Anselme Herman EYELEKO

This dataset, denoted as 𝑾_data, represents a synthetic yet structurally authentic warehouse management dataset comprising 4,132 records and 11 well-defined attributes. It was generated using the Gretel.ai platform, following the structural standards provided by the TI Supply Chain API–Storage Locations specification. The dataset encapsulates essential operational and spatial parameters of warehouses, including unique identifiers, geospatial coordinates, storage capacities, and categorical capacity statuses.

The Machine Failure Predictions Dataset (D_2) is a real-world dataset sourced from Kaggle, containing 10,000 records and 14 features pertinent to IIoT device performance and health status. The binary target feature, 'failure', indicates whether a device is functioning (0) or has failed (1). Predictor variables include telemetry readings and categorical features related to device operation and environment. Data preprocessing included aggregating features related to failure types and removing non-informative features such as Product ID.

The PdM_telemetry Dataset (D_1) is a synthetic dataset designed to support predictive maintenance (PdM) research for IIoT (Industrial Internet of Things) devices by providing sensor-based telemetry data. This dataset initially comprises 97,210 records and 30 features, including a binary target feature, 'failure', which indicates whether a device will fail within the next 24 hours. The remaining features, such as device operational metrics and error counts, serve as predictors.