Joint Attention-Guided Multitask Feature Sharing Network for High-Speed Train Fault Diagnosis

Abstract

Intelligent fault diagnosis of traction systems is vital for the reliability and safety of high-speed trains. Conventional methods extract features solely from fault signals to determine fault categories, neglecting the impact of operating conditions on traction systems. To address this limitation, multitask learning methods have been explored to simultaneously distinguish fault categories and operating conditions. However, due to the high cost of collecting high-speed train fault data, the available data are often extremely limited. Considering the parameter-intensive nature of multitask learning models and the scarcity of fault data, these models are prone to potential overfitting risks during the training process. In this work, we propose a novel joint attention-guided multitask feature sharing network (JA-MFSN) tailored for high-speed train traction system fault diagnosis. Our JA-MFSN integrates a novel joint attention module (JAM) that captures both task-shared and task-specific features with reduced parameter overhead, effectively mitigating overfitting risks. The network architecture balances model complexity and performance, enabling robust multitask learning under data-scarce conditions. Experimental results conducted on the hardware-in-the-loop (HIL) high-speed train traction control system simulation platform clearly demonstrate the superiority of the JA-MFSN approach over several existing methods.