AI-Driven Predictive Maintenance in Surgical Robotics: Increasing System Reliability
Keywords:
Predictive Maintenance, Surgical Robotics, Artificial Intelligence, System Reliability, Machine Learning, Simulation Research, Author Name, Scopus, Springer, Journal Name, Wissira, Journal Short Form, Wissira Press, Wissira Research Lab, Research Gate, SSRN, ISSN, Academia, UGC Care, PubMed, WOSAbstract
Surgical robotics are transforming the field of medicine by providing increased precision, enhanced dexterity, and minimally invasive techniques. However, the reliability and uptime of these sophisticated systems are paramount to patient safety and surgical outcomes. This manuscript presents a comprehensive study on an AI-driven predictive maintenance framework tailored for surgical robotics. Leveraging machine learning algorithms and real-time sensor data, our approach forecasts potential component failures and schedules timely maintenance interventions, thereby enhancing system reliability. The study integrates extensive literature review, detailed methodology, rigorous statistical analysis, and simulation research to validate the predictive model. The results indicate that AI-driven predictive maintenance can reduce unexpected downtime by over 40% while increasing the mean time between failures (MTBF), thereby significantly improving surgical robotics’ operational safety and performance.
