Diagnostic Framework for Smart Manufacturing Systems Driven by Intelligent Embedded IoT

Smart manufacturing is a key pillar of Industry 4.0, but it faces major challenges in real-time fault diagnosis and predictive maintenance due to large-scale data processing, latency constraints, and system complexity. Traditional approaches based on either edge computing or cloud computing alone suffer from limitations in accuracy, scalability, integration, and energy efficiency. They also often fail to adequately address network latency and real-time operational demands. To overcome these issues, this study proposes a Hybrid IoT-Embedded Diagnostic Framework that integrates edge-based anomaly detection using a 1D CNN with cloud-based efficiency classification using an LSTM-Attention model. The main contribution of this framework is the seamless integration of edge and cloud computing to enable real-time fault detection and predictive maintenance. Experimental results show that the proposed hybrid model achieves 98.07% accuracy, 98.09% precision, 98.07% recall, and an F1-score of 98.07%, outperforming cloud-only systems with 93.5% accuracy. Additionally, it reduces latency by 34% and energy consumption by 29%, making it more efficient and suitable for resource-constrained environments. Overall, the proposed approach provides a scalable, energy-efficient, and highly accurate solution for smart manufacturing, supporting the transition toward more intelligent and responsive Industry 4.0 systems