Distillation-enhanced fast neural architecture search method for edge-side fault diagnosis of wind turbine gearboxes

Abstract

Deep learning methods have been widely applied for fault diagnosis of wind turbine gearboxes. However, a new model requires experts to be empirically handcrafted, which is time consuming and labor-intensive. In addition, excessive attention is paid to diagnostic accuracy, and manual models often have high complexity, making their deployment in edge devices difficult. Accordingly, a novel method based on a distillation-enhanced fast neural architecture search is proposed for edge-side fault diagnosis of wind turbine gearboxes. First, a multibranch parallel fast neural architecture search framework is designed to build diagnosis models quickly and automatically. Meanwhile, an automatic distillation technology is proposed to empower the fast neural architecture search framework so that the searched model can achieve a balance between lightweight and high diagnostic accuracy to meet the lightweight deployment requirements for edge devices. The feasibility and effectiveness of the proposed method were verified using a gearbox dataset from a drivetrain diagnostics simulator (DDS) and measured data from a wind farm.