Abstract
The rapid development of automation systems and the increased demand for efficient fault detection in manufacturing processes have led to the need for advanced fault detection systems. In this study, we present a novel fault detection system for the Festo MPS 500 system that provides an easier approach of communication. The system implements a camera and Python algorithms for accurate and reliable detection of misaligned parts. An image processing pipeline is developed that includes ROI selection, rectangle detection, fixed point selection, distance calculation, and digital signal communication with PLCs. The system achieved a high level of accuracy in detecting misaligned parts, while establishing a seamless data exchange between PLCs using Python's Snap7 library. Moreover, the system utilizes a human-machine interface to display errors and monitor the production process. The results indicate that the proposed fault detection system offers a reliable and efficient solution to improve communication between PLCs and enhance manufacturing performance, by introducing the usage of a high level programming language.