BIG DATA ANALYTICS FOR PREDICTIVE MAINTENANCE IN INDUSTRIAL IOT SYSTEMS

Abstract

The advent of largescale Industrial Internet of Things (IIoT) deployments has generated unprecedented volumes of sensor and processdata streams, enabling the shift from reactive maintenance to datadriven predictive maintenance (PdM). This article investigates how big data analytics—spanning edge/cloud systems, streaming frameworks, featureengineering, machine learning and deep learning—can be applied to industrial assets for failure prediction and uptime optimisation. We map out the core architectural components, review analytics techniques and industrial usecases, present two illustrative graphs highlighting datavolume/accuracy and downtime/cost savings tradeoffs, and finally summarise the major deployment challenges and future research directions. The findings suggest that when implemented with proper data architecture and analytics workflows, big dataenabled PdM can significantly reduce unplanned downtime, extend asset lifecycles and realise substantial costsavings in industrial settings.