A Lightweight Sound Diagnosis Model for Transformer Discharge Fault Based on Knowledge Distillation with Supercomputing

Transformer discharge is a serious power system fault, which can lead to catastrophic accidents. Intelligent analysis of discharge faults by collecting transformer sound provides a non-invasive and sustainable health monitoring method. This paper proposes a lightweight voice fault diagnosis model based on knowledge distillation. The method utilizes the computing power of the supercomputing platform to train multiple teacher models in parallel, and then distill the knowledge of the teacher model into a lightweight student model, which not only achieves good classification accuracy but also can be easily deployed to edge computing platform. The experimental results show that the accuracy of our method is 95.65%, with only 1.28M parameters and only 0.35GFlops calculation amount, which proves the superiority of the method.