IMAD-DS: A Dataset for Industrial Multi-Sensor Anomaly Detection Under Domain Shift Conditions

IMAD-DS is a dataset developed for multi-rate multi-sensor anomaly detection (AD) in industrial environments, that considers varying operational and environmental conditions known as domain shifts. Dataset Overview: This dataset includes data from two scaled industrial machines: a robotic arm and a brushless motor. It includes both normal and abnormal data recorded under various operating conditions to account for domain shifts. These shifts are categorized into: Robotic Arm: The robotic arm is a scaled version of a robotic arm used to move silicon wafers in a factory. Anomalies are created by removing bolts at the nodes of the arm, resulting in an imbalance in the machine.Brushless Motor: The brushless motor is a scaled representation of an industrial brushless motor. Two anomalies are introduced: first, a magnet is moved closer to the motor load, causing oscillations by interacting with two symmetrical magnets on the load; second, a belt that rotates in unison with the motor shaft is tightened, creating mechanical stress. The following domain shifts are included in the dataset: Operational Domain Shifts: Variations caused by changes in machine conditions (e.g., load changes for the robotic arm and speed changes for the brushless motor). Environmental Domain Shifts: Variations due to changes in background noise levels. Combinations of operating and environmental conditions divide each machine's dataset into two subsets: the source domain and the target domain. The source domain has a large number of training examples. The target domain, instead, has limited training data. This discrepancy highlights a common issue in the industry where sufficient training data is often unavailable for the target domain, as machine data is collected under controlled environments that do not fully represent the deployment environments. Data Collection and Processing: Data is collected using the STEVAL-STWINBX1 IoT Sensor Industrial Node. The sensor used to record the dataset are the following. · Analog Microphone (16 kHz) · 3-axis Accelerometer (6.7 kHz) · 3-axis Gyroscope (6.7 kHz) Recordings are conducted in an anechoic chamber to control acoustic conditions precisely Data Format:Files are already divided into train and test sets. Inside each folder, each sensor's data is stored in a separate '.parquet' file. Sensor files related to the same segment of machine data share a unique ID. The mapping of each machine data segment to the sensor files is given in .csv files inside the train and test folders. Those .csv files also contain metadata denoting the operational and environmental conditions of a specific segment.IMAD-DS是为工业环境下多速率多传感器异常检测（Anomaly Detection，AD）开发的数据集，其考量了被称为域偏移（domain shift）的多样化运行与环境工况。 数据集概览： 本数据集包含两台缩比工业设备的数据：机械臂与无刷电机。 数据集涵盖了在各类工况下采集的正常与异常数据，以覆盖域偏移场景。这些域偏移可分为以下类别： 机械臂：该机械臂为工厂中用于搬运硅片的机械臂缩比模型。通过拆除机械臂关节处的螺栓制造异常，导致设备失衡。 无刷电机：该无刷电机为工业无刷电机的缩比模型，共设置了两种异常工况：其一，将一块磁铁移至更靠近电机负载的位置，通过与负载上的两块对称磁铁相互作用引发振荡；其二，将与电机转轴同步旋转的皮带调紧，产生机械应力。 本数据集包含以下两类域偏移： 运行域偏移：由设备工况变化引发的参数波动（例如机械臂的负载变化、无刷电机的转速变化）。 环境域偏移：由背景噪声水平变化引发的参数波动。 运行工况与环境工况的组合将每台设备的数据集划分为源域与目标域两个子集。源域拥有大量训练样本，而目标域的训练数据十分有限。这种数据分布差异反映了工业界的普遍问题：由于设备数据通常在受控环境中采集，无法完全匹配实际部署场景，因此目标域往往难以获取充足的训练数据。 数据采集与处理： 数据通过STEVAL-STWINBX1物联网传感器工业节点采集，本次数据集使用的传感器如下： · 模拟麦克风（采样率16 kHz） · 三轴加速度计（采样率6.7 kHz） · 三轴陀螺仪（采样率6.7 kHz） 数据采集在消声室中完成，以精确控制声学环境。 数据格式： 数据集已预先划分为训练集与测试集。每个文件夹内，各传感器的数据分别存储于独立的`.parquet`文件中。 对应同一段设备数据的传感器文件拥有唯一标识符。训练集与测试集文件夹内的`.csv`文件提供了每一段设备数据与传感器文件的映射关系，这些`.csv`文件同时包含了对应工况段的运行与环境元数据。