A Bayesian Network Approach for Condition Monitoring of High-Speed Railway Catenaries

Abstract

The growing variety of data from condition monitoring of high-speed railways offer unprecedented opportunities to improve railway infrastructure maintenance. For condition monitoring of railway catenaries, this paper proposes a data-driven approach that uses a Bayesian network (BN) to integrate the inspection data from catenaries into a key performance indicator (KPI). The BN topology is structured based on the physical relationships among data types, including train speed, dynamic stagger and height of the contact wire, pantograph head acceleration, and pantograph-catenary contact force. The tailored performance indicators are individually defined and extracted from the five types of data as the BN input. As the output of the BN, the KPI is defined as the overall condition level of the catenary considering all defects that can be reflected by the data types. Finally, using historical inspection data and maintenance records from a section of the Beijing-Guangzhou high-speed line in China, the BN parameters are estimated to establish a probabilistic relationship between the input and output. An approach that applies the estimated BN to catenary condition monitoring is proposed. Testing of the BN-based approach using new inspection data shows that the output KPI can adequately represent the catenary condition, leading to a nearly 66.2% reduction in the false alarm rate of defect detection compared with current practice. It is also tested that when the input data quality is not ideal, the approach can still work acceptably on noisy data with a signal-to-noise ratio greater than 3 dB or with one type of data missing.