Real-Time AI for Predictive Maintenance in Smart Factories

Authors: Ravi Kumar Perumallapalli

Country: USA

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Abstract: Artificial intelligence (AI)-powered real-time predictive maintenance systems are essential for reducing operational downtime and maximizing equipment performance in smart factories. In order to facilitate effective defect identification and equipment monitoring, this study focuses on sophisticated data processing techniques that are used in the integration of real-time AI technology with predictive maintenance tactics. Predictive maintenance becomes more reliable in manufacturing contexts by utilizing RFID-enabled systems for real-time scheduling and decision-making and by incorporating complex scheduling algorithms. In order to improve anomaly identification skills, the study also discusses the application of both static and dynamic novelty detection techniques, such as those used in jet engine health monitoring. Furthermore, the combination of intelligent decision-making systems with green ubiquitous computing for energy management in smart grids gives a new level of sustainability to factory operations. Lastly, the potential to increase data accuracy in fault prediction is highlighted for real-time filtering strategies for non-stationary signals, such as the Intrinsic Time-Scale Decomposition method presented. AI-powered predictive maintenance has the potential to greatly increase sustainability and efficiency in smart manufacturing environments because to these technical developments.

Keywords: Predictive Maintenance, Smart Factories, Real-Time AI, Equipment Failure Prediction, Data Fusion, Anomaly Detection, Edge Computing

Paper Id: 231504

Published On: 2013-09-03

Published In: Volume 1, Issue 1, September-October 2013

Cite This: Real-Time AI for Predictive Maintenance in Smart Factories - Ravi Kumar Perumallapalli - IJIRMPS Volume 1, Issue 1, September-October 2013.

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