Equipment Sound Analysis

Take a simple USB microphone, feed the sounds of plantroom equipment into machine learning (Tensorflow) to identify problems at an early stage.

Many equipment failures are preceded by some form of mechanical change that can be detected by the sound they make.

Equipment starts, such as boiler firing sequences, follow patterns that can be listened to and an alarm raised if something sounds different to normal.

This is mainly a task of training. As a machine is fed more and more examples, and trained on what sounds are, it becomes better at detecting the problems.

Sounds are fairly easy to analyse when broken down into a series of frequency bands, just as on any stereo graphic equaliser.

High frequencies often indicate metal-metal contact in rotating equipment such as pumps.

Flow rates through pipes generate noise relative to velocity (flow rate).

Microphones are cheap. https://www.amazon.co.uk/s?k=USB+directional+Microphone

• https://github.com/bartbutenaers/node-red-contrib-audio-analyser
• https://pimylifeup.com/raspberrypi-microphone/
• https://flows.nodered.org/node/node-red-contrib-mic
• https://developer.mozilla.org/en-US/docs/Web/API/Web_Audio_API
• https://makersportal.com/blog/2019/2/26/raspberry-pi-vibration-analysis-experiment-with-free-free-bar
• https://stackoverflow.com/questions/36250228/read-frequency-from-mic-on-raspberry-pi

Test1

sudo apt-get install python3-pip