Lifsitch Heitor, Rocha Gabriel, Bragança Hendrio, Filho Cláudio, Okimoto Leandro, Amorin Allan, Cardoso Fábio
Department of R&D, TPV company, Amazonas, Brazil.
Department of Electrical Engineering, State University of Amazonas, Amazonas, Brazil.
Data Brief. 2024 Aug 24;56:110866. doi: 10.1016/j.dib.2024.110866. eCollection 2024 Oct.
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 32 KHz 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.
为了加强连续电机健康监测领域，我们展示了FAN - COIL - I，这是一个源自风机盘管电机的广泛振动传感器数据集。该数据集在促进电机健康问题的检测和预测方面具有独特优势，能够实现更高效的维护调度流程，从而有可能避免定期检查的需要。与通常在受控条件下或通过模拟创建的现有数据集不同，FAN - COIL - I是根据实际运行数据汇编而成的，为真实的电机诊断和预测性维护研究提供了宝贵资源。该数据集使用32千赫兹的高分辨率采样率收集，涵盖了风机盘管电机前后两侧在连续两周内的全面振动读数，让我们得以罕见地一窥这些系统在企业环境中的动态运行模式。FAN - COIL - I不仅因其在现实世界中的适用性而脱颖而出，还因其有潜力成为研究人员和从业者的可靠基准，他们可以根据真实发动机状况来验证自己的模型。
