Edge AI-Based Autonomous Monitoring System for Smart Manufacturing Environments

  • Authors

    • Narendra Karmarkar Mathematician and Computer Scientist, Tata Institute of Fundamental Research, India Author

    DOI:

    https://doi.org/10.67228/30716357/IJMRSE-2024PII6C3X

    Published 08-05-2024

  • Edge Artificial Intelligence, Smart Manufacturing, Industry 4.0, Industrial Internet Of Things (Iiot), Autonomous Monitoring, Predictive Maintenance, Edge Computing, Deep Learning, Cyber-Physical Systems, Industrial Automation

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    Edge AI-Based Autonomous Monitoring System for Smart Manufacturing Environments. (2024). International Journal of Modern Research in Science & Engineering, 7(2), 01-14. https://doi.org/10.67228/30716357/IJMRSE-2024PII6C3X

  • Abstract

    The rapid evolution of Industry 4.0 has accelerated the adoption of intelligent manufacturing systems requiring real-time monitoring, predictive maintenance, and autonomous decision-making. Traditional cloud-based solutions often suffer from latency, bandwidth limitations, and data privacy concerns, making them unsuitable for time-critical industrial applications. This paper presents an Edge AI-based autonomous monitoring framework that integrates Industrial Internet of Things (IIoT) sensors, edge computing, deep learning models, and cloud platforms for efficient industrial monitoring. Real-time sensor data, including temperature, vibration, pressure, humidity, and power consumption, are processed locally using Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and anomaly detection algorithms to identify equipment faults and optimize operations. Only summarized insights are transmitted to the cloud, reducing communication overhead while enabling scalable enterprise-level analytics. The proposed framework improves prediction accuracy, minimizes downtime, enhances product quality, strengthens cybersecurity, and supports sustainable manufacturing. It provides a scalable and resilient solution for smart factories across industries, enabling intelligent automation, predictive analytics, and efficient autonomous industrial operations.

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