Smart Manufacturing IIoT Anomaly Dataset (SMIAD25)

This dataset represents a large-scale Industrial IoT (IIoT) multivariate time-series collection obtained from smart manufacturing environments operating under Industry 4.0 principles. The data span a continuous period from 17 January 2021 to 30 October 2025, recorded at 15-minute intervals, and capture machine-level operational behavior across multiple production lines, stations, and process stages. Each record corresponds to a unique (timestamp, machine_id) pair and reflects real operational dynamics observed in interconnected industrial systems. TThe acronym of dataset is Smart Manufacturing IIoT Anomaly Dataset (SMIAD25). The dataset contains heterogeneous feature groups commonly generated in modern manufacturing plants. These include mechanical and vibration-related measurements, acoustic indicators, electrical and drive characteristics, thermal and environmental readings, and process-level operational metrics. In addition, inspection- and vision-derived quality indicators are included to represent downstream quality assessment and surface inspection activities. Temporal evolution is characterized through change-based features, rolling statistics, and trend descriptors that reflect gradual degradation and long-term behavioral shifts. To account for inter-machine interactions, the dataset is accompanied by a machine relationship graph encoding proximity, material flow, and control dependencies among assets. This enables the study of relational effects and collective anomaly propagation in distributed manufacturing systems. The primary prediction target, anomaly_label, identifies abnormal versus normal operating conditions and exhibits strong class imbalance, consistent with real industrial fault occurrence patterns. Two auxiliary labels, anomaly_type and anomaly_severity, provide fault categorization and progressive intensity information, supporting advanced diagnostic and prognostic analysis. The dataset is suitable for temporal, graph-aware, and imbalance-robust learning approaches and supports realistic evaluation under deployment-oriented experimental settings.本数据集为大规模工业物联网（Industrial IoT，IIoT）多变量时序数据集，采集自遵循工业4.0（Industry 4.0）理念运行的智能制造场景。数据采集周期覆盖2021年1月17日至2025年10月30日的连续时段，采样间隔为15分钟，涵盖多条生产线、各工位及不同工艺阶段的设备级运行行为。每条记录对应唯一的（时间戳，设备ID）组合，反映互联工业系统中观测到的真实运行动态。本数据集的缩写为智能制造工业物联网异常数据集（Smart Manufacturing IIoT Anomaly Dataset，SMIAD25）。 本数据集包含现代制造工厂中常见的多类型特征组，涵盖机械与振动相关测量值、声学指标、电气与驱动特性、热学与环境读数，以及工艺级运行参数。此外，数据集还包含检测与视觉衍生的质量指标，用于表征下游质量评估与表面检测活动；时序演化特征则通过基于变化的特征、滚动统计量与趋势描述符体现，以反映设备的渐进式退化与长期行为偏移。 为刻画设备间的交互关系，本数据集附带一张设备关系图，对资产间的邻近性、物料流动与控制依赖关系进行编码，可用于研究分布式制造系统中的关联效应与集体异常传播规律。 本数据集的核心预测目标为anomaly_label（异常标签），用于区分正常与异常运行工况，且存在显著的类别不平衡问题，符合真实工业故障的发生规律。此外还包含两个辅助标签：anomaly_type（异常类型）与anomaly_severity（异常严重程度），分别提供故障分类与渐进式强度信息，可支撑高级诊断与预后分析。本数据集适用于时序建模、图感知以及抗类别不平衡的学习方法，可支持面向实际部署场景的实验评估。