README.md

metadata

title: Predictive Agent
emoji: 🔧
colorFrom: green
colorTo: blue
sdk: gradio
sdk_version: 5.9.1
python_version: '3.11'
app_file: app.py
pinned: true
license: mit
short_description: LSTM-Based RUL Prediction
tags:
  - predictive-maintenance
  - lstm
  - rul
  - ccgt
  - industrial-ai

Predictive Agent

LSTM-Based RUL Prediction for CCGT Equipment

Predict Remaining Useful Life for Combined Cycle Gas Turbine equipment to optimize maintenance scheduling.

Quick Start

1. Click "Demo: Degraded Equipment" to see RUL prediction for failing equipment
2. Click "Demo: Healthy Equipment" to see prediction for healthy baseline
3. Or enter your own equipment health data

Key Metrics

Metric	Description	Good	Warning
Health Index	Overall condition score	>70%	40-70%
Vibration	Mechanical condition	<0.3 in/s	0.3-0.5 in/s
Heat Rate Delta	Thermal efficiency loss	<4%	4-8%
Operating Hours	Time since overhaul	<50k	50-65k
Start Count	Thermal cycles	<1000	1000-1200

How It Works

Health Metrics -> Sequence Generation -> LSTM Inference -> RUL Estimate -> Maintenance Plan

Resources

• Model: rul-predictor-ccgt
• Dataset: ccgt-health-history
• GitHub: predictive-agent
• Portfolio: davidfernandez.dev

Author

David Fernandez - Industrial AI Engineer | LangGraph Contributor

• LinkedIn
• GitHub
• HuggingFace