Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299147
Title: New techniques for vibration condition monitoring : Volterra kernel and Kolmogorov-Smirnov
Author: Andrade, Francisco Arruda Raposo
Awarding Body: Brunel University
Current Institution: Brunel University
Date of Award: 1999
Availability of Full Text:
Access through EThOS:
Access through Institution:
Abstract:
This research presents a complete review of signal processing techniques used, today, in vibration based industrial condition monitoring and diagnostics. It also introduces two novel techniques to this field, namely: the Kolmogorov-Smirnov test and Volterra series, which have not yet been applied to vibration based condition monitoring. The first technique, the Kolmogorov-Smirnov test, relies on a statistical comparison of the cumulative probability distribution functions (CDF) from two time series. It must be emphasised that this is not a moment technique, and it uses the whole CDF, in the comparison process. The second tool suggested in this research is the Volterra series. This is a non-linear signal processing technique, which can be used to model a time series. The parameters of this model are used for condition monitoring applications. Finally, this work also presents a comprehensive comparative study between these new methods and the existing techniques. This study is based on results from numerical and experimental applications of each technique here discussed. The concluding remarks include suggestions on how the novel techniques proposed here can be improved.
Supervisor: Not available Sponsor: Brunel University Department of Mechanical Engineering ; CAPES, Fundacao Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.299147  DOI: Not available
Keywords: Vibration condition monitoring ; Signal processing techniques ; Kolmogorov-Smirnov test ; Volterra series Pattern recognition systems Pattern perception Image processing Sound Manufacturing processes
Share: