Abstract
The authors have been developing a design, training and building application with a user-friendly operation interface for CNN (Convolutional Neural Network), CAE (Convolutional Autoencoder), SVM (Support Vector Machine), YOLO (You Look Only Once), FCN (Fully Convolutional Network) 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. Intelligent anomaly diagnosis system for numerical control (NC) machine tools has been 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. Extracted sound block data (SB data) from wave files are employed for training NN models. It has been already confirmed from preliminary experiments that a 1D CNN and an autoencoder are effective for a classification task and an identification one, respectively. In this paper, a SB data-based fully convolutional data description (FCDD) model is proposed for anomaly detection of removal machining by NC machine tools and its concurrent visualization, in which time series data such as SB data can be directly applied to training and testing. The effectiveness of the proposed method is shown.
