SELF-ORGANIZING FEATURE MAPS AND HIDDEN MARKOV-MODELS FOR MACHINE-TOOL MONITORING

Vibrations produced by the use of industrial machine tools can contain valuable information about the state of wear of tool cutting edges, H owever, extracting this information automatically is quite difficult, It has been observed that certain structures present in the, vibration patterns are correlated with dullness, In this paper, we present an a pproach to extracting features present in these structures using self- organizing feature maps (SOFM's). We have modified the SOFM algorithm in order to improve its generalization abilities and to allow it to be tter serve as a preprocessor for a hidden Markov model (HMM) classifie r, We also discuss the challenge of determining which classes exist in the machining application and introduce an algorithm for automatic cl ustering of time-sequence patterns using the HMM. We show the success of this algorithm in finding clusters that are beneficial to the machi ne-monitoring application.