PREDICTIVE MAINTENANCE: INTEGRATING EDGE AI WITH CLOUD COMPUTING FOR INDUSTRIAL IOT
Keywords:
Edge Computing, Predictive Maintenance, Industrial IoT, Cloud Integration, Machine LearningAbstract
The integration of edge computing with artificial intelligence and cloud synchronization has emerged as a transformative solution for implementing predictive maintenance systems in industrial environments. This article explores the architecture and implementation of a modern predictive maintenance solution that combines edge and cloud computing capabilities to address the critical challenges of equipment failures and unplanned downtime in manufacturing operations. By leveraging sophisticated edge AI algorithms for real-time data processing and analysis, combined with cloud-based advanced analytics, organizations can significantly reduce maintenance costs while improving overall equipment effectiveness. The article examines the challenges of traditional maintenance approaches, the role of edge computing as a first line of defense, cloud integration for advanced analytics, implementation architecture, best practices, and future developments in the field. The proposed framework substantially improves operational efficiency, maintenance cost reduction, and equipment lifetime prediction accuracy by strategically deploying edge computing resources and advanced AI capabilities.
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