappings of high-dimensional features
can be used to visualize belt wear and sanding parameters meaningfully.
