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.旋转机械故障严重程度的测量与诊断，可支撑设备预测性维护作业的开展。此类作业能够实现设备运行参数的实时监测与故障预判，进而规避生产损失、设备严重损坏，并保障设备操作人员的人身安全。本文详述了某旋转机械振动信号数据集的构建流程。本次数据采集覆盖七种不同转速的运行工况。相较于当前已有的少量同类数据集，本数据集涵盖了不同严重程度等级的单一故障与复合故障。本次考量的运行工况具体包括：正常运行状态、不平衡故障、水平不对中故障、垂直不对中故障、不平衡与水平不对中复合故障、不平衡与垂直不对中复合故障，以及垂直不对中与水平不对中复合故障。本文所描述的数据集可供致力于自动检测旋转机械故障的机器学习研究人员使用。在此研究背景下，可开展多项相关课题，例如特征提取与/或选择、特征空间降维、数据增强方法，以及通过故障严重程度参数分析实现的旋转机械故障预后。

搜集汇总

背景与挑战

背景概述

Composed Fault Dataset (COMFAULDA) 是一个用于旋转机械故障诊断的振动信号数据集，包含七种操作场景（如正常条件、单一故障和组合故障）和多种严重级别。该数据集旨在支持机器学习研究，特别是自动故障检测、特征提取和预测性维护应用。