Vision‐Based Pattern Recognition for Tool Failure Prediction in <scp>IoRT</scp> ‐Connected Industrial Manipulators

ABSTRACT Industrial robotic manipulators are prone to surface wear and damage, leading to unexpected failures and costly downtimes. Early detection of such defects is crucial for enabling predictive maintenance. This study proposes a vision‐based pattern recognition framework that combines convolutional neural networks (CNN) and generative adversarial networks (GANs) to enhance defect detection and tool failure prediction in IoRT‐connected environments. The proposed scheme leverages CNN to extract multi‐scale visual features from raw images of industrial machine components. Convolutional layers are stacked with varying filter sizes to capture fine‐grained surface defects and broader contextual patterns. The pooling layers selectively retain discriminative activations, producing feature embeddings that highlight characteristics such as pitting, scratches, and cracks. This structure allows the network to transform raw pixels into meaningful patterns for reliable classification. To address data scarcity and improve generalisation, the GAN component generates synthetic defect images by simulating real‐world variability, including defect shape, background textures and orientation. The adversarial training between the generator and discriminator enhances the realism and diversity of augmented data, which in turn improves the CNN's robustness. Applied to ball screw drive spindle images, the integrated CNN‐GAN model achieves 96.7% classification accuracy, with 94% precision, 92% recall and 93% AUC. These results support the system's suitability for predictive maintenance and real‐time deployment in smart industrial settings.