AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
This paper presents a complete Internet of Things (IoT) based predictive maintenance system for real-time condition monitoring of three-phase induction motors that prevents production stoppages and avoiding premature motor replacement.
The Sudden equipment failures are common in industrial systems, which means more downtime and higher maintenance costs. A Smart AI-Integrated Predictive Maintenance and Condition Monitoring System is proposed to solve this problem. It will allow for real-time monitoring and early fault detection in industrial machines....
The fast changing nature of connected vehicles requires smart systems that can monitor health in real-time and predict failures. This paper introduces an IoT-based Smart Vehicle Monitoring and Predictive Maintenance System that involves onboard sensors, edge computing, and cloud analytics to ensure high reliability and...
Unplanned failures of induction motors impose serious operational and financial penalties on industrial facilities, yet the fault signatures that precede such failures are detectable well in advance through careful sensor instrumentation and data-driven analysis. This paper presents an end-to-end Internet-of-Things (Io...
Predictive maintenance (PdM) in Industrial Internet of Things (IIoT) environments plays a vital role in minimizing unplanned downtime, improving operational efficiency, and spreading equipment lifespan. This paper presents a Machine Learning (ML)-based predictive maintenance basis deployed on Google Cloud AI Platform f...
An Industrial Internet of Things (IIoT)-based Predictive Maintenance System that integrates smart sensors, edge computing, cloud analytics, artificial intelligence, and digital twin technology is proposed that contributes to the development of intelligent and self-optimizing industrial environments aligned with Industr...
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