Infrared air turbine dental handpiece rotor fault diagnosis with convolutional neural network

Abstract

AI has been widely used this century. In this study, we demonstrated deep learning in a convolutional neural network (CNN). CNNs are often used for image recognition and image classification. A noninvasive infrared thermal imaging camera was used for the diagnosis of damage in dental handpiece rotors. Areas in a thermal image were considered as specific conditions, which can simplify the detection of complex physical conditions. A CNN was trained to detect thermal images. Six sets of experiments were performed on rotor thermal imaging for 30 s and 1 min at 15, 20, and 25 psi air pressures. The thermal image shooting speed was 5 frame/s. Each thermal image map was subjected to CNN training. An accuracy curve was observed to evaluate the performance of the model, where the closer the accuracy variable is to 1, the more accurate the model is. The experimental results proved that the accuracy of idling at 25 psi was 100%. The proposed system can diagnose the rotor condition automatically.