Agentic Predictive Maintenance Pipeline Blueprint#
Blueprint Series — Edge AI Predictive Maintenance for Critical Infrastructure
Multi-Agent Reasoning + Edge Vision with Intel OpenVINO
This blueprint, built around a real pipeline defect dataset with six defect classes, demonstrates how edge vision AI and multi-agent reasoning with Intel OpenVINO can power a new generation of predictive maintenance workflows that go beyond simple detection into structured analysis, policy enforcement, and evidence-grade audit trails.
The current implementation demonstrates this on a pipeline defect detection use case, but the architecture is designed for domain portability — it can be extended to other inspection domains such as solar panel defect detection, bridge structural assessment, or manufacturing quality control with minimal changes to the configuration and prompt files. Similarly, this version uses images and video as input data, but the pipeline architecture is designed to accommodate additional sensor modalities in future iterations — radar, LiDAR, thermal imaging, and other Non-Destructive Testing (NDT) data sources — broadening the system’s applicability across industrial predictive maintenance scenarios.
The result is a three-unit architecture: a vision inference layer that detects defects in real time, a structured data layer that persists every detection in SQLite, and a multi-agent reasoning layer — coordinated by LangGraph — where specialized agents generate policy rules, filter and analyze detections, produce compliance audit trails, and render self-contained HTML tickets. All of it runs on a single Intel edge node.
For the full description and user guide, refer to the Predictive Maintenance Pipeline Blueprint Documentation, including a quick-start guide
The Dataset and Defect Classes#
The system is designed around industrial pipeline defect detection, with a dataset that exercises six distinct defect categories:
Defect Class |
Description |
|---|---|
Deformation |
Structural warping or bending of pipeline segments |
Obstacle |
Foreign objects or debris obstructing the pipeline path |
Rupture |
Breaks or tears in the pipeline wall — a critical, high-severity defect |
Disconnect |
Separation at joints or coupling points — a critical, high-severity defect |
Misalignment |
Positional offset between connected pipeline segments |
Deposition |
Material buildup (corrosion, sediment, biological growth) on surfaces |
Architecture: The Three-Unit Stack#
The architecture is organized into three cleanly separated units, from data acquisition to intelligent reasoning:
Data Flow#
