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Onboard condition monitoring of vehicle-track dynamic interaction using machine learning

Enabling the railway industry’s digital transformation

Time: Thu 2023-06-01 10.00

Location: F3, Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Vehicle and Maritime Engineering

Doctoral student: Rohan Kulkarni , Teknisk mekanik, Järnvägsgruppen, JVG

Opponent: Prof. Jordi Viñolas, Escuela Politécnica Superior, Universidad Francisco de Vitoria, Madrid, Spain

Supervisor: Mats Berg, Järnvägsgruppen, JVG, Teknisk mekanik; Ulf Carlsson, Teknisk mekanik; Alireza Qazizadeh, Teknisk mekanik, Järnvägsgruppen, JVG; Professor Sebastian Stichel, Järnvägsgruppen, JVG, Teknisk mekanik

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The railway sector’s reliability, availability, maintainability, and safety (RAMS) can significantly improve by adopting condition based maintenance (CBM). In the CBM regime, maintenance decisions are driven by condition monitoring (CM) of the asset. This thesis proposes machine learning (ML) based onboard CM (OCM) algorithms for CM of vehicle-track dynamic interaction via vehicle response (VR). More specifically, the algorithms are developed to monitor track irregularities (TI) and vehicle running instability incidences (VRII) via VR.

CM of TI from onboard accelerations is a cost-effective method for daily surveillance of tracks. Most of the latest research is focused on monitoring vertical irregularity via vertical accelerations. Less attention is given to monitoring alignment level (AL) and cross level (CL) track irregularities. The PhD thesis proposes an ML based OCM algorithm to identify track sections with AL and CL  track irregularities exceeding maintenance thresholds via bogie frame accelerations (BFAs). In this thesis, the OCM algorithm’s supervised ML models are trained on BFAs’ datasets synthesized with multibody simulation (MBS) of a high-speed diagnostic vehicle. Furthermore, the trained ML models and OCM algorithm are validated with measurements acquired by the same high-speed vehicle. The proposed OCM algorithm shows excellent performance in track quality surveillance only from BFAs. 

OCM of vehicle running instability (VRI) is important to ensure safety and onboard ride comfort. The latest research focuses on designing OCM algorithms for detecting VRI, but these OCM algorithms lack fault diagnosis (FD) of detected VRII. The PhD thesis proposes various OCM algorithms under an "intelligent vehicle running instability detection algorithm" (iVRIDA) umbrella to detect VRII and diagnose corresponding root causes via carbody accelerations. The occurrence of VRI during regular operation across a whole train fleet is an anomaly. Thus, an unsupervised anomaly detection (AD) based iVRIDA algorithm is proposed and later extended as iVRIDA-fleet for vehicle fleetwide application. The proposed OCM algorithms iVRIDA and iVRIDA-fleet are verified by onboard measurements of a European high-speed vehicle and the Swedish X2000 vehicle fleet.

The thesis contributes towards the digitalization of vehicle and track maintenance by enabling adaptation of the CBM regime.