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 Con-dition-based Maintenance (CBM) scheme will be beneficial to both the cur-rently in use and next generation train control systems. Due to the complex-ity of the fault mechanism of on-board train control system, a data-driven method is of great necessity to enable the Prognostics and Health Manage-ment (PHM) for the equipments in field operation. In this paper, we propose a big data platform to realize the storage, management and processing of his-torical field data from on-board train control equipments. Specifically, we fo-cus on constructing the Knowledge Graph (KG) of typical faults. The Ex-treme Gradient Boosting (XGBoost) method is adopted to build big-data-enabled training models, which reveal the distribution of the feature im-portance and quantitatively evaluate the fault correlation of all related fea-tures. The presented scheme is demonstrated by a big data platform with in-cremental 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.
Authors: jiang liu, bai-gen cai (beijing jiaotong university), zhong-bin guo (beijing jiaotong university), xiao-lin zhao (beijing jiaotong university),
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