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Digital Signal Processing

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Presentation on theme: "Digital Signal Processing"— Presentation transcript:

1 Digital Signal Processing
Modal analysis and testing S. Ziaei Rad

2 Fourier Analysis Fourier series Fourier Transform
Discrete Fourier series S. Ziaei-Rad

3 Fourier series Assume that x(t) is a periodic function in time.
S. Ziaei-Rad

4 Fourier series (Alternative form)
B- S. Ziaei-Rad

5 Fourier Transform A non-periodic function x(t) which satisfies the condition: Can be represented by: where S. Ziaei-Rad

6 Fourier Transform (Alternative form)
And S. Ziaei-Rad

7 Discrete Fourier Transform (DFT)
A function which is defined only at N discrete points can be represented by a finite series. where S. Ziaei-Rad

8 Discrete Fourier Transform Alternative form
Note that: This form of DFT is most commonly used on digital spectrum analyser. The DFT assumes that the function x(t) is periodic. It is important to realize that in the DFT, there are just a discrete number of items of data in either form, i.e. N values for x and N values for Fourier series. S. Ziaei-Rad

9 DFT Example Let N=10 In time domain we have:
In frequency domain, we have: or S. Ziaei-Rad

10 Discrete Fourier Transform (DFT)

11 DFT Spectrum S. Ziaei-Rad

12 DFT Spectrum S. Ziaei-Rad

13 DFT Spectrum S. Ziaei-Rad

14 DFT 1- The input signal is digitized by an A-D converter.
2- The input is recorded as a set of N discrete values, evenly spaced in period T. 3-The sample is periodic in time T. 4- There is a relation between the sample length T, the number of discrete values N, the sampling (or digitizing) rate and the range of resolution of the frequency spectrum, i.e. . is called the Nyquist frequency. S. Ziaei-Rad

15 DFT 1- Usually, the size of transform (N) is fixed for an analyser, therefore the frequency range and frequency resolution is only determined by the length of the sample. 2- The basic equation (*) will be used to determine the coefficient S. Ziaei-Rad

16 DFT The basic equation that is solved to determine spectral composition is: ** or S. Ziaei-Rad

17 FFT - Leakage - Averaging - Windowing - Filtering
Some efforts has been devoted to equation (**) for calculation of spectral coefficients. Cooley and Tukey (1960) introduced an algorithm called “Fast Fourier Transform (FFT)”. The method requires N to be an integral power of 2 and the values usually taken is between 256 to 4096. There are number of features of digital Fourier analysis, if not properly treated, can give rise to erroneous results. These are generally the result of discretisation approximation and the limited length of the time history. - Aliasing Zooming - Leakage Averaging - Windowing Filtering S. Ziaei-Rad

18 Aliasing - Aliasing is a problem from discretisation of the originally continuous time history. - With this descretisation process, the existence of very high frequency in the original signal may well be misinterpreted if the sampling rate is too slow. The phenomenon of aliasing a- Low-frequency signal b- High-frequency signal S. Ziaei-Rad

19 Leakage - Leakage is a direct consequence of a finite length of time history. In Fig. a, the signal is perfectly periodic in the time window T and the spectrum is a single line. In Fig. b, the periodicity assumption is not valid and the spectrum is not at a single frequency. Energy has ‘leaked’ into a number of spectral lines in close to the true frequency. Leakage is a serious problem in many application of DSP, including FRF measurements. S. Ziaei-Rad

20 Leakage S. Ziaei-Rad

21 Leakage Ways of avoiding leakage:
Changing the duration of the measurement sample length to match any underlying periodicity in the signal. However, it is difficult to determine the period of signal. Increasing the duration of the measurement period, T, so that the separation between spectral lines is finer. Adding zeros to the end of the measured sample (zero padding) In this way we partially achieving the preceding result but without requiring more data. Or by modifying the signal sample obtained in such a way to reduce the severity of leakage. The process is called ‘windowing’. S. Ziaei-Rad

22 Windowing - In many situation, the most practical solution to the leakage problem is windowing. - There are a range of different windows for different classes of problems. Windowing is applied to the time signal before performing the Fourier Transform. x(t) measured signal w(t) window profile Or in Frequency Domain Where * denotes the convolution process. S. Ziaei-Rad

23 Windowing Hanning Exponential Boxcar Cosine-taper S. Ziaei-Rad

24 Windowing Boxcar Hanning Cosine-taper Exponential S. Ziaei-Rad

25 Windowing S. Ziaei-Rad

26 Effect of Hanning Window on Discrete Fourier Transform
S. Ziaei-Rad

27 Filtering -This is another signal conditioning process which has a direct parallel with windowing. In filtering, we simply multiply the original signal spectrum by the frequency characteristic of the filter. or in time domain Common types of filters are: High pass Low pass Narrow-band Notch S. Ziaei-Rad

28 Filtering Frequency and time domain characteristics of common filters
Narrow-band High-pass Notch Band-limited Frequency and time domain characteristics of common filters S. Ziaei-Rad

29 Improving resolution Inadequate frequency resolution especially at lower end of the frequency range and for lightly-damped systems occurs because of -limited number of discrete points available - maximum frequency range to be covered - the length of time sample Possible actions to improve the resolution Increasing transform size Zero padding Zoom S. Ziaei-Rad

30 Increasing transform size
An immediate solution to this problem would be to use larger transform. This gives finer resolution around the region of interest. This caries the penalty of providing more information than required. Until recently, the time and storage requirements to perform the DFT were a limiting factor. - Transform size of order 2000 to 8000 are standard. S. Ziaei-Rad

31 Zero padding To maintain the same overall frequency range, but to increase resolution by n, a signal sample of n times the duration is needed. One way is to add a series of zeros to the short sample of actual signal to create a new sample which is longer than the original measurement and thus provide the desire finer resolution. The fact is that no additional have been provided while apparently greater detail in the spectrum is achieved. It is not a genuinely finer spectrum, rather it is the coarser spectrum that interpolated and smoothed by the extension of the analysed record. An example of the effect and potential dangers of zero padding is shown in the next slide. S. Ziaei-Rad

32 Zero padding Results using zero padding to improve resolution.
a- DFT of data between 0 to T1 b- DFT of data padded to T2 c- DFT of full record 0 to T2 S. Ziaei-Rad

33 Zoom - The common solution to the need for finer frequency resolution is to zoom on the frequency range of interest and to concentrate all the lines into a narrow band between There are different ways of doing this but perhaps the easiest one is to use a frequency shifting process coupled with a controlled aliasing device. Spectrum of signal Band-pass filter S. Ziaei-Rad

34 Averaging When analyzing random vibrations signal, we obtain estimates for the spectral densities and correlation functions which are used to characterize this type of signal. Generally, it is necessary to perform an averaging process, involving several individual time records, before a confident results is obtained. Two major considerations which determine the number of average: - the statistical reliability - the removal of random noise from the signals S. Ziaei-Rad

35 Different interpretations of multi-sample averaging
Sequential Overlap S. Ziaei-Rad

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