An Extremely Lightweight Network with Blueprint Separable Convolution and Pruning for Diagnosing High-speed Train Bogie Faults

The high-speed train bogie has complex structures, various conditions, and high maintenance demands. As a result, an extremely lightweight network (ELN) is proposed in this paper for HST fault diagnosis and may be employed in hand-held terminals. Firstly, a lightweight architecture based on blueprint separable convolution (BSConv) is designed as a feature extractor. Secondly, the mask mechanism is introduced to temporarily deactivate low-weight neurons in order to further diminish network size. Finally, the optimal network is fixed and deployed after learning and evaluation. Experiments prove that ELN has an extremely lightweight structure as well as high failure recognition accuracy.