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Bolster spring fault detection strategy for heavy haul wagons

journal contribution
posted on 14.09.2018, 00:00 by Chunsheng LiChunsheng Li, S Luo, Colin ColeColin Cole, Maksym SpiryaginMaksym Spiryagin
An on-board health monitoring system is proposed for heavy haul wagons in this paper including a signal-based fault detection and isolation (FDI) method and an on-line fault diagnose strategy. Such a system, to be feasible on freight wagons, must be sufficiently cheap and robust, hence the design assumes the constraint of using only two accelerometers mounted on the front left and right rear part of each carbody in a heavy haul train. This paper focuses on the detection of bolster spring fault conditions. The problem is made more complex by the modes of failure which might be expected in bolster spring nests. Types of spring failure are firstly identified and discussed covering situations of broken (shortening springs) and softening (individual spring loss from a nest or cross-section loss through corrosion). The effects of these faults and their detectability were investigated using simulations on straight and curved track and using a fully detailed model of a typical 40 t axle-load heavy haul wagon. The simulation results were then examined and compared using cross-correlation analysis and an FDI system was proposed. The FDI system introduced five possible fault indicators. Initial results indicated that it was possible to detect changes in bolster stiffness of ±25%. An on-line fault diagnoses strategy is proposed for bolster spring faults which only requires data from wagon monitoring during travel around sharp curves to detect and the occurrence of confirm faults. The functionality envisaged needs only a ‘once per trip’ monitoring site, such as a sharper curve, and is aimed at condition monitoring rather than the provision of alarms or comprehensive monitoring of all events. © 2018, © 2018 Informa UK Limited, trading as Taylor & Francis Group.

Funding

Other

History

Volume

56

Issue

10

Start Page

1604

End Page

1621

Number of Pages

18

eISSN

1744-5159

ISSN

0042-3114

Publisher

Taylor & Francis, UK

Peer Reviewed

Yes

Open Access

No

Acceptance Date

23/12/2017

External Author Affiliations

Southwest Jiaotong University, China

Author Research Institute

Centre for Railway Engineering

Era Eligible

Yes

Journal

Vehicle System Dynamics