Harnessing Big Data Analytics for Predictive Maintenance: A Machine Learning Approach in Industrial IoT
Year: 2025
Type: article
Abstract: In today’s fast-evolving industrial landscape, minimizing equipment downtime and optimizing operational efficiency have become crucial to maintaining competitiveness. Predictive Maintenance (PdM), driven by Big Data Analytics and Machine Learning (ML), offers a proactive approach to address these needs. This paper explores how the integration of Industrial Internet of Things (IIoT) with Big Data and ML can significantly enhance predictive maintenance frameworks. Using real-time sensor data, ML algorithms can detect patterns, predict failures, and optimize maintenance schedules, thereby reducing unplanned downtime, enhancing safety, and saving costs. This study investigates common ML techniques used in PdM, outlines a case study in manufacturing, and analyzes the challenges and future directions in deploying these technologies at scale.
Author: Dr. Panchashree Das
Institution: Centurion University of Technology and Management
Language: English
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4409567554