A cross-domain state monitoring method for high-speed train brake pads based on data generation under small sample conditions

Abstract

The proper functioning of the high-speed train brake system is of utmost importance to ensure the train comes to a halt at the intended distance. Among the key components, brake pads are susceptible to abnormal wear over prolonged usage, posing a safety risk to train operations. However, limited real-world data on brake pad damage poses challenges for effective monitoring. This paper proposes an effective transfer learning model that utilizes a small amount of data at different speeds, which can solve the small sample data problem on brake pad damage. Initially, a deep convolutional generative adversarial network (DCGAN) is employed to simulate and expand a limited dataset of vibration signals. The generated signals accurately preserve both the high-frequency and low-frequency characteristics observed in the actual signals. Subsequently, a domain-adversarial neural network (DANN) is utilized to map features from the source and target domains onto a shared feature space, enabling multi-domain feature extraction and adaptation. Finally, an intelligent monitoring network is employed to identify the health status of unlabeled brake pads under fluctuating speeds. Experimental results demonstrate the proposed approach outperforms other commonly used algorithms, providing evidence of the feasibility to monitor the small sample conditions of high-speed train brake pads.