Exploring the Use of Cloud-Based AI and ML for Real-Time Anomaly Detection and Predictive Maintenance in Industrial IoT Systems
Keywords:
Industrial Internet of Things (IIoT), Cloud Computing, Artificial Intelligence, Machine Learning, Anomaly Detection, Predictive Maintenance, Edge Computing, Big Data, Digital TwinAbstract
This comprehensive research paper delves into the application of cloud-based Artificial Intelligence (AI) and Machine Learning (ML) technologies for real-time anomaly detection and predictive maintenance in Industrial Internet of Things (IIoT) systems. The study provides an in-depth analysis of IIoT architecture, the integration of cloud computing with AI/ML techniques, and the challenges associated with implementing these technologies in industrial environments. Through extensive examination of various aspects including anomaly detection methodologies, predictive maintenance strategies, data management techniques, and model development approaches, this paper offers valuable insights into the current state and future potential of cloud-based AI/ML solutions in industrial settings. The research findings underscore the significant benefits of these technologies in enhancing operational efficiency and reliability, while also highlighting the importance of addressing implementation challenges and adapting to emerging trends in the rapidly evolving field of industrial IoT.
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