Point machines are the key actuator used in railways to provide a means of moving a switch blade from one position to the other. Failure in the point actuator has a significant effect on train operations. Condition monitoring systems for point machines have been therefore implemented in some railways, but these condition monitoring systems have limitations for detecting incipient faults. Furthermore, the majority of condition monitoring systems which are currently in use cannot diagnose faults. The ability to diagnose faults is useful to maintenance staff who need to fix problems immediately. This thesis proposes a methodology to detect and diagnose incipient faults using an advanced algorithm. In the main body of this thesis the author considers a new approach using Wavelet Transforms and Support vector machines for fault detection and diagnosis for railway electrical AC point machines operated in Japan. The approach is further enhanced with more data sets collected from railway electrical DC point machines operated in Great Britain. Furthermore, a method to express the qualitative features of healthy and faulty waveforms was proposed to test the transferability of the specific algorithm parameters from one instance of a point machine to another, which is tested on railway electrical DC point machines used in Great Britain. Finally, an approach based on Wavelet Transforms and Neural networks is used to predict the drive force when the point machine is operating. The approach was tested using electrical DC point machines operated in Great Britain. It is shown through the use of laboratory experimentation that the proposed methods have potential to be used in a real railway system.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:571826 |
| Date | January 2013 |
| Creators | Asada, Tomotsugu |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/4155/ |
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