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.
IMAD-DS captures multi-rate, multi-sensor signals from scaled industrial machines, including a robotic arm and a brushless motor, for anomaly detection research.
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.
DataSense is a real-time Industrial IoT (IIoT) dataset from the Canadian Institute for Cybersecurity, combining synchronized sensor and network data from a 40-device testbed with over 15 types of industrial sensors for anomaly and intrusion detection research.
Standardized energy flexibility data model enabling uniform communication of energy flexibility potentials within industrial companies and in exchange with external energy systems (grids, aggregators), supporting demand response and sector coupling.
Comprehensive Industrial IoT security dataset from the Canadian Institute for Cybersecurity, featuring realistic network traffic with 34 types of attacks including DDoS, ransomware, data exfiltration, and advanced persistent threats across multiple IIoT protocols.
Triaxial vibration time-series from an induction motor bearing under healthy and multiple fault severities (inner/outer race) at different mechanical loads, sampled at 10 kHz for condition monitoring research.[page:2][web:165]
State-of-the-art IIoT dataset from Canadian Institute for Cybersecurity with synchronized sensor and network data from 40 devices including 15+ industrial sensors. Features multi-objective feature selection for anomaly detection in industrial environments.
Realistic cybersecurity dataset with 14 attack types from 10+ IoT/IIoT device types including sensors, actuators, and industrial controllers. Supports centralized and federated learning with 61 optimized features.
Industry-standard dataset for prognostics research with simulated run-to-failure data from 100 turbofan engines including 21 sensor readings and remaining useful life (RUL) labels for predictive maintenance algorithms.
Real-time IoT sensor data collected from industrial machines for predictive maintenance and anomaly detection in smart manufacturing environments, featuring temperature, vibration, pressure readings, and machine operational status for Industry 4.0 applications.