Industrial IoT Synthetic Failure Simulation Dataset
Abstract
"This Industrial IoT dataset provides synthetic yet realistic sensor data simulating equipment operation under normal and various failure conditions. Designed for predictive maintenance and machine learning, it includes sensor specifications, operational thresholds, and failure labels, allowing researchers to develop anomaly detection models without the constraints of sensitive real-world industrial data."
Description
Overview
Industrial equipment monitoring requires robust datasets for predictive maintenance. This dataset bridges the gap for researchers lacking access to private factory floors by providing high-fidelity simulations of sensor behavior during equipment degradation.
What’s inside
- Data modalities: Time-series sensor logs and structured metadata.
- Scale: Multiple simulated sensor streams covering various measured quantities.
- Metadata: Sensor locations, specifications, and operational thresholds.
Collection / Setup
- Generated via synthetic failure simulations to ensure high coverage of rare failure modes.
- Modeled after real-world industrial sensor thresholds and sampling rates.
Labels / Targets
- Binary labels (Normal vs. Failure) and multi-class failure modes.
Recommended tasks
- Predictive maintenance
- Anomaly detection
- Remaining Useful Life (RUL) estimation
- Machine learning model benchmarking
Limitations
- Synthetic nature may not capture unexpected external environmental noise.
- Simplified failure mechanics compared to complex real-world machinery.
Access & License
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleCite This Dataset
Joshi, Vratraj (2025). Industrial IoT Synthetic Failure Simulation Dataset. [Dataset]. Kaggle. https://www.kaggle.com/datasets/vratrajjoshi/sensor-data
Source: Kaggle (2025)
Indexed by IoTDataset.com on Feb 05, 2026
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