Revolutionizing Industrial Efficiency using the Predictive Maintenance Empowered by IoT Technologies

Abstract

In the landscape of modern manufacturing, the adoption of Artificial Intelligence combined IoT technologies has served as a catalyst for change, revolutionizing traditional practices and ushering in the era of Industry 5.0. Accordingly, this study examines the transformative capacity of IoT and AI-driven methodologies, with a specific emphasis on Machine Learning algorithms, Natural Language Processing, and Predictive Analytics, to enhance Predictive maintenance through quality assurance in manufacturing plants. The algorithm showed perfect performance over experiment trials. The average accuracy was determined as 95.1%, precision as 93.8%, and F1 score as 94.5%. This is an empirical confirmation that Machine Learning can be successfully used in the production and business processes for the defect identification, maintenance prediction, and production process optimization. This also results in the product quality increase as well as the low wastes rate during the production process. NLP techniques demonstrated significantly promising results: the average accuracy was calculated as 88.9%, precision as 87.3%, and F1 score as 87.9%. NLP allows manufacturers to analyze text sources and get a deeper understanding of Predictive maintenance through quality assurance and problem-solving approaches to customers’ complaints. The Predictive Analytics models gave good results, since the average accuracy in the models was 79.8%, precision was 77.5%, whereas the F1-score was 78.6%. Predictive Analytics supports forecasting to avoid problems with quality and repair. Thus, decision-making is possible before problems with downtimes occur.