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Gangadhar123/CNC_Predictive_Maintenance1

sourceHugging Faceupdated 1y agoView on Hugging Face

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App README

🛠️ CNC Test Data Generator

This Hugging Face Space generates synthetic CNC machine sensor and quality control data for AI development and testing. The data simulates real-world CNC machine scenarios including normal operation, tool wear, groove defects, overheating, high vibration, and more.

📦 Features

—Simulates sensor values, quality metrics, and machine metadata
—Includes both normal and faulty machine behavior
—Covers multiple machine types, fault types, and component types
—Generates labeled data for anomaly detection, RUL estimation, and quality scoring
—One-click CSV download of the generated dataset

📋 Sample Data Columns

Column Name	Description
machine_id	Unique machine identifier
vibration	Machine vibration (g)
spindle_speed	Spindle speed (RPM)
temperature	Machine temperature (°C)
groovedepthmm	Groove depth of part (mm)
surfacefinishmicrons	Surface finish (microns)
tool_wear	Tool wear factor (0-1.5)
anomaly_score	Anomaly score (0-1)
is_anomaly	Binary anomaly flag (0/1)
truerulhours	True Remaining Useful Life (hours)
rul_predicted	Predicted RUL by model (hours)
quality_score	Binary quality score (0 = fail)
fault_type	Description of fault (if any)

🎮 How to Use

1.Click the "Generate Test Data" button.
2.Wait a few seconds for the data to be generated.
3.Click "Download CSV" to get the dataset to your local machine.

⚙️ Use Cases

—Prototype AI models for predictive maintenance
—Train anomaly detection models (e.g. GRU autoencoder)
—Build RUL regression models using XGBoost or LSTM
—Train quality classifiers for CNC-produced parts

📁 Output Location

—The downloaded file is named cnc_test_data.csv.

🤖 Author & Maintainer

Built by [Your Name or Team Name]

—🔗 [Your Website or GitHub]
—📧 [Your Contact Email]

💡 Tip

You can fork and modify this Space to adjust:

—The number of samples generated
—Add noise injection
—Simulate more fault types or machine behaviors