Operational Modal Analysis of Y25 Bogie via Stochastic Subspace Identification for the Condition Monitoring of Primary Suspension Systems

Railway vehicle suspension systems are vital to the vehicle safety and ride comfort, which is further driven by high speed operations. Condition Monitoring (CM) based online measurement is an efficient and achievable method to ensure the suspension systems working under normal function. In this paper, a potential method, which can achieve online CM of railway vehicle primary suspension, denoted as Average Correlation Signals based Stochastic Subspace Identification (ACS-SSI) was explored through simulation and experimental studies. Particularly, the dynamic performance of an Y25 bogie were investigated under the operational condition and the main focus was on the modes related to the suspension system. Firstly, ACS-SSI was presented briefly. Then, the employed test rig, an advanced dynamic test cell in the Institute of Railway Research (IRR) at University of Huddersfield, was introduced and the theoretical modal parameters of the tested bogie associating with the primary suspension system were calculated based on a multi rigid body model in the SIMPACK. The theoretical natural frequencies of bounce, roll and pitch modes are 11.07 Hz, 13.93 Hz and 15.19 Hz, respectively. Finally, ACS-SSI was adopted to identify modal parameters of the bogie using the collected responses on the four corners of the bogie frame. The pitch mode was identified successfully, which can illustrate the condition of the suspension system. Therefore, it can draw the conclusion that ACS-SSI has the potential to achieve suspension online monitoring.