AI-Enhanced Remote Diagnosis in Robotic Maintenance Systems

Abstract

The ever‑increasing sophistication of robotic maintenance systems in manufacturing and critical‑infrastructure domains has outpaced the capabilities of traditional diagnostic regimes. Conventional approaches—predicated on scheduled inspections, manual symptom recognition, and simple threshold alarms—prove insufficient when confronted with heterogeneous fleets of high‑degree‑of‑freedom manipulators, mobile platforms, and collaborative robots operating under continuous, high‑load conditions. Such systems demand a diagnostic paradigm that not only anticipates failures before catastrophic breakdowns but also adapts dynamically to evolving operating contexts. This manuscript presents an AI‑enhanced remote diagnosis framework tailored for modern robotic maintenance, integrating multi‑modal sensor fusion, edge‑based preprocessing, and cloud‑native machine learning analytics. We detail the system architecture—comprising onboard accelerometers, thermistors, and motor‑current monitors; an MQTT‑based communication layer; and a scalable Spark‑Streaming/XGBoost analytics pipeline—and describe the data‑generation and labeling processes used for model training. A comprehensive simulation in ROS‑Gazebo involving six UR10 arms executing representative pick‑and‑place tasks evaluates the framework’s performance. Statistical analysis on an unseen test set reveals a 90.4% diagnostic accuracy—15.2% higher than a baseline threshold method—alongside a 33.6% reduction in mean time to detection. Through a 72‑hour virtual operation, end‑to‑end latencies averaged 120 ms, demonstrating real‑time feasibility. False‑alarm rates fell from 8.7% to 2.3%, and false negatives from 5.5% to 3.1%, confirming robust sensitivity and specificity. The proposed system therefore offers a scalable, adaptive solution for minimizing unscheduled downtime and maintenance costs in Industry 4.0 deployments. Future work will incorporate motion‑context awareness and real‑world field trials to further refine diagnostic precision and operational resilience.