PHM-Agent: Autonomous Intelligent Agents for Industrial Maintenance

First Author*, Second Author*, Third Author
Institution Name
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*Indicates Equal Contribution
πŸ€– Autonomous Decision Making πŸ”„ Self-Learning πŸ‘₯ Multi-Agent Coordination ⚑ Real-time Response
Live Demo API Docs Get Started
Paper Supplementary arXiv

πŸ€– PHM-Agent in Action: Watch autonomous agents analyze sensor data, coordinate decisions, and execute maintenance actions in real-time across multiple industrial systems.

βœ“ Real-time Decision Making
βœ“ Multi-Agent Coordination
βœ“ Explainable Actions
βœ“ Continuous Learning

Autonomous Agents for Industrial Maintenance

We present PHM-Agent, a groundbreaking multi-agent system that brings autonomous intelligence to industrial maintenance and health management. Our agents operate independently and collaboratively to monitor equipment health, predict failures, and execute maintenance actions in real-time. Unlike traditional black-box approaches, PHM-Agent provides transparent decision-making through natural language explanations and maintains continuous learning capabilities to adapt to new industrial environments.

The system features distributed agents with specialized roles: Monitoring Agents for continuous sensor data analysis, Decision Agents for maintenance planning, and Coordination Agents for multi-system optimization. Each agent leverages advanced LLM reasoning while maintaining high-speed industrial operation requirements.

Why Autonomous Agents?

Industrial maintenance faces unprecedented challenges that require intelligent, autonomous solutions. Traditional approaches fall short in today's complex industrial environments:

  • Scale Complexity: Modern facilities have thousands of interconnected systems requiring simultaneous monitoring
  • Decision Speed: Critical failures can occur within minutes, demanding instant autonomous response
  • Knowledge Integration: Maintenance expertise is scattered across teams and needs to be systematically captured
  • Adaptive Learning: Systems must continuously evolve with new equipment, conditions, and failure patterns
  • Human Collaboration: Agents must work alongside human operators, providing clear explanations and reasoning

PHM-Agent addresses these challenges through intelligent autonomous agents that can reason, learn, communicate, and act independently while maintaining full transparency in their decision-making process.

Agent System Architecture

PHM-Agent employs a distributed multi-agent architecture where specialized autonomous agents collaborate to provide comprehensive industrial maintenance solutions. Each agent operates independently while participating in a coordinated ecosystem for optimal system-wide performance.

🧠

Perception Layer

Multi-modal sensor data processing, feature extraction, and real-time anomaly detection across industrial systems.

πŸ€”

Reasoning Engine

LLM-powered decision making with domain knowledge, causal inference, and explainable maintenance recommendations.

🎯

Action Module

Autonomous maintenance scheduling, work order generation, and direct system control with safety constraints.

πŸ“‘

Communication Hub

Inter-agent messaging, coordination protocols, and human-agent interface for collaborative decision making.

πŸ”„ Continuous Learning: All agents continuously adapt and improve through reinforcement learning, incorporating new operational patterns, failure modes, and maintenance strategies.

Datasets & Benchmarks

Dataset Description Size Task Metric
NASA C-MAPSS Turbofan engine degradation simulation 20,631 samples RUL Prediction RMSE, Score
PHM08 Challenge Bearing fault detection and classification 2,156 samples Fault Classification Accuracy, F1-Score
IMS Bearing Bearing vibration data for condition monitoring 984 samples Health Assessment AUC, Precision
FEMTO-ST Bearing accelerated life test data 17 complete runs RUL Prediction Ξ±-Ξ» Score

🏒 Agent Architecture & Components

Explore the sophisticated architecture powering autonomous PHM agents

Performance Metrics

RUL Prediction Results

Method RMSE Score MAE
PHM-LLM (Ours) 12.4 245.8 9.7
LSTM 16.8 338.4 13.2
CNN-LSTM 15.1 302.1 12.1
Transformer 14.2 281.6 11.5

Fault Classification Results

Method Accuracy F1-Score Precision
PHM-LLM (Ours) 94.3% 0.941 0.938
SVM 87.2% 0.869 0.871
Random Forest 89.6% 0.892 0.895
Deep CNN 91.8% 0.916 0.919

πŸš€ Try PHM-Agent Live

Experience autonomous agent decision-making in real-time

Select Scenario

Agent Ready
Response Time: -
Confidence: -
Action: Standby

Agent Console

[PHM-Agent v2.1] Initializing...
πŸ” Monitoring systems: 3 active agents
🧠 Reasoning engine: Ready
πŸ“‘ Communication hub: Online
⚑ Status: Waiting for data input...
Select a scenario above to begin live demonstration
How it works: Select an industrial scenario, then watch as our autonomous agents analyze sensor data, identify patterns, predict potential failures, and recommend specific maintenance actions β€” all in real-time with full explainability.

πŸŽ₯ Agent Development Tutorials

Master PHM-Agent development with our comprehensive tutorial series

Tutorial Series

  • 01
    Introduction to PHM-Agent
    12:34
  • 02
    Setting Up Your First Agent
    15:21
  • 03
    Training Custom PHM Agents
    18:45
  • 04
    Deploying Agents in Production
    20:10
  • 05
    Multi-Agent Coordination
    25:30

πŸ”§ Agent API Documentation

Build and integrate PHM-Agents into your systems

API Endpoints

πŸ€– Agent Management
πŸ“ˆ Data Processing
πŸ”§ Maintenance

POST /agents/create

Create a new PHM agent instance

Request Body

{
  "name": "TurbineMonitor-01",
  "type": "predictive_maintenance",
  "config": {
    "model": "phm-agent-v2",
    "sensors": ["vibration", "temperature", "pressure"],
    "threshold": 0.85,
    "update_frequency": "1min"
  },
  "location": "WindFarm-Site-A"
}

Response

{
  "agent_id": "agent_12345",
  "status": "active",
  "created_at": "2024-01-15T10:30:00Z",
  "endpoint": "https://api.phm-agent.com/agents/12345"
}

Quick Start

Python SDK

from phm_agent import Agent

# Initialize agent
agent = Agent(config='production')

# Start monitoring
results = agent.monitor(
    sensors=['temperature', 'vibration'],
    threshold=0.85
)

# Get recommendations
actions = agent.recommend_actions()

JavaScript SDK

const PHMAgent = require('phm-agent');

// Create agent instance
const agent = new PHMAgent({
  apiKey: 'your-api-key',
  endpoint: 'api.phm-agent.com'
});

// Start monitoring
await agent.startMonitoring({
  sensors: ['temp', 'vibration'],
  alertThreshold: 0.85
});

🏭 Agent Applications Across Industries

Real-world deployments showcasing autonomous agents in action

Poster

πŸ“ Cite Our Work

Multiple publications covering different aspects of PHM-Agent research

Main Framework Paper

@article{phm-agent-2024,
  title={PHM-Agent: Autonomous Intelligent Agents for Predictive Maintenance},
  author={Author, First and Author, Second and Author, Third},
  journal={Conference on Autonomous Agents and Multi-Agent Systems (AAMAS)},
  year={2024},
  pages={123--135},
  publisher={ACM},
  doi={10.1145/aamas2024.12345},
  url={https://doi.org/10.1145/aamas2024.12345}
}
View Paper

Citation Guidelines

Please cite the PHM-Agent Framework paper for general usage of our agent system. For specific aspects like multi-agent coordination or benchmark evaluation, please also cite the relevant specialized papers.