SENSOR-DRIVEN DEEP LEARNING FRAMEWORK FOR REALTIME PREDICTIVE MAINTENANCE AND FAILURE PROGNOSIS IN SMART INDUSTRIAL ROBOTIC SYSTEMS

作者：K. Vijaya Bhaskar Reddy, Ganesh. A, A. Krishna Reddy, M. Venkatesh, G. Karthik · 发表于：Scientific Digest : Journal of Applied Engineering · 年份：2025 · DOI：10.70864/joae.2025.v13.i7(1).pp231-239

In industrial robotics, studies show that over 30% of unplanned downtime is due to equipment failure, with robot-related faults accounting for nearly 20% of total maintenance costs annually. Additionally, predictive maintenance powered by AI can reduce repair costs by up to 25% and increase uptime by 10–20%. These figures underline the growing need for intelligent fault diagnosis systems to improve reliability and efficiency in robotic environments. Traditional manual fault diagnosis techniques are time-consuming, require skilled operators, and often fail to detect early-stage failures in dynamic industrial settings. Moreover, they lack realtime adaptability and consistency, especially in high-speed, sensor-rich robotic operations. These shortcomings result in costly production halts and delayed maintenance responses. To address these limitations, a robust AI-driven fault diagnosis system is proposed that uses sensor-driven datasets including force, torque, voltage, and current parameters from robotic arms. The collected data undergoes a comprehensive preprocessing stage involving outlier removal, normalization, and partitioning into training, validation, and testing sets. Two machine learning models are explored: an existing K-Nearest Neighbors (KNN) classifier and a proposed Dense Neural Network (DNN), which is trained to classify system states into normal or failure conditions. The DNN, designed with multiple hidden layers, effectively learns complex relationships in senso...