I have an IR pyroelectic flame sensor which detects flames within the 8-10hz of flame flicker. I am new to the ADC world so any help is very appreciated.

Documentation: https://pyreos.com/wp-content/uploads/2020/11/Pyreos-Analog-TO-Flame-Sensor-One-Channel.pdf

I have the py0573 which has a bandpass filter for CO2 spectrum. According to the company's algorithm recommendation, I basically need to plot/detect FFT (Fast Fourier transform) bins from the 5-15hz frequency to determine a "flame" exists on the FOV of the sensor.

In order for me to read the signal from the half-rail, I installed a MCP3008 to my RPi model 4 and set the signal to CH0 of the ADC. I also included a .1uf decoupling capacitor.

Using the below code, I am able to get some readings from the sensor, but I am unsure if this sampling rate is setup correctly or not.


  1. How do I setup the read from the ADC within the 8-10hz range?
  2. Is there a way to plot this with FFT? Do I need FFT or can I determine the total sum values from ADC to indicate a "flame"?
  3. Is the frequency setup basically the loop speed of Python or the spi.max_speed_hz option?
  4. Am I converting the raw ADC values correctly using numpy interp?
  5. If the sensor has a max 10hz frequency then I need to double that for my sample rate?
  6. Should I be using Adafruit's adafruit-circuitpython-mcp3xxx package instead?

With the below code, I am able to see the values change when I light a flicker match but its a bit inconsistent and only changes when the flame is flickering and not when its still.

import spidev 
from numpy import interp
import time
import datetime
from datetime import timedelta

# Start SPI connection
spi = spidev.SpiDev() # Created an object

# Read MCP3008 data
def analogInput(channel, hz):
  spi.max_speed_hz = hz
  adc = spi.xfer2([1,(8+channel)<<4,0])
  data = ((adc[1]&3) << 8) + adc[2]
  return data

init_time = datetime.datetime.now()

while True:
    output1 = analogInput(0, 1000000)
    output1 = interp(output1, [0, 1023], [0, 100])
    if init_time < datetime.datetime.now() - timedelta(seconds=2):
        print("min" + str(min(data_list)))
        print("max" + str(max(data_list)))
        print("avg" + str(sum(data_list)/len(data_list)))
        init_time = datetime.datetime.now()

  • 1
    Comments are not for extended discussion; this conversation has been moved to chat.
    – goldilocks
    Mar 10 at 18:51


How to use Rpi python MCP3008 ADC to help analyse flame flicker signals?

pyreos schematic


  1. A py0573 bandpass filter is used for CO2 spectrum

  2. To determine if a "flame" exists on the FOV of the sensor, the vendor recommends a plot/detect FFT bins from the 5-15hz frequency.

Electrical Characteristics

  1. Max. Voltage (+V) = 8.0 V

  2. Min. Voltage (+V) = 2.7 V

  3. Output voltage normalised around mid-rail

  4. Time Constant = ~12 ms

  5. Op-Amp with 10 GΩ feedback resistor


Part 1 - Using MCP3008 to read raw data

First thing first is to prepare 1,024 flame sensor readings for FFT. (See Reference 6 and Appendix B below for more details.)

FFT Raw Data

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(1) Thin Film Pyroelectric Flame Sensor - PY-ITV-FLAME–TO39(2+1) R5.2 - Pyreos

(2) MCP3008 SPI 2.7V 8-Channel 10-Bit A/D Converters - MicroChip

(3) Rpi Reading MCP3008 10 bit ADC Result Problem - Rpi SE 2019may22

(4) Flame Detector - Wikipedia

(5) Tips To Select The Right Flame Detector - Edward Naranjo, Emerson Automation Solutions 2019mar06

(6) AN0125 Application Note: Sample Flame Detection Ratio Based Algorithm - Pyreos

(7) Pyreos Evaulation Kit

(8) MCP3008 10 Bit ADC Raspberry Pi Python Library 0.1a3 (pip install mcp3008) - PiPy.org

(9) MCP3008/MCP3201/MCP3208 10-bit/12-bit ADC Rpi Python Programming / Demo Program / Q&A - tlfong01, EESE 2020aug06

(10) A Review on Forest Fire Detection Techniques - Ahmad A. A. AlkhatibFirst Published March 4, 2014 Review Article

(11) 32/64/128/256/512/1024/2048/4096 Point FFT Core - IPCores


Appendix A - Pyroelectric Flame Sensor PY-ITV Datasheet Summary


The Pyreos thin film pyroelectric infrared flame detectors offer exceptionally high responsivity, a wide field of view of typically 100° (*subject to filter band pass specification) and class leading rapid recovery from thermal and electrical shocks (<1 second downtime). This current mode sensor has excellent signal to noise at the signature 8-10 Hz flicker range of a flame, and can provide accurate discrimination of flame sources in triple IR flame detection systems.

The sensor element is built into a low noise circuit that has an internal CMOS op amp with a 10GΩ feedback resistor outputting a voltage signal centred around half the supply rail.

Sensor Characteristics

Filter aperture = 5.2 mm x 4.2 mm

Element size = 1000 µm x 1000 µm

Package = TO39

Responsivity = 1 150,000 V/W

D* = 3.5 x 108 cm√Hz/ W

Noise = Mean 70 µV√Hz

Field of View = Typical 100°

Electrical Characteristics

Max. Voltage (+V) = 8.0 V

Min. Voltage (+V) = 2.7 V

Output voltage normalised around mid-rail

Time Constant = ~12 ms

Operating Temperature = -40 to +85 °C

Storage Temperature = -40 to +110 °C

Op-Amp with 10 GΩ feedback resistor

Filter As per Filters Available table

Appendix B - Package Info and Internal Schematic

package info and schematic

Appendix C - Example Raw Data for FFT

example raw data

Appendix D - Rigol DS1504Z 50MHz 1Gsp/s Scope 1KHz square wave signal FFT Test

fft test

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