KURNIADI, LIEM FARREL (2024) MACHINE ANOMALY DETECTION USING INTERNET OF THINGS WITH MOVING AVERAGE METHOD. S1 thesis, UNIVERSITAS KATOLIK SOEGIJAPRANATA.

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Abstract

The engine is one of the important indicators in the development of the times. knowing the performance and preventing damage to the engine is very important to maintain the stability of the engine. Therefore, this study was made to monitor the engine using the LM35 temperature sensor, SW420 vibration sensor, and FC04 sound sensor to monitor the engine. which then the sensor results are processed using the moving average method, the data that has been smoothed is then given a threshold calculation as a reference in determining engine performance. With the 50 data window range the results of this study can find out and identify engine damage early on so that action can be taken and avoid more fatal engine damage in the future

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