Video camera-based overhead line condition monitoring for enhanced asset management

Maksabi, Mersedeh (2021). Video camera-based overhead line condition monitoring for enhanced asset management. University of Birmingham. Ph.D.

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Abstract

Having a direct impact on performance of the catenary-pantograph system, the reliability of the overhead contact line is crucial for continuous current collection, safe network operation, preventing delays and consequential financial losses. Contrary to condition assessment of the pantograph that can be performed off the track in rolling stock depots, condition monitoring of the contact wire comprises certain complexities and requires line closures. Although challenging, the consequences of catenary-pantograph system failure caused by bad positioning of the contact wire or its wear established a need within the railway industries for developing monitoring techniques to enable routine assessment of the distributed contact lines at the lowest disruption level. This significant need led to allocation of significant funding and deployment of inspection cars equipped with high-fidelity sensors for measurement of the parameters affecting the contact stability. The drawbacks of using sensors drawn attention towards a monitoring alternative that enables condition assessment of the asset non-invasively and through processing of video images. The most recent vision-based monitoring system aids in identifying probable anomalies within the pantograph collector strips through extraction of the contact wire stagger from the recorded data. Yet, the contact wire uplift is another key parameter affecting the contact stability and safe running of the train. As one of the predominant parameters affecting the performance of catenary-pantograph system, the focus of this thesis is on the contact wire uplift. Two vision-based monitoring approaches of trackside and on-board are considered and the feasibility of acquiring contact wire uplift parameter from the video images as well as reliance on the image data are investigated.

Type of Work:

Thesis (Doctorates > Ph.D.)

Award Type:

Doctorates > Ph.D.

Supervisor(s):