Differentiable Architecture Searched Network with Tree-Structured Parzen Estimators for Rotating Machinery Fault Diagnosis

Abstract

Deep learning is widely used in the field of rotating machinery fault diagnosis. However, manually designing the neural network structure and adjusting the hyperparameters for specific fault diagnosis task are complex and requires a lot of expert knowledge. Aiming at these problems, Differentiable Architecture Searched Network with Tree-Structured Parzen Estimators (DASNT) is proposed for fault diagnosis. Differentiable Architecture Search (DARTS) is utilized to automatically search network structure for specific fault diagnosis task. Tree-Structured Parzen Estimators (TPE) is utilized to optimize the hyperparameters of the network searched by DARTS, which can further improve the fault diagnosis accuracy. The results of comparison experiments indicate that the network architecture and hyperparameters optimized by DASNT can achieve superior fault diagnosis performance.