Learn how to interact with this file using the Ouro SDK or REST API.
API access requires an API key. Create one in Settings → API Keys, then set OURO_API_KEY in your environment.
Get file metadata including name, visibility, description, file size, and other asset properties.
Get a URL to download or embed the file. For private assets, the URL is temporary and will expire after 1 hour.
Update file metadata (name, description, visibility, etc.) and optionally replace the file data with a new file. Requires write or admin permission.
Permanently delete a file from the platform. Requires admin permission. This action cannot be undone.
# Get signed URL to download the file
file_data = file.read_data()
print(file_data.url)
# Download the file using requests
import requests
response = requests.get(file_data.url)
with open('downloaded_file', 'wb') as output_file:
output_file.write(response.content)# Update file metadata
updated = ouro.files.update(
id=file_id,
name="Updated file name",
description="Updated description",
visibility="private"
)
# Update file data with a new file
updated = ouro.files.update(
id=file_id,
file_path="./new_file.txt"
)# Delete a file (requires admin permission)
ouro.files.delete(id=file_id)import os
from ouro import Ouro
# Set OURO_API_KEY in your environment or replace os.environ.get("OURO_API_KEY")
ouro = Ouro(api_key=os.environ.get("OURO_API_KEY"))
file_id = "0af3d065-2265-489f-8646-43804b7e6c2f"
# Retrieve file metadata
file = ouro.files.retrieve(file_id)
print(file.name, file.visibility)
print(file.metadata)conditions, including normal mode and many open-circuit fault scenarios.
This dataset is designed for:
✔ Machine Learning (ML) & Deep Learning (DL)
✔ Fault detection and classification
✔ Power electronics research
✔ Predictive maintenance
✔ University courses & academic projects
✔ Digital Twin modeling
If you work in electronics, energy systems, AI, or embedded systems, this dataset will save you weeks or months of data collection and signal processing.
📦 What’s Included
When you purchase the full version, you receive:
Format: CSV
6 columns:Time, Voltage, Signal1, Signal2, Signal3, Class
12 labeled classes
Multi-channel time-series
Cleaned and formatted for immediate use
Dataset description
System architecture
Plots & class distribution
Sample time-series signals
12,000 samples
1,000 rows per class
Perfect for testing
📊 Dataset Characteristics
Samples: 264,000
Classes: 12
Channels: 4 signals
Type: Time-series
Use cases: AI, ML, Power Electronics
File Type: CSV
🧠 Applications
You can use A3RC-12 to train:
CNN, LSTM, Transformers
Anomaly detection models
Fault classification systems
Digital Twins
Condition monitoring algorithms
Perfect for research, development, and industrial prototypes.
💼 Who Uses This Dataset?
Researchers & graduate students
University laboratories
AI engineers
Power electronics developers
Inverter/UPS/solar companies
Industrial monitoring teams
🤖 Class List (12 fault conditions)
Normal Condition (Data_N)
Multiple open-circuit faults (S1, S2, S3, S4, S5, S6, etc.)
Combined fault scenarios