luviner/industrial-faults

---
license: cc-by-4.0
task_categories:
- tabular-classification
- time-series-classification
tags:
- anomaly-detection
- predictive-maintenance
- synthetic-data
- industrial
- fault-detection
- bearing-fault
- time-series
pretty_name: Luviner Industrial Fault Dataset
size_categories:
- 1K<n<10K
language:
- en
---

# Luviner Industrial Fault Dataset

**6,500 labeled samples** across **13 classes** (1 normal + 12 industrial fault types) with 8 sensor features.

Generated by the [Luviner AI](https://luviner.com) synthetic anomaly engine — 12 parametric failure mode generators that produce temporally progressive, physically realistic fault signatures.

## Dataset Description

### Why synthetic industrial data?

Real industrial failure data is extremely scarce — machines rarely fail, and when they do, the data is often proprietary. This dataset provides realistic labeled fault data for:

- **Training** anomaly detectors and fault classifiers
- **Benchmarking** time-series classification models
- **Education** on industrial fault signatures
- **Prototyping** predictive maintenance systems

### Features (8 sensor channels)

| Feature | Description |
|---------|-------------|
| `vibration_x` | Vibration amplitude, X-axis |
| `vibration_y` | Vibration amplitude, Y-axis |
| `vibration_z` | Vibration amplitude, Z-axis |
| `temperature` | Surface temperature |
| `pressure` | System pressure |
| `current` | Motor current draw |
| `flow_rate` | Process flow rate |
| `acoustic_db` | Acoustic emission level |

### Classes (13)

| Label | Fault Type | Category |
|-------|-----------|----------|
| 0 | normal | — |
| 1 | bearing_degradation | mechanical |
| 2 | shaft_imbalance | mechanical |
| 3 | gear_tooth_crack | mechanical |
| 4 | misalignment | mechanical |
| 5 | thermal_runaway | thermal |
| 6 | thermal_cycling | thermal |
| 7 | electrical_fault | electrical |
| 8 | sensor_drift | electrical |
| 9 | intermittent_contact | electrical |
| 10 | cavitation | process |
| 11 | blockage | process |
| 12 | leakage | process |

### Temporal progression

Each fault type has **temporally progressive** characteristics — faults evolve from subtle to severe over the sample sequence. This is realistic: industrial failures don't appear from nothing, they develop over time.

## Files

| File | Samples | Description |
|------|---------|-------------|
| `luviner_industrial_faults.csv` | 6,500 | Main dataset (500/class, severity 1.0) |
| `luviner_faults_mild.csv` | 2,600 | Mild faults (200/class, severity 0.5) |
| `luviner_faults_moderate.csv` | 2,600 | Moderate faults (200/class, severity 1.5) |
| `luviner_faults_severe.csv` | 2,600 | Severe faults (200/class, severity 2.0) |

## Quick Start

```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report

df = pd.read_csv("luviner_industrial_faults.csv")
X = df.drop(columns=["label", "fault_type"])
y = df["label"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
clf = RandomForestClassifier(n_estimators=100, random_state=42)
clf.fit(X_train, y_train)
print(classification_report(y_test, y_test, target_names=df["fault_type"].unique()))
```

## Generate your own

Want more data, different features, or custom failure modes? Use the [Luviner API](https://luviner.com/en/pricing/api):

```bash
curl -X POST https://luviner.com/api/v1/synthetic/generate \
-H "Authorization: Bearer YOUR_TOKEN" \
-H "Content-Type: application/json" \
-d '{"failure_mode": "bearing_degradation", "n_features": 8, "n_samples": 1000, "severity": 1.5}'
```

Also available on [RapidAPI](https://rapidapi.com/luviner/api/luviner).

## Citation

```bibtex
@dataset{luviner_industrial_faults_2026,
title={Luviner Industrial Fault Dataset},
author={Luviner Edge AI},
year={2026},
url={https://huggingface.co/datasets/luviner/industrial-faults},
note={Synthetic industrial failure data from 12 parametric fault generators}
}
```

## License

CC-BY-4.0 — free to use, share, and adapt with attribution.