Wavelet based Bearing Fault Prognosis using Machine Learning in Cloud Platform

Today's technological breakthroughs are tied closely to intelligence and cloud platforms, ensuring that everything in the industry is connected quickly and without interruption. The proposed system implements industrial condition monitoring, which monitors motor parameters and uses machine learning algorithms on a cloud analytics platform to estimate future parameter values of motors. The time and frequency charac-teristics in wavelet transform are extracted for detailed motor condition diagnosis. Wavelet transform features provide a vital prognosis for early motor conditions in the cloud algorithm, interlinked with Random Forest(RF) for the best-prediction algorithm model. The Google Cloud Platform(GCP) offers the best cloud computing services with more google online features.