Composed Fault Dataset (COMFAULDA)

The measurement and diagnosis of the severity of failures in rotating machines allow the execution of predictive maintenance actions on equipment. These actions make it possible to monitor the operating parameters of the machine and to perform the prediction of failures, thus avoiding production losses, severe damage to the equipment, and safeguarding the integrity of the equipment operators. This paper describes the construction of a dataset composed of vibration signals of a rotating machine. The acquisition has taken into consideration seven distinct operating scenarios, with different speed values. Unlike the few datasets that currently exist, the resulting dataset contains simple and combined faults with several severity levels. The considered operating setups are normal condition, unbalance, horizontal misalignment, vertical misalignment, unbalance combined with horizontal misalignment, unbalance combined with vertical misalignment, and vertical misalignment combined with horizontal misalignment. The dataset described in this paper can be utilized by machine learning researchers that intend to detect faults in rotating machines in an automatic manner. In this context, several related topics might be investigated, such as feature extraction and/or selection, reduction of feature space, data augmentation methods, and prognosis of rotating machines through the analysis of failure severity parameters.

对旋转机械故障严重程度的测量与诊断，有助于执行预测性维护措施，以保障设备的安全运行。此类措施不仅能够监控机器的运行参数，预测故障的发生，从而避免生产损失、设备严重损坏，还能确保设备操作者的安全。本文描述了一个由旋转机械振动信号构成的_dataset_的构建过程。该_dataset_的采集考虑了七种不同的运行场景，涵盖了不同的速度值。与现有的少数_dataset_相比，本_dataset_包含了简单和复合故障，以及多个严重程度等级。所考虑的运行配置包括：正常状态、不平衡、水平偏移、垂直偏移、不平衡与水平偏移结合、不平衡与垂直偏移结合，以及垂直偏移与水平偏移结合。本文所描述的_dataset_可供旨在自动检测旋转机械故障的机器学习研究人员使用。在此背景下，可能需要探讨一些相关主题，如特征提取与/或选择、特征空间缩减、数据增强方法，以及通过分析故障严重程度参数对旋转机械进行预测性维护。