This thesis presents work carried out over a number of years within the field of railway vehicle instrumentation. The railway industry is currently moving to be more heavily “data driven”. This means that railway organisations are putting policies into place whereby decisions have to be justified based on recorded and citable data. To achieve this, the railway industry is increasingly turning to greater and greater levels of instrumentation to deliver the data on which to base these decisions. This thesis considers not only this increased requirement for data, but the frameworks and systems that must be put into place in order first to obtain it, and then to extract useful information from it. In particular the author considers the issue of contextualisation of data, where multiple datastreams may be used to provide context for, or allow more accurate and beneficial interpretation of each other in order to support better decision making. In order to obtain this data, the thesis explores, through a series of case studies, a number of options for different instrumentation system architectures. This culminates in the development of a distributed system of embedded processors arranged in an extensible modular framework to provide a rich, coherent and integrated dataset which can then be processed contextually to yield a better understanding of the railway system.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:560860 |
| Date | January 2012 |
| Creators | Stewart, Edward James Charles |
| Publisher | University of Birmingham |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://etheses.bham.ac.uk//id/eprint/3767/ |
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