---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- smart-factory
- sensor-data
- industry-4-0
- industrial-iot
- predictive-maintenance
- telemetry
- synthetic-data
- mindweave
- time-series
- iot
- test-data
- edge-analytics
- anomaly-detection
- manufacturing
- machine-monitoring
pretty_name: IoT Sensor Telemetry (Synthetic) (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: anomaly_events
  data_files: data/anomaly_events.csv
  default: true
- config_name: sensors
  data_files: data/sensors.csv
- config_name: telemetry_readings
  data_files: data/telemetry_readings.csv
---

# IoT Sensor Telemetry (Synthetic) (Free Sample)

> **This is a free sample** with 5,003 rows. The full dataset has **50,006 rows** across 3 tables.

High-frequency telemetry from a simulated smart factory operating three
CNC lines, a finishing cell, and a predictive-maintenance program over
six months. Covers temperature, humidity, pressure, and vibration sensors
sampled on a rolling 5-minute schedule with realistic shift patterns,
machine assignments, maintenance alerts, and operational state changes.

Includes two injected anomalies: a progressive calibration drift on sensor
3 during month 4 and a sudden spike pattern on sensor 1 that signals an
equipment failure event. Useful for time-series analytics, anomaly
detection, edge telemetry pipelines, and Industry 4.0 monitoring demos.


## Sample tables

| Table | Sample Rows |
|-------|------------|
| anomaly_events | 2 |
| sensors | 1 |
| telemetry_readings | 5,000 |
| **Total** | **5,003** |

## Full dataset

The complete dataset includes all tables with full row counts:

| Table | Full Rows |
|-------|----------|
| anomaly_events | 2 |
| sensors | 4 |
| telemetry_readings | 50,000 |
| **Total** | **50,006** |

**Formats included:** CSV, Parquet, SQLite

**[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)**

## About

Generated by [Mindweave Technologies](https://mindweave.tech) -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)

Browse all datasets: [https://mindweavetech.gumroad.com](https://mindweavetech.gumroad.com)