A feature engineering framework for online fault diagnosis of freight train air brakes. (September 2021)

• Record Type: Journal Article
• Authors: Wang, Qihang; Gao, Tianci; Tang, Haichuan; Wang, Yifeng; Chen, Zhengxing; Wang, Jianhui; Wang, Ping; He, Qing
• Is Part Of: Measurement. Volume 182(2021)
• Publication Date: 2021-09
• Subjects: Freight train -- Air brake system -- Feature engineering -- Fault diagnosis -- Reinforcement learning; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109672
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This study diagnoses automatic train air brake systems with data collected from a three-car in-lab experiment platform. A divided-and-integrated framework is proposed for car-level and component-level fault location. A feature selection algorithm based on modified reinforcement learning is developed. The outcome of this study will benefit existing automatic air brake system fault diagnosis. Abstract: Automatic air brake systems are widely used in freight train braking to ensure railway operation safety. Various types faults pose an enormous threat to freight operations. Existing algorithms lack a unified framework for generating key features. In this research, we propose a novel feature engineering framework for the fault diagnosis of freight train air brakes. First, experimental data are collected through a three-car in-lab experimental platform. Second, a peak detection method combined with first-order difference function to partition and classify the air pressure time series into the braking phase and releasing phase. Third, a divided-and-integrated framework is designed for feature engineering. Feature selection is carried out via a modified reinforcement learning method. Finally, multiple machine learning algorithms are explored and the results indicate that random forest method shows the best performance. The proposed model achieves about 99% accuracy for car-level fault detection and over 94% accuracy for component-level fault diagnosis.