Sound Recognition based on Convolutional Neural Network for Real-Time Cutting State Monitoring of Tube Cutting Machine

Abstract

Machine sound monitoring is widely used in various applications of operational state and diagnostic monitoring as machine-emitted sound contains the operational and process information. In the metal cutting industry, it is not surprising that operators are easily able to recognize whether cutting is engaging by listening to the operational sounds based on their experiences even if the cutting parameters are changed. Inspired by the ability of recognizing human sound, we propose a real-time sound monitoring for cutting state based on the convolutional neural network (CNN). Three sound sensors were deployed to a tube cutting machine on different places to capture and analyze sound signals in real-time. To enable sound stream automatically, the MTConnect framework was employed. Four different sizes: 0.25, 0.5, 0.75, and 1-second, of the time frames, were selected and the effects of the time frames, as well as the sensors, were compared in terms of performances of predicting the cutting time and the productivity. The Log-Mel spectrum was adopted as a feature for convolutional neural network (CNN) model. In the CNN model training, a random search method was used to determine the hyperparameters of the CNN models. To verify the trained CNN models, the evaluation dataset which consisted of unknown and untrained part production was used. The accuracies of predicted cutting state on the evaluation dataset for all sensors and all sizes of the time frames ranged from 83.3 to 98.6%. The accuracies of the predicted productivity were also analyzed according to the sensors and the size of time frames. Furthermore, real-time monitoring response was evaluated to figure out the effects of the size of time frames on the prediction speed and the time delay. The sizes of the time frames play an important role in various aspects. As a result, it is suggested that the shorter size of the time frames is the better choice if the prediction accuracy of cutting time and the monitoring response time is critical whereas the longer size is the better if the prediction accuracy of productivity is more important. Among the internal sound sensors in various locations, one on the machine base shows the best performances in all sizes of the time frames with respect to not only the prediction of the cutting time but also the performance of predicting the productivity.