An Efficient Predictive Alimentary System using Machine Learning on the edge

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Abstract
The industries and factories that produce goods which are always in demand require good amount of productivity, with small delays even can result in supply crunches. Most of the factories do not have smart machinery which can predict the breakdowns of the machines used and save time for better productivity because of the cost they come with. With a Predictive Maintenance system using machine learning on the edge this can save both time and cost of the factories and at the same time give better and quick results compared to cloud systems. In this paper we discuss on how the machine learning models can fit in a micro controller and run inference on it and predict results without requiring it to connect to a cloud system. We have emulated a washing machine to be a factory equipment and ran the system in it with its monitored data for predictions.

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