User note: FFT averaging

Averaging

NVGate provides many settings for averaging result. This document aim at describing these functions and parameters for averaging data result. This document focuses on averaging available at FFT modules. These are quite similar setting for SOA modules.

1/ Why averaging?

There are many reasons that lead to average measurement to get accurate results.

FFT analysis is performed on an extract of time signal which is called "trigger block"

Averaging enable to get compliance with the basics FFT hypothesis.

• Get a analysis on a stationary signal data
• Reduce input noise, averaging increase the signal/noise ratio
• By definition FFT estimator provides 100% standard deviation

Averaging parameters should be setup according analysis and tracked phenomena.

2/ Averaging Domain

Averaging could be done in either Frequency or Time Domain.

• Frequency Average

Each signal block generates an FFT, the average result is the average of the spectrum :

Data result is available for magnitude only. There is no information about the phase.

• Time Domain

The average is performed in time domain, the FFT is computed on the average time signal data

It results complex data that can be displayed in Magnitude and Phase (or Real Imaginary).

Time domain averaging makes sense for synchronous time block, it must be use with a trigger for block synchronization, else the average make no sense.

This average is useful to avoid getting result from external sources. For example in a noisy factory, setting a trigger linked to a specific machine will prevent from getting noise from other machine.

There is an additional average type named Frequency Synchronous or FDSA. This is based on Frequency domain average but it act as Time domain average. It uses one frequency line (specifies by user) as a phase reference, each FFT is recomputed so that the phase of this reference frequency is set to "0".

This allows to focus on a specific phenomena even if there is no trigger available to perform Time domain analysis.

3/ Average Type

1) linear average

For the linear average, each "n" spectrum get the same weight for the averaging. It usually set for long time averaging result. The result is according the global level of energy for each narrow band during the duration of measurement.

2) Exponential Average

The Exponential Average is computed with a linear average for the last "n" spectrums whereas older spectrum has a lower weight. Exponential average provides an infinite sliding result. This is commonly use with a low value of average. As it kept the student variation in memory for a short time, this is a good setting for monitoring or tracking non temporary phenomena.

3) Peak Hold average

The Peak Hold average is not any mathematical average. The peak hold provides a result that correspond, at each band, for the maximal value of each the "n" spectrum. It is useful to get the maximal values as a worst case of measurement:

4) Ref. Peak Hold average

In the same way the Ref. Peak Hold average is no more a mathematical average.

It is basically same feature as the Peak Hold average. The main difference that it collects and holds the data of the spectrum according to a new peak on the reference channel.

This technique is used for some modal test with a shaker excitation to reject non linear phenomena. In this case the Ref. Peak Hold average is used as a tracking filter linked with excitation frequency.