Data_1

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. Data preprocessing steps involved creating additional features from timestamp values and consolidating error count indicators to streamline analysis. Due to class imbalance, with only 0.70% of samples classified as failures, the dataset was balanced using the SMOTE algorithm to achieve equal representation of failure and non-failure cases. Further enhancement was performed by generating 38,085 synthetic samples using the Gretel.ai platform, enriching the dataset's variability and relevance for machine learning model training. This dataset is intended for developing and evaluating predictive models for IIoT device failure, contributing to more accurate and timely maintenance interventions.