Data_2

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 initial dataset exhibited a significant class imbalance (3.39\% failure rate), which was rectified using the SMOTE algorithm, resulting in a balanced dataset. To further enhance modeling accuracy, 3,865 synthetic samples were generated through the Gretel.ai platform. D_2 is suitable for predictive modeling tasks in IIoT, allowing researchers to explore machine failure patterns and improve maintenance planning for industrial systems.

《机器故障预测数据集》（D_2）是一份源于Kaggle的现实世界数据集，包含10,000条记录和14个与工业物联网（IIoT）设备性能及健康状况相关的特征。二进制目标特征'故障'指示设备是否正常工作（0）或已发生故障（1）。预测变量包括遥测读数以及与设备运行和环境相关的分类特征。数据预处理包括汇总与故障类型相关的特征，以及移除诸如产品ID等非信息性特征。初始数据集呈现出显著的类别不平衡（故障率为3.39%），通过SMOTE算法进行了校正，从而实现数据集的平衡。为进一步提升建模精度，通过Gretel.ai平台生成了3,865个合成样本。D_2适用于IIoT中的预测建模任务，使研究人员能够探究机器故障模式，并改善工业系统的维护规划。