This thesis addresses the problem of detecting and positively identifying the approximately known acoustic emission signatures produced through fatigue crack propagation in power station steam chests. This work includes extensive laboratory fatigue testing to produce and record signatures in specimens fabricated from the steam chest steel, on-site recording of the ambience noise levels from a fully operating oil-burning power station and develops and demonstrates the effectiveness of various signal processing techniques at extracting the signatures embedded in the noise. This noise is high amplitude, giving us a low signal to noise ratio, and is broad in the frequency domain, with both regular and irregular high-amplitude metallic noise transients that cover the entire frequency range of interest. It is therefore essential to use sophisticated signal post-processing techniques to detect and to identify the crack signatures. The post-processing techniques developed and employed include time-frequency transformations, matched filters and signal expansion filters implemented in both in the time domain and in various two-dimensional time-frequency domains. From a performance comparison, both on the experimentally recorded data and on data digitally generated for the purpose, we determine the optimum signal processing method for our requirements and provide an assessment of the relative computational efficiencies. Generated for comparison are spurious but similar signatures characteristic of the power station steam chest environment; oxide crushing within an existing dormant crack and stress corrosion cracking signatures produced by the same steel constantly loaded in a corrosive environment. It is demonstrated that there is sufficient distinction between these signatures and those produced by crack propagation.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:359963 |
| Date | January 1997 |
| Creators | Cook, Jon |
| Contributors | Bannister, R. H. |
| Publisher | Cranfield University |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://dspace.lib.cranfield.ac.uk/handle/1826/11060 |
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