Nearly every manufacturer uses a machine at some point in their process, and each of those machines is almost guaranteed to contain at least one motor. In order to maintain uptime and efficiency, these motors must always work correctly, as even a small breakdown can lead to disastrous effects. Predictive maintenance aims to achieve this goal while also not going overboard in trying to prevent them entirely by combining sensors with predictive techniques that can schedule maintenance when a failure is probable.
Shebin Jose Jacob’s solution utilizes the Arduino Nano 33 BLE Sense, along with its built-in microphone, to capture audio and predict when a motor is about to fail. He achieved this by first creating a new Edge Impulse project and gathering samples for four classes of sound: OK, anomaly 1, and anomaly 2, as well as general background noise. After designing an impulse and training a classification model on the samples, he was able to achieve an impressive accuracy of about 95% on the test samples.
The final step involved deploying the model as firmware for the Arduino, which would allow it to classify sounds in real-time by continuously reading from the microphone. Whenever an anomaly is detected, a red LED at the top illuminates.
You can read more about the project here on its Edge Impulse tutorial.
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