Effect of image size on performance of a plastic gear crack detection system based convolutional neural networks: an experimental study

Nowadays, deep learning (DL) has become a rapidly growing and provides useful tools for processing and analyzing big machinery data. Many research projects achieved success in failure classification from machinery data using convolutional neural networks (CNNs), one of the most extensive study aspects of DL. On this trend, we constructed a crack detection system of POM (Polyoxymethylene) gears using a deep convolutional neural network (DCNN). In our work, vibration data collected from plastic gears was visualized and labelled as crack data or non-crack images. A DCNN based on pre-trained VGG16, which firstly pre-learned from ImageNet’s data and then re-learned from the labelled images, is utilized to classify crack or non-crack situations of plastic gears. In this case of study, the image quality distortions of the dataset such as blur, noise or contrast are stable and do not affect the performance of the DCNN. However, the image size, which keep a vital role to reach high performance of the detection system, has been unknown. Hence, this paper reveals an optimized size of images created from vibration data for high-accuracy of learning.