Robust Fault Detection in Industrial Machines Using Hybrid Transformer-DNN With Visualization via a Humanoid-Based Telepresence Robot - Amir R. Ali; Hossam Kamal (IEEE Access, 2025)
Fault diagnosis in industrial systems remains a critical challenge due to complex operational conditions and the need for high accuracy in real-time applications. Traditional deep learning models often struggle with generalization and adaptability, leading to misclassifications, reduced accuracy, and unreliable fault detection. To overcome these limitations, this study proposes an enhanced hybrid Transformer-deep neural network (DNN) architecture designed for both binary and multi-class fault classification in industrial environments. By leveraging the Transformer’s superior feature extraction capabilities alongside the DNN’s classification strength, the model significantly improves detection accuracy and reduces misclassification errors. The proposed model is evaluated on two distinct datasets, including industrial machine fault detection (TMFD) and induction motor fault diagnosis (MFD). In binary classification tasks, the hybrid Transformer-DNN achieved accuracies of 99.97% for industrial machines and 98.26% for induction motors. For multi-class classification, it attained 99.97% accuracy on industrial machines and 98.39% on induction motors. These results demonstrate the model’s superior performance and robust fault identification across diverse datasets compared to other models. Furthermore, the system’s outputs are integrated into a humanoid-based telepresence platform named ARAtronica, facilitating enhanced remote monitoring and safety during fault inspections. This research offers a scalable, highly accurate framework to advance predictive maintenance in various industrial applications.