Constructing Knowledge Graph for Prognostics and Health Management of On-board Train Control System Based on Big Data and XGBoost

• Published in: Big Data Technologies and Applications Vol. 371; pp. 3 - 16
• Main Authors: Liu, Jiang, Cai, Bai-gen, Guo, Zhong-bin, Zhao, Xiao-lin
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2021; Springer International Publishing
• Series: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
• Subjects: Big data; Extreme gradient boosting; Knowledge graph; Machine learning; Prognostics and health management; Train control system
• ISBN: 3030728013; 9783030728014
• ISSN: 1867-8211; 1867-822X
• DOI: 10.1007/978-3-030-72802-1_1

Train control system plays a significant role in safe and efficient operation of the railway transport system. In order to enhance the system capability and cost efficiency from a full life cycle perspective, the establishment of a Condition-based Maintenance (CBM) scheme will be beneficial to both the currently in use and next generation train control systems. Due to the complexity of the fault mechanism of on-board train control system, a data-driven method is of great necessity to enable the Prognostics and Health Management (PHM) for the equipments in field operation. In this paper, we propose a big data platform to realize the storage, management and processing of historical field data from on-board train control equipments. Specifically, we focus on constructing the Knowledge Graph (KG) of typical faults. The Extreme Gradient Boosting (XGBoost) method is adopted to build big-data-enabled training models, which reveal the distribution of the feature importance and quantitatively evaluate the fault correlation of all related features. The presented scheme is demonstrated by a big data platform with incremental field data sets from railway operation process. Case study results show that this scheme can derive knowledge graph of specific system fault and reveal the relevance of features effectively.