Trinity of Safety–Quality–Efficiency: Cloud–Edge–Device Collaborative Monitoring for Manufacturing Systems With Industrial Validation

Against the backdrop of Industry 4.0, complex industrial processes face heightened risks of performance degradation and safety hazards due to increasing scale and integration. To address this, we propose a novel cloud-edge-device collaborative framework for hierarchical performance monitoring and fault diagnosis. Our approach explicitly targets multi-performance coupling and cross-domain coordination in complex systems. Aligned with the architecture of manufacturing systems, a cloud-edge-device collaborative framework is designed deeply integrating models for safety control, quality monitoring, and energy consumption prediction. The architecture strategically distributes tasks: (1) At the device layer, real-time control-loop safety is considered and the safety-related data processing and alignment are realized; (2) The edge layer deploys a dual-attention minimal gated unit network coupled with a quality-driven autoencoder, enabling temporal and nonlinear feature extraction for quality-centric fault diagnosis within sub-processes; (3) The cloud layer integrates self-attention minimal gated units with a broad learning system for multi-perspective energy efficiency prediction and holistic evaluation. Finally, a hot strip mill process prototype system is designed and developed to validate the effectiveness and engineering value of the proposed framework through practical case studies.