<i>Vibration Sensor Dataset for Estimating Fan Coil Motor Health</i>

<i>To enhance the field of continuous motor health monitoring, we present FAN-COIL-I, an extensive vibration sensor dataset derived from a Fan Coil motor. This dataset is uniquely positioned to facilitate the detection and prediction of motor health issues, enabling a more efficient maintenance scheduling process that can potentially obviate the need for regular checks. Unlike existing datasets, often created under controlled conditions or through simulations, FAN-COIL-I is compiled from real-world operational data, providing an invaluable resource for authentic motor diagnosis and predictive maintenance research. Gathered using a high-resolution 32KHz sampling rate, the dataset encompasses comprehensive vibration readings from both the forward and rear sides of the Fan Coil motor over a continuous two-week period, offering a rare glimpse into the dynamic operational patterns of these systems in a corporate setting. FAN-COIL-I stands out not only for its real-world applicability but also for its potential to serve as a reliable benchmark for researchers and practitioners seeking to validate their models against genuine engine conditions.</i>为推动电机持续健康监测领域的发展，我们推出FAN-COIL-I——一款源自风机盘管电机的大规模振动传感器数据集。该数据集可有效助力电机健康问题的检测与预测，优化维护调度流程，甚至有望免除常规巡检工作。与多数在受控环境下生成或通过模拟构建的现有数据集不同，FAN-COIL-I源自真实运行场景的采集数据，可为真实电机诊断与预测性维护研究提供极具价值的资源。该数据集以高分辨率32kHz采样率进行采集，涵盖了连续两周内风机盘管电机前后两侧的完整振动读数，难得地展现了企业场景中这类设备的动态运行模式。FAN-COIL-I的优势不仅在于其具备真实场景适用性，还可作为可靠基准数据集，供研究人员与从业者基于真实电机运行工况验证其模型。