Edge AI-Based Autonomous Monitoring System for Smart Manufacturing Environments

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.