Professor Daewon Kim and his research team at the Department of Electronic Engineering has developed a smart cable monitoring system that predicts the lifespan of cables used in robotic arms

This system uses energy harvesting technology and artificial intelligence to monitor the stress being applied to machine cables in real time, accurately predicting their lifespan and significantly reducing the risk of cable damage. This innovative research was published online in May 2024 in Advanced Energy Materials (Impact Factor: 27.8) under the title, “Artificial Intelligence Assisted Smart Self-Powered Cable Monitoring System Driven by Time-Varying Electric Field Using Triboelectricity Based Cable Deforming Detection.”

Proper maintenance of robotic arms and automated production lines are essential to the successful operation of smart factories, where repetitive movements can cause cable damage and downtime. Conventional cable monitoring systems cannot provide real-time surveillance in all locations, hampering the prevention of malfunctions or critical accidents. The new system developed by Professor Kim’s team continuously monitors cable conditions and predicts their remaining lifespan, allowing for preventative measures to be taken before issues arise.

Real-time detection and wireless communication without external power input
This new system features a triboelectric nanogenerator used as a sensor for cables (TBSC), an electrostatic field energy harvester (EFEH), a wireless communication module (WCM), and a main server. The TBSC array detects various cable movements through static signals, which are then transmitted to the main server via self-powered wireless communication. This setup enables real-time wireless data transmission without the need for external power inputs.

Combining the EFEH and TBSC increased available electrical energy by 155 percent. The collected data is stored on the main server. To predict the cable lifespan, Professor Kim’s team has enhanced existing algorithms. The deep learning-based algorithm has analyzed cable data to predict remaining lifespan with an accuracy of 93.7 percent. “Collaborating with industry partners enabled us to secure the massive data volume necessary for implementing the algorithm, elevating its accuracy,” he explained.

Professor Kim emphasized the advantages of this research, noting its cost-effectiveness fueled by energy harvesting technology and 3D printing. “Our system operates without batteries or other external power sources, which improves its versatility,” he explained. Looking ahead, he added, “While the current system detects damage in only part of the cable, our goal is to expand its capabilities to monitor the entire cable.”