MOIRA-UNIMORE Bearing Dataset for Independent Cart Systems

This paper introduces a comprehensive and publicly accessible data set from an experimental study on an independent cart system powered by linear motors. The primary objective is to advance research in machine health monitoring, predictive maintenance, and stochastic modeling by providing the first data set of its kind. Vibration signals were collected using sensors placed along the track, alongside key system variables such as cart position, following error, speed, and set current. Experiments were conducted under a wide range of operating conditions, including different fault types, fault severities, cart speeds, and fault orientations, for both single-cart and multi-cart configurations. The data set captures the relationship between vibration signatures, system variables, and fault characteristics across diverse speed profiles. The data set includes inner race (IR) and outer race (OR) faults in both the top and bottom bearings, with fault severities of 0.25 mm, 0.5 mm, 1.0 mm, and 1.5 mm in width. Eight different types of experiments were performed, classified based on the number of carts used, the section of the guide rail traversed, and the type of movement exhibited. Each experiment was conducted at two distinct nominal speeds of 1000 mm/s and 2000 mm/s, with acquisition durations ranging from 30 s to 2 min. Many experiments included multiple realizations to ensure statistical reliability. Data were recorded at a sampling frequency of 50 kHz with a resolution of 24 bits. For single-cart experiments, 5 system variables were captured, while for three-cart experiments, 15 system variables were recorded along with nine vibration channels. The total data set is approximately 400 GB, offering an extensive resource for data-driven research. Independent cart systems present unique challenges such as non-synchronous operation, speed reversals, and modularity, with each cart containing multiple bearings. In industrial applications where hundreds of carts may operate simultaneously, monitoring a large number of bearings becomes highly complex, making fault identification and localization particularly difficult. Unlike conventional rotary systems, where bearings are fixed around a rotating shaft, independent cart systems involve bearings that both rotate and translate along the track. This fundamental difference makes existing data sets and methodologies inadequate, emphasizing the need for specialized research. By addressing this gap, this work provides a critical resource for benchmarking and developing novel algorithms for fault diagnosis, signal processing, and machine learning in industrial transport applications. The outcomes of this study lay the foundation for future research in the condition monitoring of linear motor-driven transport systems. MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems Jabbar, A.; Cocconcelli, M.; D’Elia, G.; Borghi, D.; Capelli, L.; Cavalaglio Camargo Molano, J.; Strozzi, M.; Rubini, R. MOIRA-UNIMORE Bearing Data Set for Independent Cart Systems. Appl. Sci. 2025, 15, 3691. https://doi.org/10.3390/app15073691本文介绍了一套全面且可公开获取的数据集，源自直线电机驱动的独立小车系统的实验研究。本数据集的核心目标在于推动机械健康监测、预测性维护及随机建模领域的研究，提供此类场景下的首个同类数据集。 研究人员沿导轨布置传感器采集振动信号，同时同步记录小车位置、跟随误差、运行速度及设定电流等关键系统变量。实验覆盖了丰富的工况条件，涵盖单小车与多小车配置下的多种故障类型、故障严重程度、小车运行速度及故障方位。 本数据集捕捉了不同速度曲线下，振动特征、系统变量与故障特性之间的关联。数据集包含上下轴承的内圈（inner race, IR）与外圈（outer race, OR）故障，故障宽度分别为0.25 mm、0.5 mm、1.0 mm及1.5 mm。 本次研究共开展8类不同实验，分类依据为所用小车数量、导轨通行区段及运动类型。每类实验均在1000 mm/s与2000 mm/s两种标称速度下开展，采集时长介于30 s至2 min之间。多数实验设置了多次重复以保证统计可靠性。 数据采集的采样频率为50 kHz，分辨率为24位。单小车实验采集5项系统变量，三小车实验则记录15项系统变量，并同步采集9路振动通道数据。整套数据集总容量约为400 GB，可为数据驱动型研究提供了极为丰富的资源。 独立小车系统存在诸多独特挑战，例如非同步运行、速度换向及模块化特性，且每台小车均搭载多组轴承。在工业应用场景中，可能有上百台小车同时运行，对大量轴承的健康监测工作复杂度极高，故障识别与定位难度显著提升。 与传统旋转系统中轴承固定于旋转轴的场景不同，独立小车系统中的轴承既会自转又会沿导轨平移。这种本质差异使得现有数据集与研究方法不再适用，凸显了开展针对性研究的必要性。 本研究填补了这一领域的空白，为工业运输场景下的故障诊断、信号处理及机器学习领域的新型算法研发与基准测试提供了关键支撑。本研究成果为直线电机驱动运输系统的状态监测领域奠定了坚实的研究基础。 MOIRA-UNIMORE 独立小车系统轴承数据集 Jabbar, A.; Cocconcelli, M.; D’Elia, G.; Borghi, D.; Capelli, L.; Cavalaglio Camargo Molano, J.; Strozzi, M.; Rubini, R. MOIRA-UNIMORE 独立小车系统轴承数据集。《应用科学》，2025, 15, 3691. https://doi.org/10.3390/app15073691