Rolling bearing fault detection has developed rapidly in the field of fault
diagnosis technology, and it occupies a very important position in this field.
Deep learning-based bearing fault diagnosis models have achieved significant
success. At the same time, with the continuous improvement of new signal
processing technologies such as Fourier transform, wavelet transform and
empirical mode decomposition, the fault diagnosis technology of rolling
bearings has also been greatly developed, and it can be said that it has
entered a new research stage. However, most of the existing methods are limited
to varying degrees in the industrial field. The main ones are fast feature
extraction and computational complexity. The key to this paper is to propose a
lightweight bearing fault diagnosis model DKDL-Net to solve these challenges.
The model is trained on the CWRU data set by decoupling knowledge distillation
and low rank adaptive fine tuning. Specifically, we built and trained a teacher
model based on a 6-layer neural network with 69,626 trainable parameters, and
on this basis, using decoupling knowledge distillation (DKD) and Low-Rank
adaptive (LoRA) fine-tuning, we trained the student sag model DKDL-Net, which
has only 6838 parameters. Experiments show that DKDL-Net achieves 99.48\%
accuracy in computational complexity on the test set while maintaining model
performance, which is 0.58\% higher than the state-of-the-art (SOTA) model, and
our model has lower parameters. Our code is available at Github link:
this https URL

提出了一种轻量级的滚动轴承故障诊断模型 DKDL-Net，通过解耦知识蒸馏和低秩自适应微调，在 CWRU 数据集上进行训练，实验证明在保持模型性能的同时，DKDL-Net 在计算复杂度上实现了 99.48% 的准确率，比现有技术模型高出 0.58%，且参数更少。