Stržinar Žiga, Pregelj Boštjan, Petrovčič Janko, Škrjanc Igor, Dolanc Gregor
"Jožef Stefan" Institute, Jamova cesta 39, SI-1000 Ljubljana, Slovenia.
University of Ljubljana Faculty of Electrical Engineering, Tržaška cesta 25, SI-1000 Ljubljana, Slovenia.
Data Brief. 2024 Jun 10;55:110619. doi: 10.1016/j.dib.2024.110619. eCollection 2024 Aug.
Gathered from a real-world discrete manufacturing floor, this dataset features measurements of pneumatic pressure and electrical current during production. Spanning 7 days and encompassing approximately 150 processed units, the data is organized into time series sampled at 100 Hz. The observed machine performs 24 steps to process each unit. Each measurement in the time series, is annotated, linking it to one of the 24 processing steps performed by the machine for processing of a single piece. Segmenting the time series into contiguous regions of constant processing step labels results in 3674 labeled segments, each encompassing one part of the production process. The dataset enriched with labels facilitates the use of supervised learning techniques, like time series classification, and supports the testing of unsupervised methods, such as clustering of time series data. The focus of this dataset is on an end-of-line testing machine for small consumer-grade electric drive assemblies (device under test - DUT). The machine performs multiple actions in the process of evaluating each DUT, with the dataset capturing the pneumatic pressures and electrical currents involved. These measurements are segmented in alignment with the testing machine's internal state transitions, each corresponding to a distinct action undertaken in manipulating the device under observation. The included segments offer distinct signatures of pressure and current for each action, making the dataset valuable for developing algorithms for the non-invasive monitoring of industrial (specifically discrete) processes.
该数据集收集自实际的离散制造车间，其特点是记录了生产过程中的气压和电流测量值。数据跨度为7天，涵盖约150个加工单元，按100Hz的采样频率组织成时间序列。观察到的机器对每个单元执行24个步骤进行加工。时间序列中的每个测量值都有注释，将其与机器加工单个工件所执行的24个加工步骤之一相关联。将时间序列分割为具有恒定加工步骤标签的连续区域，得到3674个带标签的段，每个段都包含生产过程的一部分。带有标签的数据集便于使用监督学习技术，如时间序列分类，并支持对无监督方法的测试，如时间序列数据聚类。该数据集的重点是用于小型消费级电动驱动组件（被测设备 - DUT）的在线测试机。该机器在评估每个DUT的过程中执行多个动作，数据集记录了所涉及的气压和电流。这些测量值根据测试机的内部状态转换进行分割，每个状态转换对应于在操作被测设备时采取的一个不同动作。所包含的段为每个动作提供了独特的压力和电流特征，使得该数据集对于开发用于工业（特别是离散）过程无创监测的算法具有重要价值。
