Real-Time Anomaly Detection in Industrial Processes: Leveraging GPU-Accelerated Machine Learning and AI Robotics for Predictive Maintenance and Process Optimization

EasyChair Preprint 14385

Abstract

In the evolving landscape of Industry 4.0, real-time anomaly detection in industrial processes is crucial for maintaining operational efficiency, reducing downtime, and preventing costly failures. This paper explores the integration of GPU-accelerated machine learning and AI-driven robotics to enhance predictive maintenance and process optimization. By leveraging the computational power of GPUs, complex machine learning models can be trained and deployed rapidly, enabling the detection of subtle anomalies in vast streams of industrial data in real-time. AI robotics further enhances this framework by providing adaptive control and autonomous decision-making, ensuring that any detected anomalies are addressed promptly and efficiently. The proposed approach not only improves the reliability and performance of industrial systems but also reduces maintenance costs by predicting failures before they occur. This study demonstrates the potential of combining advanced computational techniques with intelligent robotics to create a robust and scalable solution for real-time anomaly detection in diverse industrial environments.

Keyphrases: AI Robotics, GPU-accelerated, machine learning

@booklet{EasyChair:14385,
  author    = {Abey Litty},
  title     = {Real-Time Anomaly Detection in Industrial Processes: Leveraging GPU-Accelerated Machine Learning and AI Robotics for Predictive Maintenance and Process Optimization},
  howpublished = {EasyChair Preprint 14385},
  year      = {EasyChair, 2024}}