Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance

• Main Authors: Angelopoulos, J. (Author), Mourtzis, D. (Author), Tsoubou, S. (Author)
• Format: Article
• Language: English
• Published: MDPI 2023
• Subjects: digital twin; Industry 4.0; machine learning; predictive maintenance; reliability optimization; remaining useful life; robotic cell
• Online Access: View Fulltext in Publisher; View in Scopus

 Robotic systems have become a standard tool in modern manufacturing due to their unique characteristics, such as repeatability, precision, and speed, among others. One of the main challenges of robotic manipulators is the low degree of reliability. Low reliability increases the probability of disruption in manufacturing processes, minimizing in this way the productivity and by extension the profit of the company. To address the abovementioned challenges, this research work proposes a robotic cell reliability optimization method based on digital twin and predictive maintenance. Concretely, the simulation of the robot is provided, and emphasis is given to the reliability optimization of the robotic cell’s critical component. A supervised machine learning model is trained, aiming to detect and classify the faulty behavior of the critical component. Furthermore, a framework is proposed for the remaining useful life prediction with the aim to improve the reliability of the robotic cell. Thus, following the results of the current research work, appropriate maintenance tasks can be applied, preventing the robotic cell from serious failures and ensuring high reliability. © 2023 by the authors.