The profound value of wayside monitoring in helping safeguard the RAMS of railway operations is undeniable. However, despite significant investments by the rail industry, the efficiency and reliability of wayside monitoring have not reached the desired level. Structural deterioration of the rail infrastructure and rolling stock faults still remain a significant problem which needs to be addressed as traffic density, train speeds and axle loads increase in rail networks around the world. The main objectives of this study were to develop and evaluate an advanced wayside monitoring system based on acoustic emission and vibration analysis that can detect various types of axle bearing defects in rolling stock and structural deterioration in cast manganese crossings. The potential architecture for different levels of system correlation has been proposed which can be further integrated with customised monitoring system. A novel signal processing technique based on spectral coherence has been developed. This particular method is based on the identification of suitable templates containing features of interest. It also features in identifying the severity of the defect. In addition, a suitable approach for data fusion from various sensors has been investigated. Successful tests have been carried out under simulated conditions and in the UK network.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:731887 |
| Date | January 2017 |
| Creators | Huang, Zheng |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/7904/ |
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