Basic Evaluation of Anomaly Monitoring System for Numerical Control Machine Tools Using Neural Networks

Abstract

The authors have been developing a design, training and building application with a user-(breakpoint)friendly operation interface for CNN (Convolutional Neural Network), CAE (Convolutional Autoencoder), SVM (Support Vector Machine), YOLO, FCN and so on, which can be used for the defect detection of various kinds of industrial products even without deep skills and knowledges concerning information technology. In those models, images are basically used for training data. In this presentation, intelligent anomaly diagnosis system for numerical control (NC) machine tools is considered, i.e., what structures of neural networks should be applied. Mechanical sound and vibration generated from a machine tool itself or machining sound and vibration generated from a router bit, i.e., end mill cutter is recorded and used for training data. For experimental evaluation, nine kinds of mechanical sounds (.wav) are collected from several machine tools, and then training datasets consisting of sound blocks are prepared. Each sound block is time series data extracted from wave files (.wav). For example, if a wave file is recorded with a sampling rate 44100 [Hz] and an extracted time for forming a sound block is set to 0.005 [s], then the data length of the sound block becomes 44100×0.005≒220. The extracted sound blocks from a wave file are employed for training three type of NN models. As for the NN models for comparison, conventional shallow NN, RNN and 1D CNN are designed and trained. Classification results of test sound blocks by the three models are shown. Furthermore, an autoencoder is designed and considered for anomaly detection by training it using only normal sound blocks of a machine tool.