Machine learning and IoT – Based predictive maintenance approach for industrial applications

Abstract

Unplanned outage in industry due to machine failures can lead to significant production losses and increased maintenance costs. Predictive maintenance methods use the data collected from IoT-enabled devices installed in working machines to detect incipient faults and prevent major failures. In this study, a predictive maintenance system based on machine learning algorithms, specifically AdaBoost, is presented to classify different types of machines stops in real-time with application in knitting machines. The data collected from the machines include machine speeds and steps, which were pre-processed and fed into the machine learning model to classify six types of machines stops: gate stop, feeder stop, needle stop, completed roll stop, idle stop, and lycra stop. The model is trained and optimized using a combination of hyperparameter tuning and cross-validation techniques to achieve an accuracy of 92% on the test set. The results demonstrate the potential of the proposed system to accurately classify machine stops and enable timely maintenance actions; thereby, improving the overall efficiency and productivity of the textile industry.