AGENTIC AI PREDICTIVE MAINTAINANCE IN INDUSTRY 4.0

Abstract

The Agentic AI for Predictive Maintenance in Industry 4.0 project is an advanced industrial intelligence system that combines autonomous Artificial Intelligence, multiagent architectures, machine learning, and Industrial Internet of Things (IIoT) technologies to predict, diagnose, and prevent equipment failures proactively in modern smart manufacturing environments. In Industry 4.0 ecosystems, industrial machines continuously generate large volumes of sensor data related to temperature, vibration, pressure, and operational conditions. Traditional maintenance approaches often fail to detect hidden faults early, leading to unplanned downtime, increased operational costs, and reduced productivity. This project addresses these challenges by implementing an intelligent agentic AI framework capable of performing real-time predictive maintenance and autonomous maintenance decision-making. The proposed system integrates a Large Language Model (LLM)-powered orchestration layer with advanced time-series anomaly detection and Remaining Useful Life (RUL) prediction models such as Temporal Convolutional Networks (TCN) and Isolation Forest algorithms. The implementation is evaluated using the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset, which contains operational and failure simulation data from more than 100 turbofan engine units. The multi-agent architecture consists of specialized intelligent agents including a Sensor Agent, Diagnosis Agent, Scheduler Agent, and Report Agent, each responsible for different stages of industrial monitoring, anomaly detection, maintenance scheduling, and automated reporting. The system achieved strong predictive maintenance performance with a Remaining Useful Life (RUL) prediction RMSE of 12.4 cycles on the CMAPSS FD001 dataset, exceeding the target benchmark of 15 cycles. The anomaly detection module achieved an F1-score of 0.94 with 96.2% precision in identifying unseen equipment failure modes. In addition, the autonomous maintenance scheduling system demonstrated 91.3% scheduling accuracy aligned with actual failure windows, while maintaining low-latency decision-making with approximately 340 milliseconds per sensor cycle on edge hardware devices.