Before we discuss the use of data windows, we should first remind ourselves of three basic properties of the FFT (Fast Fourier Transform) process.
First, energy information in signal must be preserved during transformation. That is, the energy measured on time signal must equal the energy measured on the frequency representation of that signal.
Second, an FFT converts the signal representation between time and frequency domains. The time domain representation shows when something happens and the frequency domain representation shows how often something happens.
And finally, an FFT assumes that the signal is repetitive and continuous.
Let us consider an example. First, we will look at the special case of a 10Hz sinusoid (Figure 1). This signal is periodic within the time record used to calculate the spectrum.
If we perform an FFT using the DATS software the result, shown below, will consist of a single line in the spectrum with an amplitude that represents the rms of the time series amplitude.
Now, let us consider a second example. In this case (Figure 3) we have a 9.5Hz sinusoid.
If we perform an FFT operation on this it yields the multi-line spectrum shown in Figure 4.
So why is it not a single line spectrum?It is still a sinusoid isn’t it? Well, not as seen by the FFT process. It assumes the signal to be periodic (not only within a single record) and on-going or continuous. The 9.5 Hz signal (Figure 3) seen in analog form is on-going and continuous, but when viewing it from a digital perspective (discrete number of samples in a specified time block), this signal is not a sinusoid (see Figure 5).
This is why the FFT produces the multi-line spectrum in figure 4 with up to 20 visible lines. Our next question is – How do we minimize the effects of the discontinuities? The answer is that we use something called a “window”. Typically, the window used for most general purpose data is the “Hanning” or “Von Hann”. In the DATS software it is called the “True Hanning” window.
The “Von Hann” or “Hanning” window is named after Julius Von Hann (1839-1921). Von Hann was an Austrian meteorologist and is seen as the father of modern meteorology. He was the director of the Central Institute for Meteorology in Vienna (1887-1897), professor of meteorology at the University of Graz (1897-1890) and professor of cosmic physics at the University of Vienna (1890-1910). In signal processing the Hann function was named after him by R. B. Blackman and John Tukey in “Particular Pairs of Windows.” published in “The Measurement of Power Spectra, From the Point of View of Communications Engineering”, New York: Dover, 1959, pp. 98-99.
Multiplying the window function (Figure 6) by the original time signal forces the signal to zero at the beginning and end of each time record (Figure 7).Placing multiple time records shown end to end shows the signal is now forced to be periodic when the time records are placed end to end.One problem solved, but the signal now in each of the time records is not a sinusoid any longer.This modification to the sinusoid is represented in the frequency domain as 4 lines in the frequency representation of the signal.
The spectrum which was 20 lines before applying the Hanning Window function has now been reduced to a spectrum of only 4 lines.Not perfect, but much closer to the single line spectrum one would expect for the single frequency time signal. Obviously, one never get something for nothing.What happens to the single line spectrum from the 10 Hz single frequency time signal?Instead of a single line spectrum, the modified single frequency signal now is represented by a 3 line spectrum (Figure 9).There is no loss in amplitude read out accuracy, but a loss in frequency resolution is present.
But what do we do about the data that is being missed at the beginning and end of each record? Data is being reduced and/or set to zero over one half the time record -How do we assure events happening in the region of reduced amplitude areas?A processing technique exists called “overlap” processing.By applying this technique, the events occurring at or near the beginning and ending of the time records are enhanced by using overlap processing. Figure 10 represents records being processed “end-to-end” or 0% overlap. Figure 11 shows overlap of 50%.
Typically 67% overlap (Figure 12) is considered sufficient to weight the events near the beginning/end of the time record, however 75% overlap (Figure 13) is somewhat better.Today, with the high processing capabilities of computers, there is little reason not to utilize overlap processing.
Different shapes of the windowing function dictate what the spectrum shaping looks like.The table below lists a few window function types. All Window functions that operate on the time domain signals typically zero out the beginning and end of time record. The obvious exceptions to this are the “force” and “exponential decay” windows used in hammer/modal applications.
When to use
Only when signal is known to be periodic within time record
Most often used general purpose
When absolute amplitude accuracy is required – calibration/sensitivity check
When relatively high amplitude accuracy and frequency resolution important
This article was originally created for a presentation to a North American Prosig User group (PUG).
John Mathey graduated with a MS degree from the University of Toledo in 1972. John has over 35 years of experience with instrumentation, measurement, and analysis. Twenty-five of those years were spent at Ford Motor Company solving and providing training for vehicle noise, vibration, and harshness (NVH) issues. He was a technical specialist at Prosig USA, Inc. where he provided technical support to Prosig customers in the U.S.A.