Intelligent deep learning models for fault diagnosis in sixth generation industrial internet of things environments

Hareesha Dandamudi (Prasad V Potluri Siddhartha Institute of Technology)
Chenchu Punnarao Bandi (Synopsys Inc.)
Simhadri Mallikarjuna Rao (Vignan’s Foundation for Science)
Palacharla SVS Sridhar (Koneru Lakshmaiah Education Foundation)
Mythili Murugan (M. Kumarasamy College of Engineering)
Srikanth Kilaru (Vignan’s Nirula Institute of Technology and Science for Women)
Rama Krishna Paladugu (RVR and JC College of Engineering)

International Journal of Reconfigurable and Embedded Systems (IJRES)
Vol 15, No 2: July 2026

The integration of sixth-generation (6G) communication and Industry 4.0 technologies has transformed industrial automation, connectivity, and intelligent data analysis. However, the increasing volume and diversity of data generated from multiple industrial sources create significant challenges for accurate and real-time fault detection. This study presents a deep learning-based framework designed to improve fault identification in 6G-enabled Industry 4.0 environments. The proposed system processes heterogeneous data collected from internet of things (IoT) devices, monitoring sensors, and automated industrial equipment to ensure reliable and scalable fault analysis. A hybrid model combining convolutional neural networks (CNNs) and long short-term memory (LSTM) networks is implemented to capture spatial features and temporal relationships within industrial datasets. The framework also focuses on optimizing computational resources while maintaining high detection performance. Simulation-based evaluations demonstrate that the proposed approach enhances fault detection accuracy and system reliability, making it suitable for advanced smart manufacturing and industrial monitoring applications.