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Title: New techniques for vibration condition monitoring: Volterra kernel and Kolmogorov-Smirnov
Authors: Andrade, Francisco Arruda Raposo
Keywords: Vibration condition monitoring;Signal processing techniques;Kolmogorov-Smirnov test;Volterra series
Issue Date: 1999
Publisher: Brunel University School of Engineering and Design PhD Theses
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.
Description: This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.
Appears in Collections:Mechanical and Aerospace Engineering
Dept of Mechanical Aerospace and Civil Engineering Theses

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