An In-Depth Tutorial on BJTU-RAO Bogie Datasets for Fault Diagnosis

作     者：Qin, Yong Wang, Yiran Li, Zhaojun Steven Wang, Biao Ding, Ao Wang, Chengcheng Qin, Yuanjing Wang, Yang 

作者机构：Beijing Jiaotong Univ State Key Lab Adv Rail Autonomous Operat Beijing 100044 Peoples R China Beijing Jiaotong Univ Key Lab Railway Ind Proact Safety & Risk Control Beijing 100044 Peoples R China Western New England Univ Dept Ind Engn & Engn Management Springfield MA 01119 USA Instrumentat Technol & Econ Inst Beijing 100055 Peoples R China NYU Tandon Sch Engn New York NY 11201 USA 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：60879-60888页

核心收录：

基　　金：National Key Research and Development Program of China [2022YFB4300604] State Key Laboratory of Advanced Rail Autonomous Operation [RAO2023ZZ003] 

主　　题：Motors Vibrations Public transportation Gears Fault diagnosis Employee welfare Sensors Safety Rails Prognostics and health management Subway train bogie fault diagnosis multi-sensor data publicly available datasets train transmission systems experimental platform 

摘      要：The reliability and safety of trains have always been the top priority in the railway industry. As the critical subsystem of trains, the health states of bogie transmission systems directly affect the operation safety of trains. Train fault diagnosis, predictive maintenance, and algorithms evaluation become crucial to ensure operational safety. Thus, there is an urgent need for high-quality fault datasets of train transmission systems. This tutorial introduces the first publicly available fault datasets of train transmission systems, referred to as Beijing Jiaotong University - Rail Autonomous Operations (BJTU-RAO) bogie datasets. The datasets contain multi-sensor data streams of transmission systems of subway train bogies including a health state and 50 faulty states, which are acquired by fault simulation experiments and are released at the 2024 Global Reliability and Prognostics and Health Management (PHM) Conference. For each type of state, samples under nine different working conditions are collected to represent the different operating conditions of trains, and the signals are recorded at a sampling frequency of 64 kHz. The datasets are collected, organized, and made publicly available by the research team from the State Key Lab of Advanced Rail Autonomous Operation at BJTU, aiming to encourage scholars and engineers to investigate and validate their fault detection or diagnosis algorithms for key components of train transmission systems, such as driving gearboxes, axle boxes, and traction motors.