Predictive Analytics for Proactive Maintenance in Industrial IoT Applications
A. Jaya Mabel Rani, B. Yasotha, P Sevanthi, K Karthika, Y. Alexander Jeevanantham, V. Samuthira Pandi
Abstract
Industrial Internet of Things (IIoT) enabled predictive analytics have enabled the manufacturing sector to transition from reactive to proactive maintenance. This article discusses the use of predictive analytics in the IIoT field of preventive maintenance. The objectives consist of increased output, availability, and decreased malfunctions-research on preventive maintenance cast light on its evolution. The proposed work presents a framework for IIoT data acquisition, cleansing, feature selection, and analytical processing. Experiments and case studies validate the methodology. Utilizing predictive analytics for routine maintenance can improve the effectiveness and productivity of machinery. Data integrity, model consistency, and scalability must be resolved before implementation. The proposed work's findings and recommendations have significant implications for future research into predictive analytics for proactive maintenance in IIoT systems and for industry practitioners.