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IOT - Internet of Things - Monitoring Motor Arrays

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POSTED IN: Data Analytics & Visualization Blog

IOT - Internet of Things - Monitoring Motor Arrays

 

 

Video - Early Test - Old Geophone

 

DC Bias is fiddly depending on transducer - what worked for Geophone did not for these GIKFUN-HDX-2's (no data sheet)


//const int sdvig = 32767; //DC bias of the ADC, approxim +2.5V.

//const int sdvig = 15000; //DC bias of the ADC, approxim +1V????  THE BLUE SENSORS DC BIAS IS VERY DIFFERENT FROM GEOPHONE. (( WORKS with 2.5v dc bias in, 1v out)


const int sdvig = 00000; //DC bias of the ADC, approxim +1V????  THE BLUE SENSORS DC BIAS IS VERY DIFFERENT FROM GEOPHONE. (( WORKS)

 

CODE HERE

https://github.com/rustyoldrake/earthquake.arduino/blob/master/arduino_FFT_motor_senseV3.ino

 

Simple Method

  1. Have an array of motors running - sensors picking up time domain data for each one
  2. Do a FFT Fourier Transform on it - then time slice into 'windows'
  3. Tag each window over time for Spectrum
  4. Over time, use prior knowledge of 'known good' and if available 'known faulty' to classify into good, change, warn and fail states - moving window
  5. Unpack data a little more (e.g. frequency shift/delta, noise floor, skirt size, signal to noise ratio, signal purity, harmonics) for windows, e.g. N, N-1, N-10 etc..
  6. Use basic open source Machine Learning tools (e.g. Random Forest) for classify into good/change/warn/fail based on prior knowledge
  7. Rinse and repeat

 

There are more sophisticated Deep Learing and ML tools that probably handle the time domain more elegantly, so that's worth reading up on...

 

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About the Author

Ryan Anderson

Ryan Anderson

Hi! I like to play with data, analytics and hack around with robots and gadgets in my garage. Lately I've been learning about machine learning.

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