Automating Predictive Maintenance for Energy Efficiency via Machine Learning and IoT Sensors

Abstract

The arise of maintenance issues in mechanical systems is cause for decreased energy efficiency and higher operating costs for many small-to medium-sized businesses. The sooner such issues can be identified and addressed, the greater the energy savings. We have designed and implemented an automated predictive maintenance system that uses machine learning models to predict maintenance needs from data collected via data sensors attached to mechanical systems. As a proof of concept, we demonstrate the effectiveness of the system by predicting several operating states for a standard clothes dryer.