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🏭 Manufacturing🏗️ 4 Tech Levels🚀 Production-Grade

Predictive Maintenance AI

See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent

May 2, 2025
demointeractivepredictive-maintenancemanufacturingmulti-agent
🤖Demo 1 of 4

Advanced Multi-Agent System

BOFU: Production LangGraph with Router → Specialized Agents → Aggregation

1. Select Analysis Scenario

CNC Machine #47 shows elevated vibration (2.8mm/s, normal: 1.2mm/s) + temperature increase (78°C, normal: 65°C). System predicts bearing failure in 11 days.
Competitors: CNC Machine #47 (Bearing Assembly), Historical Failure DB (10,247 bearing failures), Parts Inventory (Bearing SKU: BR-2847)

2. Monday's Prompt

This positioning analysis prompt from Monday's insight will be processed by Tuesday's agents in Wednesday's workflow.

3. Run Multi-Agent Workflow

You'll see 5 AI agents: Data Collector → Messaging Analyzer → Position Mapper → Strategy Advisor → Insight Generator
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🔌Demo 2 of 4

MCP Tool Use Pattern

TOFU: LLM calls IoT sensor tools via Model Context Protocol - see real sensor data

Automation Task

User query → LLM decides to call get_sensor_data(motor_23, all_sensors) → Executes → Returns real-time readings → LLM interprets health status

Monitoring:Motor #23 (Conveyor Drive)
ai-agent-terminal

Terminal ready. Run simulation to begin.

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🔍Demo 3 of 4

RAG-Based Maintenance Intelligence

MOFU: Search 1000+ maintenance logs with vector similarity

Search Query

"Diagnose Bearing Failure from 1000+ Historical Cases"

Embed query → Search vectors (cosine similarity > 0.75) → Retrieve top 5 chunks (bearing failure cases) → Generate diagnosis with citations

Document Sources:Maintenance Log #2847 (2023-08-14): CNC bearing failureWork Order #4721 (2022-11-03): Spindle bearing replacementFailure Report #9012 (2024-01-22): Bearing inner race spallingTechnician Notes #3401 (2023-05-17): Progressive bearing wear pattern

RAG Vector Search Network

Embedding
Searching
Generating
Query
Vector
Documents
Result

Search Results

Run visualization to see results

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🛠️Demo 4 of 4

Multi-Tool Predictive Maintenance

MOFU: Multiple sensor tools working together 24/7

ROI Analysis

Orchestrator calls: SensorMonitorTool(100_machines) → PatternAnalyzerTool(5000_readings) → MaintenanceSchedulerTool(flagged_machines) → AlertTool(stakeholders) - all in parallel

Integrations:CNC Machine #47 (bearing wear detected)Pump #8 (contamination pattern)Conveyor #12 (belt misalignment)Press #23 (hydraulic pressure drop)Robot Arm #5 (servo motor overheating)
Manual Process

Time to Complete

240 min

Cost per Analysis

$850

Data Points Analyzed

150

Accuracy Rate

73.0%

Reports per Day

2

Error Rate

18.0%

Ready for Production Predictive Maintenance System?

We'll build your custom system: MCP tools (TOFU - try sensor access) → RAG search (MOFU - search 10K+ logs) → Multi-tool (MOFU - daily automation) → Multi-agent (BOFU - 24/7 autonomous monitoring). From demos to deployment in 8-12 weeks.

Book Architecture ReviewStart with MCP Tools (TOFU)
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