Intelligent Architecture for Electric Motors: IIoT and Machine Learning for Advanced Data Acquisition and Analysis.

Abstract

There is a growing demand in various industries for the collection of variables related to the conditions of production line equipment, such as electric motors. This demand has increased due to the rise of Industry 4.0 and the digital transformation that companies are deploying. Understanding that a typical plant has between 6,000 to 12,000 pieces of equipment, selecting critical equipment to assign an investment in the installation and start-up of sensors that measure operating conditions is both an operational and investment challenge. This is where IIoT (Industrial Internet of Things) technologies become relevant, as they allow for cost mitigation by not using wiring for data collection, as well as for a faster and more flexible deployment. The next challenge is how to monitor, process, visualize, and analyze the large volume of data (Big Data) that is generated. Therefore, this work proposes an architecture that addresses these challenges, as well as a methodology that can be used for the integration of these projects, and how every day the industry demands more application of Machine Learning techniques.