Critical Infrastructure Predictive Maintenance Pipeline Blueprint

GitHub Vision AI Pipeline Guide Multimodal Pipeline Guide

Blueprint Series - Edge AI Predictive Maintenance for Critical Infrastructure
Edge Vision + Multi-Agent Reasoning with Intel OpenVINO

The Predictive Maintenance Pipeline is a complete agentic AI system for monitoring critical infrastructure, such as pipelines, bridges, and solar panel installations. It demonstrates the use of Intel hardware in the context of Edge AI, implementing it in specific use cases:

• Vision AI Predictive Maintenance for Pipeline monitoring

• Multimodal Predictive Maintenance for Gas Detection

The architecture is designed for domain portability and applicability across industrial predictive maintenance scenarios. With minimal changes to the configuration and prompt files, it can be extended to other inspection domains such as solar panel defect detection, bridge structural assessment, or manufacturing quality control.

Similarly, it can accommodate additional number and type of sensor modalities, such as radar, LiDAR, thermal imaging, and other Non-Destructive Testing (NDT) data sources.

At its core is a three-unit architecture and deployment model:

1. Unit 1 - Inference and ingestion

   • AI models run on Intel hardware through OpenVINO

   • Input data is processed and converted into structured prediction records

2. Unit 2 - SQLite persistence layer

   • Predictions are stored in a queryable local database

   • The database acts as the contract between inference and reasoning

3. Unit 3 - Multi-agent reasoning (LangGraph)

   • A Meta-Agent coordinates four worker agents:

     • Policy Agent

     • Analysis Agent

     • Evidence Agent

     • Ticketing Agent

Pipeline Components

• Video/Image ingest: DL Streamer (GStreamer-based) processes live streams and image batches.

• Model inference: YOLOv8 models optimized with OpenVINO Toolkit run on Intel CPU, iGPU, and NPU targets.

• Data persistence: SQLite stores structured detection/classification records for downstream reasoning.

• Agent orchestration: LangGraph coordinates Meta-Agent flow across Policy, Analysis, Evidence, and Ticketing agents.

• LLM reasoning: OpenVINO GenAI local models, remote server mode, or deterministic fallback templates.

• Prompt and configuration layer: domain prompts and YAML/JSON configs control use case behavior without core code changes.

• Interfaces and outputs: CLI and web UI drive execution; each run produces policy, analysis, evidence, and ticket artifacts.

Source Documentation

• Vision AI pipeline source: predictive_maintenance_pipeline.md

• Multimodal source: multimodal_predictive_maintenance_pipeline.md

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This blueprint is a proof of concept and is not intended for production use.