Abstract:
Our project focuses on implementing a comprehensive solution to reduce the risk of major
breakdowns in electric motors caused by bearing faults. At its core, we developed an innovative
edge cog device that uses vibration sensors for early prediction of different bearing
faults in motors. The collected vibration signal data can give us a better knowledge of
the various faults happening in mechanical systems. In this work, we analyze vibration
signal data by integrating several signal processing methods and connecting them with
machine learning approaches to categorize various types of bearing failures, therefore
avoiding considerable downtime and costly repairs. The procedure begins with these sensors
gathering real-time vibration data, which is then analyzed at the edge node using
various signal processing algorithms to detect bearing faults. This data is then transferred
to the cloud, where a deep learning neural network made up of 1-D convolution, pooling,
and dense layers, and trained on a variety of datasets containing both healthy and defective
bearings is examined to precisely determine the fault type. Furthermore, we provide
a user-friendly dashboard that enables real-time monitoring and notifications, allowing
maintenance teams to resolve any irregularities as soon as they are noticed. By combining
these advanced techniques, our edge device not only prevents catastrophic failures but
also promotes timely maintenance plans and operational efficiency in general.
