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Vehicle running instability detection algorithm (VRIDA): A signal based onboard diagnostic method for detecting hunting instability of rail vehicles
KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.ORCID iD: 0000-0001-5644-248X
KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.ORCID iD: 0000-0002-0875-3520
KTH, School of Engineering Sciences (SCI), Centres, The KTH Railway Group. KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Vehicle Engineering and Solid Mechanics, Rail Vehicles.ORCID iD: 0000-0002-2571-4662
KTH, School of Engineering Sciences (SCI), Engineering Mechanics, Fluid Mechanics and Engineering Acoustics, Marcus Wallenberg Laboratory MWL.
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2021 (English)In: Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit, ISSN 0954-4097, E-ISSN 2041-3017Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Sage Publications, 2021.
Keywords [en]
High-speed train, condition monitoring, early warning alarm, wheelset hunting, axlebox accelerations
National Category
Vehicle Engineering
Research subject
Järnvägsgruppen - Fordonsteknik; Vehicle and Maritime Engineering; Engineering Mechanics
Identifiers
URN: urn:nbn:se:kth:diva-296100DOI: 10.1177/09544097211020578OAI: oai:DiVA.org:kth-296100DiVA, id: diva2:1558379
Funder
Swedish Transport Administration, RV29
Note

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors are grateful for the financial support from KTH Railway Group (Project no. RV29).

Available from: 2021-05-30 Created: 2021-05-30 Last updated: 2021-05-30

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Publisher's full texthttps://journals.sagepub.com/doi/10.1177/09544097211020578

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Kulkarni, RohanQazizadeh, AlirezaBerg, MatsCarlsson, UlfStichel, Sebastian

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Kulkarni, RohanQazizadeh, AlirezaBerg, MatsCarlsson, UlfStichel, Sebastian
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The KTH Railway GroupRail VehiclesMarcus Wallenberg Laboratory MWL
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Proceedings of the Institution of mechanical engineers. Part F, journal of rail and rapid transit
Vehicle Engineering

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