Analysis on RAMS Information for Metro Vehicles Using Natural Language Processing Algorithm: Evidence From China
Min Luo, Luman Yu, Yimiao Yao · 2020
Abstract The RAMS information of rail vehicles is an important data for the operation and maintenance of rail transit, and is the key to improving the performance and reliability level of rail vehicle equipment. At present, there are a large number of colloquial, hybridized and subjective data records in the RAMS information of metro trains; especially in the vehicle fault record, this phenomenon is widespread, which brings great difficulties to subsequent data analysis. Therefore, how to convert these irregular fault records into computer-readable fault texts is of great significance. This paper proposes the RAMS information structuring algorithm based on the Jieba word segmentation and Doc2Vec technology. Besides, we compile a professional dictionary of metro vehicles and a standard fault statement library for metro vehicles.