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Recent transfer learning (TL) approaches in industrial intelligent fault diagnosis (FD) mostly follow the “pre-train and fine-tuning” paradigm to address data drift, which emerges from variable working conditions. However, we find that this approach is prone to the phenomenon known as catastrophic forgetting. Furthermore, performing frequent models fine-tuning on the resource-constrained edge nodes can be computationally expensive and unnecessary, given the excellent transferability demonstrated by existing models. In this work, we propose the Drift-Aware Weight Consolidation (DAWC), a method optimized for edge deployments, mitigating the challenges posed by frequent data drift in the industrial Internet of Things (IIoT). DAWC efficiently manages multiple data drift scenarios, minimizing the need for constant model fine-tuning on edge devices, thereby conserving computational resources. By detecting drift …