CWRU Domain Generalization Version

The Case Western Reserve University (CWRU) is a widely adopted dataset for rolling bearing elements (Smith and Randall 2015). Accelerometer sensors were deployed at both the drive-end and fan-end of the housing motors. Vibration signals were collected with 12 KHz sampling rate under eight different operating conditions. Particularly, we have four different operating conditions with different loading torques collected from the drive end, denoted as domain A, B, C, and D. Similarly, we have other four operating conditions collected from the fan-end of the motor, denoted as domain F, G, H, and I. For each operating condition, there is one healthy state and three faulty states, namely, inner fault (IF), outer fault (OF), and bearing fault (BF). Each faulty state has three levels of severity with dimensions of 7, 14, 21 mil. In total, we have 10 classes with 1 healthy class and 9 faulty classes. To prepare the data for our experiments, we partitioned the sensor readings into smaller samples using sliding windows with a fixed length of 4,096 and the shifting step of 290, which is widely used in previous studies (Zhang et al. 2017; Ragab et al. 2021). Overall, we can generate 4,000 samples for each domain.凯斯西储大学（Case Western Reserve University，CWRU）发布的滚动轴承元件数据集是目前机械故障诊断领域广泛采用的公开基准数据集（Smith与Randall，2015）。研究人员在电机壳体的驱动端与风扇端均部署了加速度传感器，以12 kHz的采样率采集了8种不同工况下的振动信号。其中，从驱动端采集得到4种带有不同负载扭矩的工况，分别记为域A、B、C、D；同理，从电机风扇端采集得到另外4种工况，记为域F、G、H、I。每种工况下包含1种健康状态与3种故障状态，即内圈故障（inner fault，IF）、外圈故障（outer fault，OF）与滚动体故障（bearing fault，BF）；每种故障状态均设有3种严重程度等级，对应损伤尺寸分别为7、14、21密耳（mil）。整体共包含10个类别，其中1类为健康类别，9类为故障类别。为制备实验所需数据，我们采用固定长度为4096、滑动步长为290的滑动窗口，将原始传感器读数分割为小型样本；该预处理方案已在既往相关研究中得到广泛应用（Zhang等，2017；Ragab等，2021）。最终每个域可生成4000个样本数据。

搜集汇总

背景与挑战

背景概述

该数据集是凯斯西储大学（CWRU）滚动轴承故障诊断数据集的领域泛化版本，专门用于故障诊断和领域泛化研究。它包含从电机驱动端和风扇端采集的振动信号，涵盖八个不同操作条件（域A到D和F到I），每个条件下有健康状态和三种故障类型（内圈、外圈、轴承故障），每个故障有三个严重级别，共10个类别。数据经过滑动窗口预处理（窗口长度4096，步长290），每个域生成4000个样本，适用于时间序列分析和机器学习模型训练。