A cloud–edge collaborative hierarchical diagnosis framework for key performance indicator-related faults in manufacturing industries

Abstract

In the context of intensifying global market competition and the accelerated advancement of industrial intelligence powered by the Industrial Internet of Things, manufacturing enterprises face pressing challenges in achieving sustainable development through quality and efficiency enhancement. Effective key performance indicators (KPIs) related fault diagnosis plays a crucial role in ensuring product quality stability and efficient production within modern manufacturing industries. However, manufacturing industries are characterized by numerous production sub-processes, hierarchical cooperation and interaction, and complex spatio-temporal features, making the implementation of comprehensive KPI-related fault diagnosis methods challenging. To overcome these challenges and fully leverage the hierarchical and multi-scale nature of manufacturing systems, an innovative hierarchical KPI-related fault diagnosis framework based on cloud–edge collaboration is proposed in this paper. First, a hierarchical information enhancement method utilizing dual-scale slow feature analysis and minimal gated units is developed to handle the multi-scale nature of system levels. Second, graph attention networks are combined with minimal gated units to capture the spatio-temporal dynamics across all levels, and KPI constraints are incorporated to fully extract the KPI-related spatio-temporal features. In addition, bottom-up propagation and top-down updation strategies are designed to facilitate information interaction between levels. Building on this, a cloud–edge collaborative architecture is developed, with specific tasks assigned each side. Finally, the framework is applied to a collaborative prototype system for cloud–edge- device in the hot rolling process, and its effectiveness and applicability are thoroughly evaluated.