Vehicle running instability detection algorithm (VRIDA): A signal based onboard diagnostic method for detecting hunting instability of rail vehicles. (March 2022)

• Record Type: Journal Article
• Authors: Kulkarni, R; Qazizadeh, A; Berg, M; Carlsson, U; Stichel, S
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 236:Number 3(2022)
• Page Start: 262
• Page End: 274
• Publication Date: 2022-03
• Subjects: High-speed train -- condition monitoring -- early warning alarm -- wheelset hunting -- axlebox accelerations; Railroads -- Periodicals; Personal rapid transit -- Periodicals; 625.1
• DOI: 10.1177/09544097211020578
• Languages: English
• ISSNs: 0954-4097

Abstract

In recent years, significant research transpired on onboard monitoring of various phenomena arising in dynamic vehicle-track interaction. One key issue being monitoring of vehicle hunting instability. Current hunting detection standards are appropriate for certification tests of vehicles, but incapable to monitor the health of the vehicle and track subsystems influencing the hunting instability. This paper proposes a signal based procedure for accurately triggering Hunting/No-Hunting alarm by conforming to requirements of onboard monitoring. A new method is conceived to reveal coherence among lateral and longitudinal accelerations during vehicle hunting. Furthermore, an index which amalgamates phase and amplitude information of lateral and longitudinal axlebox accelerations is introduced to detect coupled modes in lateral and yaw directions, i.e. hunting modes. Several simulations based pragmatic case studies are performed to assess the efficacy of the proposed procedure. The proposed method outperforms traditional hunting detection procedures by detecting more Hunting/No-Hunting occurrences. The proposed method contributes towards digitalization of rail vehicles through condition-based and predictive maintenance.