Differentiable Architecture Search for Aeroengine Bevel Gear Fault Diagnosis

The deep learning models have swept the field of intelligent fault diagnosis, especially the deep convolutional neural network (DCNN) with ability to automatically extract features from large-scale data. On one hand, as most of the existing DCNN models are specifically designed for image task, direct application of these models to machine fault diagnosis will cause domain bias. On the other hand, it is time-consuming and often dependent on expert knowledge to design a DCNN model for a given fault diagnosis task from a specific dataset. In this paper, a differentiable architecture search method through gradient optimization is proposed to design a new network model for aeroengine bevel gear fault diagnosis. This method can design efficient network from a complex search space for a specific dataset via constructing the hyper-network and gradient-based search strategy. The proxy model and optimization approximation are used to speed up the searching process. Compared with the traditional discrete neural architecture search methods, this method has great superiority in computational cost. The aeroengine bevel gear dataset is used to verify the performance of this method, and only 3 GPU hours are required for this dataset to search the optimized network. The proposed method achieves the state-of-the-art performance under different signal-to-noise ratios via comparisons with other mainstream manually designed networks.