Vibration and exploring the Fast Fourier Transform with CAD

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Vibration and exploring the Fast Fourier Transform with CAD

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  • #787412
    SillyOldDuffer
    Moderator
      @sillyoldduffer

      This won’t be to everyone’s taste, but it is Engineering and I am going Model it!  Hopefully others will join in.  Plenty on here whose maths is much better than mine, plus a few electronics/computer gurus who might understand FFT.

      This isn’t a digital/electronics/computer/maths/CAD project – it’s intended to help isolate whatever fault(s) cause a machine tool or engine to vibrate.

      In Nigel’s Graham’s Fine Chatter topic , I mentioned a failed project where I attempted to identify the individual frequencies my lathe vibrates at by sticking a microphone on the bed and recording an audio file whilst cutting. The file is analysed with a computer to extract the frequencies with a Fast Fourier Transform. I almost certainly failed because I don’t understand FFT and digitilisation well enough yet.

      Fulmen expressed an interest:  Fast Fourier Transform: That’s very clever. I’d like to see a sensible guide on that some day. I did a quick search for digital motor stethoscopes but couldn’t find any. That would make for a sweet product , a bluetooth stethoscope with a spectrum analyzer.  

      I agree and as the hardware isn’t difficult, basically a microphone and  laptop,  I thought I’d have another go, this time modelling the FFT with a CAD tool that we can all download and play with.

      The tool is the GNU Radio Companion.  Free, and no programming required – models are created by dragging and dropping function blocks, and then linking them.  Download from here.

      When the tool is installed and started, try this:

      Should open a screen like this.  On the right, is a list of available function blocks.

      Screenshot from 2025-03-05 19-53-30

      Go to the Waveforms section (bottom of list), expand it, and click on Signal Source, hold the button, drag left and drop.  The box can be moved about later.  Then go to the Audio Section and drag and drop an ‘Audio Sink’.  Should get:

      Screenshot from 2025-03-05 20-01-38

       

      Note Signal Source has a blue out box and Audio Sink has an orange in.   Blue means ‘complex number’ and orange means ‘float’.  A booby trap because they aren’t compatible, so double click on Signal Source and change its Output Type from complex to float, making both out and in orange:

      Screenshot from 2025-03-05 20-06-42

      Then move the mouse onto Signal Source out, click hold, and drag the resulting arrow ended line onto Audio Sink in:

      Screenshot from 2025-03-05 20-08-27

      This models a 1kHz audio oscillator connected to the computer’s sound card.  Pressing the black triangular Play button top right on the tool-bar should activate it, making an annoying whistle.   No programming, soldering transistors, or plugging in cables!

      Tomorrow I’ll model 3 or 4 Signal Sources mixed together and try and retrieve the input frequencies from the muddle.   Watch this space.  More function blocks are needed.   Didn’t work last time I tried.

      Dave

       

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      #787416
      Michael Gilligan
      Participant
        @michaelgilligan61133

        For “the rest of us” … this is a very good introduction:

         

        https://youtu.be/QmgJmh2I3Fw?feature=shared

        MichaelG.

        #787417
        Robert Atkinson 2
        Participant
          @robertatkinson2

          Hi Dave,
          Looks interesting. I appreciate that this is a learning exercise but if you want someting a bit more ready to go have a look at Spectrum Lab.
          https://www.qsl.net/dl4yhf/spectra1.html
          For a DIY pickup rather than a microphone the “speaker” from a pair of cheap ear-buds works well and even better if you add a bit of mass to the center of the diaphram. A small nut fixed with something flexible like contact adhesive works well.

          A lot of digital oscilloscopes will do FFT but the limited amplitude resolution of most ‘scopes of 8 bits limits their usefulness. Even though a sound card has poor amplitude accuracy their high resolution and dynamic range make them a good choice for FFT. I have a couple of 12bit Pico technology USB ‘scopes that work well. One of them is an automotive model and their automotive software has an FFT function specifically intended for noise and vibration analysis.

          Robert.

          #787425
          John Haine
          Participant
            @johnhaine32865

            Worth looking at the Physics Toolbox Suite, and app for Android, possibly iPhone too.  Can do loads of stuff including audio fft using the phone sensors.

            #787428
            Michael Gilligan
            Participant
              @michaelgilligan61133

              Yes, the excellent Physics Toolbox Suite is available for iOS

              I would also recommend this:

              https://apps.apple.com/gb/app/sonic-tools-svm/id1245046029

              [cleverer people than I should read the developer’s comments]

              MichaelG.

              #787431
              Fulmen
              Participant
                @fulmen

                Thank you, Dave. I’ll take a dive into the subject tonight.

                #787433
                Julie Ann
                Participant
                  @julieann
                  On SillyOldDuffer Said:

                  …who might understand FFT.

                  I might be in that category; part of my Ph.D. was to do with extending the mathematics of FFTs and creating hardware architectures to compute FFTs.

                  It’s a common misconception that the FFT is an algorithm developed by Cooley and Tukey. The algorithm by Cooley and Tukey is simply one of many fast Fourier transform algorithms, some of the others being the prime factor algorithm, the Rader algorithm and that by Winograd.

                  Another misconception is that the Cooley and Tukey FFT is based on powers of 2. The original Cooley and Tukey paper simply states that the length of the input vector needs to be a highly composite number. It turns out that using powers of 3 is slightly more efficient than powers 2.

                  Of course from a practical point of view using powers of 2 is helpful when calculating via a digital computer. An FFT using prime, or mutually prime, factors may be computational efficient but indexing the data can be a pain.

                  The video posted by Michael is wrong, the FFT doesn’t exploit redundancy, it simply transforms one complex matrix to a series of sparse matrices that leads to fewer overall calculations.

                  Julie

                  #787442
                  Michael Gilligan
                  Participant
                    @michaelgilligan61133

                    Thanks for the correction, Julie

                    … I bow to your wisdom.

                    My personal experience was limited to using random vibration [and swept sine, of course] as a test-tool … I never really understood the mathematics.

                    MichaelG.

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