By combining a speed signal with a data signal and using the Short Time FFT algorithm (Hopping FFT), it is possible to extract order data directly as a function of time (Orders from Hopping FFT) rather than as a function of speed (Waterfall). This is very useful when analyzing a complete operational cycle which includes run ups, rundowns and periods at operational speeds.
OVERALL LEVEL, DYNAMIC RMS & STANDARD DEVIATION
The overall level, the dynamic rms and the standard deviation are different commonly used names for the same quantity. They all imply that the mean or dc level of the time history has been ignored in the calculation. The overall level is typically used with acoustics and measures the dynamic part of the signal. In acoustics it is usually expressed in dB but with vibration then linear units are preferred. Dynamic Rms is a term used to indicate it only pertains to the vibration content. The standard deviation is a term used by statisticians to again signify using the fluctuating part of the signal.
As part of this process the Prosig DATS software calculates the overall signal level (rms) as a function of time. This is actually available in two forms: one as the overall level computed from frequency data; and secondly as the Dynamic Rms computed by the DATS FFTHOP module from each time slice. A third and independent method of evaluating the variation of the overall level with time is to use the trend module to compute the Standard Deviation.
In the example in Figure 1 all three measures give the same results but this is not always the case as shown later.
The example the Hopping FFT sequence in Figure 1 used a Rectangular data window but in Figure 2 we see exactly the same data with the exception that the Hopping FFT sequence used a Hanning window.
The overall level now differs from the other two measures because it is calculated after the application of the data window. In this case the Hanning data window has emphasized the central section of each time slice, so that it is more “time centered”. This is illustrated in Figure 3 by showing the measures when over plotted on the original time history.
The use of the data window helps to localise the phenomenon.