Dr Nazimunnisa, C. Karthik, K. Srija Reddy, Kakkula. Arya Kumar Sagar, Sharath Chandra 02 08 In industrial robotics studies indicate that over 30 of unplanned downtime is caused by equipment failures with robotrelated 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 1020 These statistics highlight the urgent need for intelligent fault diagnosis systems to enhance reliability and efficiency in robotic operations Traditional manual diagnostic methods are timeconsuming reliant on skilled personnel and often incapable of detecting earlystage failures in dynamic industrial environments They also lack the consistency and adaptability required for realtime sensorintensive robotic processes resulting in costly production delays and slow maintenance responses To overcome these limitations this study proposes a robust AIbased fault diagnosis system that utilizes sensor dataincluding force torque voltage and currentfrom 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 KNearest 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 F1score Once trained the system facilitates realtime fault prediction and failure pattern analysis supporting preventive maintenance and significantly improving the safety reliability and productivity of industrial robotic systems Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026