Fault Detection and Identification Based on Image Processing and Deep Learning

Abstract

Fault detection approaches based on computer vision have been utilized in a variety of applications, including maintaining high quality control in industrial manufacturing processes and detecting defects in inaccessible or isolated systems. However, various systems have been utilized for each application. In this research, we present a fault detection approach based on computer vision and deep learning that can be used for a variety of applications. We tested our model on three distinct datasets of different engineering problems. A MobileNetV2 based convolutional neural network model was applied on different engineering problems related photovoltaic solar, manufacture of magnetic tiles and electrical commuter systems. The results obtained for these three cases are: 85 percent, 92 percent, and 92 percent, respectively.