Update README.md

README.md

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----
-license: mit
----
+---
+language:
+  - en
+license: mit
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - tabular-classification
+tags:
+  - anomaly-detection
+  - industrial
+  - sensor-data
+  - synthetic
+  - time-series
+  - multivariate
+pretty_name: Industrial Equipment 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 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 |
+| vibration_mm_s | float | Vibration velocity in mm/s |
+| pressure_kpa | float | System pressure in kilopascals |
+| motor_rpm | float | Motor rotational speed |
+| flow_rate_lpm | float | Coolant/fluid flow rate in liters per minute |
+| power_consumption_kw | float | Electrical power draw in kilowatts |
+| coolant_temp_c | float | Coolant outlet temperature in Celsius (lags main temp) |
+| acoustic_level_db | float | Acoustic emission level in decibels |
+| oil_viscosity_cst | float | Lubricant viscosity in centistokes |
+| humidity_pct | float | Ambient humidity percentage |
+| ambient_temp_c | float | Ambient environmental temperature in Celsius |
+| equipment_age_hours | float | Cumulative operating hours of the equipment |
+| hours_since_maintenance | float | Hours elapsed since last maintenance event |
+| is_anomaly | int | Target label: 1 = anomalous, 0 = normal |
+| anomaly_type | string | Anomaly category: normal, thermal_runaway, bearing_degradation, pressure_leak, sensor_malfunction |
+| rolling_anomaly_rate | float | ⚠️ LEAKAGE — Rolling mean of is_anomaly with centered window |
+| maintenance_priority_score | 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.