Download Advanced Applications in Acoustics, Noise and Vibration by Frank Fahy, John Walker PDF

By Frank Fahy, John Walker

Complex functions in Noise and Vibration provides remedies of acoustic and vibration phenomena including summaries of the newest wisdom, research and techniques at the moment to be had for facing functional difficulties.

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Extra resources for Advanced Applications in Acoustics, Noise and Vibration

Sample text

R. K. 18 Time–frequency distributions for beam response data. measurements from these curves, allied to knowledge of the spacing between the site of excitation and measurement, allow one to estimate the dispersion laws for this beam. 18c depicts the WVD for this data. Two curves, defining the dispersion curves for the two wave packets, can be identified. In this case, these curves are drawn as sharper lines, implying that one can identify the arrival time of a specific frequency component with greater accuracy, suggesting that using the WVD may allow one to estimate dispersion laws more accurately than can be achieved using the spectrogram.

The time series analysed contains 512 samples. In addition to the methods discussed herein, a classical non-parametric method is also used to compute a spectral estimate. Specifically, a segment averaging approach is employed (for details see Hammond 1998). 14. In each frame, the spectrum of the filtered noise is shown as a dotted curve and the frequencies of the sinusoidal components are indicated by vertical dotted lines. 14a,b shows the results obtained from segment averaging. 14a) results in a heavily smoothed spectral estimate in which the two closely spaced sinusoids are not resolved and the heights and the depths of the resonance and anti-resonance are underestimated.

It is in this form that h n can be regarded as a system impulse response sequence, u n a white noise input sequence and x n the output sequence. 55) are characterised by the infinite set of parameters g n or h n . 55) is H f 2 2 . However, for practical purposes, it is necessary to reduce the models to those containing only a finite number of parameters; typically AR, MA or ARMA models are used to achieve this. R. K. Hammond section, if we wish to estimate the power spectral density from a finite length of data, it is only sensible to choose an estimation procedure appropriate to the data model.

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