Ai Driven Anomaly Detection in Smart Manufacturing / Industry?4.0 Using Distributed Parameter Models for Enhanced Process Monitoring

Abstract

Smart manufacturing and Industry?4.0 have transformed traditional industrial operations through digitization, interconnectivity, and intelligent automation. Among various enabling technologies, artificial intelligence (AI) plays a pivotal role in improving operational efficiency and predictive maintenance. Anomaly detection, an essential part of industrial monitoring, ensures that unexpected deviations in production processes are identified early, reducing downtime and improving safety. This paper explores AI-driven anomaly detection in smart manufacturing using Distributed Parameter Models (DPMs), which allow modeling of spatially and temporally distributed processes in large-scale industrial systems. The study focuses on methodologies for implementing AIenhanced DPMs, examines challenges in real-world deployment, and highlights future directions in Industry?4.0 applications.

KEYWORDS: Smart manufacturing, Industry?4.0, anomaly detection, distributed parameter models, artificial intelligence, process monitoring, predictive maintenance, cyber-physical systems.