EdgeFD: An Edge-Friendly Drift-Aware Fault Diagnosis System for
  Industrial IoT
Chen Jiao Mao Fengjian Lv Zuohong Tang Jianhua
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 using classifier confidence and estimating
parameter importance with the Fisher Information Matrix, a tool that measures
parameter sensitivity in probabilistic models, we introduce a drift detection
module and a continual learning module to gradually equip the FD model with
powerful generalization capabilities. Experimental results demonstrate that our
proposed DAWC achieves superior performance compared to existing techniques
while also ensuring compatibility with edge computing constraints.
Additionally, we have developed a comprehensive diagnosis and visualization
platform.