Industrial IoT Dataset (Synthetic) for Predictive Maintenance
Catalog Summary
"The Industrial IoT Dataset (Synthetic) provides a large-scale simulation of sensor readings and operational metrics from machines deployed in a smart factory environment. It focuses on predictive maintenance and anomaly detection."
Catalog Notes
Overview
A factory sensor simulator dataset created for predictive maintenance and machine learning tasks in Industry 5.0 contexts.
Technical Details
Contains records for 500,000 simulated machines, capturing time-dependent sensor data and equipment status variables.
Collection Setup
Data generation via a simulation pipeline emulating different operational modes and failure dynamics across a virtual fleet.
Recommended Research Tasks
Fault detection, remaining useful life (RUL) estimation, and condition-based maintenance policy design.
Access & License
Available on Kaggle for industrial analytics research. Access Dataset
View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on Kaggle.
Preview on KaggleProvided Citation
author = {Canozensoy, C.},
title = {{Industrial IoT Dataset (Synthetic) for factory sensor simulation}},
howpublished = {Kaggle},
year = {2025}
This citation is displayed as supplied. Automatic style conversion is disabled because structured citation metadata is not recorded.
Source metadata: Kaggle (2025)
Indexed by IoTDataset.com on Feb 05, 2026
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