A network method for identifying the root cause of high-speed rail faults based on text data

Abstract

Root cause identification is an important task in providing prompt assistance for diagnosis, security monitoring and guidance for specific routine maintenance measures in the field of railway transportation. However, most of the methods addressing rail faults are based on state detection, which involves structured data. Manual cause identification from railway equipment maintenance and management text records is undoubtedly a time-consuming and laborious task. To quickly obtain the root cause text from unstructured data, this paper proposes an approach for root cause factor identification by using a root cause identification-new word sentence (RCI-NWS) keyword extraction method. The experimental results demonstrate that the extraction of railway fault text data can be performed using the keyword extraction method and the highest values are obtained using RCI-NWS.