Multidimensional Feature Fusion and Ensemble Learning-Based Fault Diagnosis for the Braking System of Heavy-Haul Train

Abstract

Electronically controlled pneumatic (ECP) brake is widely used in heavy-haul train. Although the latest data-driven fault diagnosis can exploit the collection data from the braking system, it still has challenges for effective fault diagnosis model because of industrial data noise and insufficient fault samples. This article proposes a fault diagnosis model based on multidimensional feature fusion and ensemble learning for braking system of heavy-haul train (MFF-GBFD). First, the multidimensional features are extracted. By principal component analysis and feature fusion, the redundant features are eliminated. Then, the model is trained under ensemble learning framework with boosting strategy. Experiments are carried out on the data from the ECP braking system of DK-2 locomotive. The efforts show that the proposed MFF-GBFD model presents better performances as a result from the early-stage feature extraction, feature selection, and feature fusion. It also has higher accuracy and $F_1$ values compared with the traditional classification algorithms.