Industrial IoT Dataset (Synthetic) for Predictive Maintenance
Abstract
"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."
Description
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 Kaggle
Cite This Dataset
Canozensoy, C. (2025). Industrial IoT Dataset (Synthetic) for Predictive Maintenance. [Dataset]. Kaggle. https://www.kaggle.com/datasets/canozensoy/industrial-iot-dataset-synthetic
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Original source: Kaggle (2025). Visit official page for more details.
Indexed by IoTDataset.com on Feb 05, 2026
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