Classical & Bayesian Spectral and Tracking Analysis | Harris K. Gondo
| | Abstract | Analysis of rotating machines for design purpose or fault diagnosis requires generally an estimation of parameters that characterizes the vibration and sound patterns. Spectral estimation methods based on classical techniques assume stationarity and high signal-to-noise ratio (SNR). The nonstationarity of vibration and acoustic data is accommodated by the commonly used windowing technique. This thesis explores the Bayesian fundamental frequency estimation theory and investigates both classical and Bayesian approaches to the problem of spectral analysis and slowly varying frequency tracking. We use Periodogram, MUSIC, linear Kalman filter and Bayesian techniques to jointly estimate and track the spectral components. The error sensitivity is shown and the performance for frequency estimation is compared. Such a comparison is based on stationary time series corrupted by additive white Gaussian noise (AWGN). Further, the effect of the prior hyperparameters adjustment is illustrated on the speed profile estimated. The most important results are shown through the experiments in computer simulation. The Bayesian estimator performs well regardless the nature of the signal. Moreover, it provides a reliable and new way of determining the running speed of rotating mechanical system. The marginalization property of the Bayesian can be used to remove DC component (if present) in the data and target the fundamental frequency of interest. That is, the Bayesian method can provide more accurate estimation than stochastic and classical methods when the hyperparameters are adjusted correctly. The reason for such performance status is detailed. | | Type | Master's thesis [Academic thesis] | | Year | 2008 | | Publisher | Informatics and Mathematical Modelling, Technical University of Denmark, DTU | | Address | Richard Petersens Plads, Building 321, DK-2800 Kgs. Lyngby | | Series | IMM-M.Sc.-2008-01 | | Note | Supervised by Assoc. Prof. Ole Winther, IMM, DTU. | | Electronic version(s) | [pdf] | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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