luviner/industrial-faults

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 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

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:

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.

Citation

@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.