Feature extraction networks for dull tool monitoring

Automatic feature extraction is a need in many current applications, including the monitoring of industrial tools. Currently available approaches suffer from a number of shortcomings. The Kohonen (1989) self-organizing neural network (SONN) has the potential to act as a feature extractor, but we find it benefits from several modifications. The purpose of these modifications is to cause feature variations to be aligned with the SONN indices so that the indices themselves can be used as measures of the features. The modified SONN is applied to the dull tool monitoring problem, and it is shown that the new algorithm extracts and characterizes useful features of the data.