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Accelerometers Fourier Transformations and More!

This is my first attempt at a contribution to this community, so please bear with me.

We were able to detect tool height by reading an Analog Devices accelerometer attached to the work piece, and looking for a spike in the frequency domain corresponding to the spindle speed. I think it was an ADXL320, and we read the signal through the microphone port on the computer.

This should be all you need to do some comp black magic to make it work. Disclaimer: I don't think this operates in the "realtime" domain, so low feed rates are recommended. Also, see here: http://linuxcnc.org/docview/html/hal_halmodule.html Any comments or feedback are welcome! David Stonely deserves all the credit for the code.

-Mikegg

  1. !/usr/bin/python

import pyaudio import numpy import sys import struct import hal import time

  1. Functions

  1. HAL Stuff

h = hal.component("tddetect") h.newpin("enable", hal.HAL_BIT, hal.HAL_IO) h.newpin("is_mdi_mode", hal.HAL_BIT, hal.HAL_IN) h.newpin("spindle_speed", hal.HAL_FLOAT, hal.HAL_IN) h.newpin("touch", hal.HAL_BIT, hal.HAL_OUT) h.newparam("mov_avg", hal.HAL_FLOAT, hal.HAL_RO) h.newparam("pct_change", hal.HAL_FLOAT, hal.HAL_RO) h.newparam("pbc", hal.HAL_U32, hal.HAL_RO) h.newparam("frange", hal.HAL_U32, hal.HAL_RW) h.newparam("mov_avg_window", hal.HAL_U32, hal.HAL_RW) h.newparam("td_thresh", hal.HAL_FLOAT, hal.HAL_RW) h.ready()

h['mov_avg_window'] = 1

float_buffer = numpy.zeros(1024) mov_avg_buffer = numpy.zeros(1024) mov_avg_index = 0 power_base_acc = 0.0 power_base = 0.0 power_base_cnt = 0 initialize = True chk_enable = False

chunk = 1024 FORMAT = pyaudio.paFloat32 CHANNELS = 1 RATE = 44100 F_STEP = 44100.0 / chunk

p = pyaudio.PyAudio?()

stream = p.open(format = FORMAT,

                channels = CHANNELS,
                rate = RATE,
                input = True,
                frames_per_buffer = chunk)

debug = True

try:

	while 1:
		if (h['enable'] and h['is_mdi_mode']):
			lfreq = (h['spindle_speed'] / 60.0) - (h['frange'] / 2) 
			lfreq_bin = round(lfreq / F_STEP) 			
			hfreq = lfreq + (2 * h['frange'])
			hfreq_bin = round(hfreq / F_STEP) 
  1. if debug:
  2. print "lfreq_bin:", lfreq_bin, " hfreq_bin", hfreq_bin

			data = stream.read(chunk)
			float_buffer = struct.unpack('@' + 'f'*1024, data)
			b = numpy.array(float_buffer)
			c = numpy.fft.rfft(b)

			d = numpy.square(abs(c))
			tot_pow = numpy.sum(d[lfreq_bin:hfreq_bin+1])
			mov_avg_buffer[mov_avg_index] = tot_pow
  1. if debug:
  2. print "mov_avg_buf: ", mov_avg_buffer[0:h['mov_avg_window']]

			h['pbc'] = power_base_cnt

			if power_base_cnt < h['mov_avg_window']:
				power_base_acc += tot_pow
				power_base = power_base_acc / h['mov_avg_window']
				power_base_cnt += 1
  1. print power_base_acc, ' ', power_base
				chk_enable = False
			else:
				chk_enable = True

			mov_avg = numpy.sum(mov_avg_buffer[0:h['mov_avg_window']]) / h['mov_avg_window'] 
  1. if debug or not chk_enable :
  2. print 'mov_avg: ', mov_avg

			mov_avg_index += 1 
			if mov_avg_index == h['mov_avg_window']:
				mov_avg_index = 0

			h['mov_avg'] = float(mov_avg)

			pct_change = abs((mov_avg - power_base)/power_base) * 100

			h['pct_change'] = float(pct_change)

			initialize = True

			if ((pct_change > h['td_thresh']) and chk_enable)  :
  1. print "pct_change: ", pct_change, ' chk_enable: ', chk_enable
				h['touch'] = True
				time.sleep(0.1)
				h['enable'] = False
  1. print "Detected Power Change"

			debug = False
		else:
			if initialize :
				h['touch'] = False
  1. if debug:
  2. print "Initializing"
				float_buffer   = numpy.zeros(1024)
				mov_avg_buffer = numpy.zeros(1024)
				mov_avg_index  = 0
				power_base_acc = 0.0
				power_base     = 0.0
				power_base_cnt = 0
				initialize = False
				chk_enable = False
				debug = True

except KeyboardInterrupt?:

	raise SystemExit?
finally:
	stream.close()
	p.terminate()

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Edited December 15, 2011 6:35 pm by Mikegg (diff)
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