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Petsteb/industrial-sensor-anomaly-data

Industrial Equipment Sensor Anomaly Data Overview Synthetic multivariate sensor data from a simulated manufacturing plant with 5 equipment units (EQ-001 through EQ-005). Each unit generates 10,000 one-minute-interval readings across 11 sensor channels, 2 metadata fields, 3 derived features, and equipment operating mode labels. The dataset is designed for anomaly detection benchmarking. It embeds 4 distinct anomaly types at approximately 4.5% prevalence: Thermal… See the full description on the dataset page: https://huggingface.co/datasets/Petsteb/industrial-sensor-anomaly-data.

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Dataset Card

Industrial Equipment Sensor Anomaly Data

Overview

Synthetic multivariate sensor data from a simulated manufacturing plant with 5 equipment units (EQ-001 through EQ-005). Each unit generates 10,000 one-minute-interval readings across 11 sensor channels, 2 metadata fields, 3 derived features, and equipment operating mode labels.

The dataset is designed for anomaly detection benchmarking. It embeds 4 distinct anomaly types at approximately 4.5% prevalence:

—Thermal runaway — gradual temperature escalation over 15–40 minutes with correlated coolant lag and viscosity drop
—Bearing degradation — vibration spikes with high-frequency noise and proportional acoustic increase
—Pressure leak — slow pressure decay over 20–60 minutes with compensatory flow rate increase
—Sensor malfunction — erratic single-sensor readings that do NOT reflect actual equipment failure (only one sensor affected while correlated sensors remain normal)

Dataset Details

—Rows: 50,000 (10,000 per equipment unit)
—Columns: 22
—Anomaly rate: ~4.5%
—Time span: 1-minute intervals starting 2024-01-01
—Generation: Fully synthetic, reproducible with numpy.random.default_rng(seed=42)

Built-in Complexity

The data contains deliberate challenges for ML pipelines:

—Target leakage features (3): rolling_anomaly_rate, maintenance_priority_score, and alert_code contain information derived from the target label that would not exist at prediction time
—Missing values: ~1–3% per sensor in two patterns — random sporadic dropouts and correlated block outages (5–15 minute windows)
—Concept drift: Baseline sensor values shift partway through the timeline for each equipment unit
—Operating mode effects: Sensor baselines differ significantly across modes (startup, normal, high_load, cooldown, idle)
—Cross-sensor correlations: Temperature affects oil viscosity, vibration drives acoustic levels, RPM and flow determine power consumption

File Structure

—data.csv — Full raw dataset (50,000 rows × 22 columns)

Features

Column	Type	Description
reading_id	string	Unique row identifier (R000000–R049999)
timestamp	datetime	Reading timestamp at 1-minute intervals starting 2024-01-01
equipment_id	string	Equipment unit identifier (EQ-001 through EQ-005)
operating_mode	string	Current mode: startup, normal, high_load, cooldown, idle
temperature_c	float	Main bearing temperature in Celsius
vibrationmms	float	Vibration velocity in mm/s
pressure_kpa	float	System pressure in kilopascals
motor_rpm	float	Motor rotational speed
flowratelpm	float	Coolant/fluid flow rate in liters per minute
powerconsumptionkw	float	Electrical power draw in kilowatts
coolanttempc	float	Coolant outlet temperature in Celsius (lags main temp)
acousticleveldb	float	Acoustic emission level in decibels
oilviscositycst	float	Lubricant viscosity in centistokes
humidity_pct	float	Ambient humidity percentage
ambienttempc	float	Ambient environmental temperature in Celsius
equipmentagehours	float	Cumulative operating hours of the equipment
hourssincemaintenance	float	Hours elapsed since last maintenance event
is_anomaly	int	Target label: 1 = anomalous, 0 = normal
anomaly_type	string	Anomaly category: normal, thermalrunaway, bearingdegradation, pressureleak, sensormalfunction
rollinganomalyrate	float	⚠️ LEAKAGE — Rolling mean of is_anomaly with centered window
maintenancepriorityscore	float	⚠️ LEAKAGE — Score assigned post-hoc based on anomaly status
alert_code	string	⚠️ LEAKAGE — Alert category derived from anomaly_type with noise

Generation Script

The dataset was generated with the included generate_data.py script using fixed random seeds for full reproducibility. Running the script produces an identical data.csv.

License

MIT — free for any use.