Condition-aware meta-learning for few-shot fault diagnosis across multiple conditions

This paper proposes a knowledge graph-guided condition-aware meta-learning (KG-CAML) method for few-shot fault diagnosis under sensor data variations induced by fluctuating raw materials and changing operational loads. Within a meta-learning framework, KG-CAML integrates data-driven sensor correlations with expert process knowledge by constructing a KG that captures dependency structures among equipment and measurement variables. A relational graph convolutional network is employed as the encoder to extract consistent feature representations. On this basis, the correlation alignment metric is introduced to quantify the distributional similarity and alignment loss between training tasks and test conditions. Furthermore, to mitigate knowledge forgetting during meta-learning, a task memory updater is designed to dynamically update and retain task representations across stages, thereby continuously accumulating and transferring critical diagnostic knowledge. Experimental results on Tennessee Eastman processes and Wastewater Treatment Plant demonstrate that the diagnostic performance of KG-CAML outperforms state-of-the-art methods under multiple working conditions and few-shot scenarios significantly.