Predictive Maintenance Vision AI 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.
It uses the 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 |
This blueprint is a proof of concept and is not intended for production use.