Article
AI-DRIVEN PREDICTIVE MAINTENANCE FOR ROBOTIC SYSTEMS IN INDUSTRIAL ENVIRONMENTS
In industrial robotics, studies indicate that over 30% of unplanned downtime is caused by equipment failures, with robot-related faults accounting for nearly 20% of total annual maintenance costs. Furthermore, predictive maintenance powered by artificial intelligence (AI) has the potential to reduce repair expenses by up to 25% and improve uptime by 10–20%. These statistics highlight the urgent need for intelligent fault diagnosis systems to enhance reliability and efficiency in robotic operations. Traditional manual diagnostic methods are time-consuming, reliant on skilled personnel, and often incapable of detecting early-stage failures in dynamic industrial environments. They also lack the consistency and adaptability required for real-time, sensor-intensive robotic processes, resulting in costly production delays and slow maintenance responses. To overcome these limitations, this study proposes a robust AI-based fault diagnosis system that utilizes sensor data—including force, torque, voltage, and current—from robotic arms. The dataset undergoes comprehensive preprocessing, including outlier removal, normalization, and division into training, validation, and testing subsets. Two machine learning models are implemented: an existing K-Nearest Neighbors (KNN) classifier and a proposed Deep Neural Network (DNN), trained to classify system conditions as either normal or indicative of failure. The DNN architecture, composed of multiple hidden layers, effectively captures complex patterns in the sensor data, enabling accurate fault classification. Model performance is assessed using key evaluation metrics such as accuracy, precision, recall, and F1-score. Once trained, the system facilitates real-time fault prediction and failure pattern analysis, supporting preventive maintenance and significantly improving the safety, reliability, and productivity of industrial robotic systems
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