A cloud–edge collaboration based quality-related hierarchical fault detection framework for large-scale manufacturing processes

Against the backdrop of the new-generation intelligent manufacturing and Industrial Internet of Things, manufacturing processes are evolving towards integration, large-scale operations, and complexity, and the requirements for process safety and product quality reliability are constantly improving. However, these processes are generally distinguished by multi-production sub-processes with cooperative coupling, and multi-hierarchical levels integrated automation systems, where any deviation or abnormality occurring within a sub-process or system level will lead to the extensive propagation and evolution of faults, making the comprehensive quality-related fault detection face increasing challenges in practice. In response, a novel cloud–edge collaboration-based quality-related hierarchical fault detection framework is constructed in this paper, which can facilitate interconnected communication between sub-processes and enhance collaborative interactions at different hierarchical levels to improve fault detection accuracy and reliability. First, two quality supervised training mechanisms based on minimal gated unit are introduced for capturing different scale dynamics features on the cloud side and edge side. These methods enable more adequate extraction of quality-related deep hidden features, particularly in the presence of nonlinearity and dynamics. Subsequently, a federated bi-directional knowledge distillation-based strategy is proposed, leveraging the concept of federated learning. Quality knowledge such as prediction and quality-related features from both the cloud side and edge side are integrated into the training steps of federated learning, thereby strengthening the interaction between multiple sub-processes and distinct levels. Furthermore, comprehensive quality-related fault detection strategies are formulated based on the quality-related features of the cloud side and each sub-process on the edge side, respectively. Finally, the proposed framework is deployed into a cloud–edge-device collaborative prototype system based on a real hot strip mill process to demonstrate its effectiveness and applicability.