Understanding Windowing And Overlapping Analysis
[latexpage]The following article was inspired by a question asked by a reader regarding my previous article - Data Windows : What, why and when?. Specifically, the reader asked "Could you please…
Figure 4: Energy corrected spectrum
Amplitude And Energy Correction – A Brief Summary
Amplitude and energy correction has been and is a continuing point of confusion for many people calculating spectra from time domain signals using Fourier transform methods. The first thing to say, the information contained in data presented as amplitude and energy corrected spectra is equivalent. The only difference is the scaling of the numbers calculated.
Data Windows : What, why and when?
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.
Orders v Time – Comparing Overall Levels
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.
Average Waterfalls Or Average Orders?
[latexpage]One would expect that averaging waterfalls and then extracting orders would give the same result as extracting orders from individual waterfalls and then averaging them. This is not the case.
