AI-Based Fault Detection in Industrial Robots Using Deep Learning Models

The increasing deployment of industrial robots in manufacturing, logistics, and assembly lines has necessitated advanced fault detection systems to ensure operational reliability and efficiency. Conventional diagnostic methods often fail to identify faults in real time, leading to production losses and safety risks. Artificial Intelligence (AI), particularly deep learning models, has emerged as a transformative tool for automated fault detection and predictive maintenance. This paper presents a comprehensive analysis of AI-based fault detection methods in industrial robots, focusing on the implementation of deep learning architectures such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Autoencoders. The study reviews recent advancements, datasets, challenges, and real-time deployment strategies. The results demonstrate that AI-driven systems significantly outperform traditional methods in accuracy, adaptability, and fault prediction capabilities.