Summary: This dataset from Rieth et al. (2017) includes simulated data for anomaly detection in joint human-automated systems, featuring different types of faults and operational conditions, collected as part of a study presented at Applied Human Factors and Ergonomics 2017.
| Parameter | Value |
|---|---|
| Name | Additional Tennessee Eastman Process Simulation Data |
| Labeled | Yes |
| Time Series | Yes |
| Simulation | Yes |
| Missing Values | No |
| Dataset Characteristics | Time-Series, Multivariate |
| Feature Type | Real |
| Associated Tasks | Anomaly Detection |
| Number of Instances | INA |
| Number of Features | 55 |
| Date Donated | 2017 |
| Source | Harvard Dataverse |
Includes dataframes named ‘fault_free_training’, ‘fault_free_testing’, ‘faulty_testing’, and ‘faulty_training’ with 55 columns. Columns range from fault types, simulation run identifiers, and sample indices to 52 process variables, reflecting different conditions of the Tennessee Eastman Process.
Anomaly detection, Process simulation, Human-automated systems, Operational research, Fault analysis