凯斯西储大学轴承故障数据集官方网址：https://engineering.case.edu/bearingdatacenter/download-data-file

官方数据集整理版（不用挨个下了）：

        链接：https://pan.baidu.com/s/1RAmPFmxdsLhs_hhMMnmklA  提取码：fr5r

处理好的十分类数据集（含源文件与代码）：

        链接：https://pan.baidu.com/s/1_3C7e2tAb_bQbl4FcV0EMQ  提取码：7tna

        十分类数据集文件说明：

                1）100、108~等十个数据文件是从官方的数据集摘出来的

                2）之后用matlab（classes10code.m）处理为层c10signals.mat文件

                3)  再用python（datasetSample.py）将其处理为十分类数据c10classes.mat

                      c10classes.mat文件中包含打乱顺序的训练集（900×2048）、测试集（300×2048）

                      训练集（900×2048）： 900个样本，每个样本包含2048个数据点。

                4)  十分类数据集所含内容为如下图片：

                                                                                

 凯斯西储数据集解读：

        数据格式：轴承故障数据文件为Matlab格式

                         每个文件包含风扇和驱动端振动数据，以及电机转速，文件中文件变量命名如下：

                                DE - drive end accelerometer data  驱动端振动数据

                                FE - fan end accelerometer data      风扇端振动数据

                                BA - base accelerometer data          基座振动数据

                                time - time series data                      时间序列数据

                                RPM- rpm during testing                   单位转每分钟  除以60则为转频

         数据采集频率分别为：数据集A：在12Khz采样频率下的驱动端轴承故障数据

                                             数据集B：在48Khz采样频率下的驱动端轴承故障数据

                                             数据集C：在12Khz采样频率下的风扇端轴承故障数据

                                             数据集D：以及正常的轴承数据（采样频率应该是48k的）

        数据集B解读：在48Khz采样频率下的驱动端轴承故障直径又分为0.007英寸、0.014英寸、0.028英寸三种类别，每种故障下负载又分为0马力、1马力、2马力、3马力。在每种故障的每种马力下有轴承内圈故障、轴承滚动体故障、轴承外环故障（由于轴承外环位置一般比较固定，因此外环故障又分为3点钟、6点钟和12点钟三种类别）。

        在48Khz采样频率下的驱动端轴承故障数据如图：

                                 

          注：每个文件打开后，如IR007_1文件（48Khz的驱动端内环轴承故障数据下）打开后还会包含  BA：基座；DE:驱动端，FE:风扇端，RPM：转速四个数据，个人理解应该是在  驱动端轴承故障的情况下  包含BA，DE，FE三个不同位置传感器采集到的数据。

matlab代码：

clc;
clear all;
close all;

drive_100 = load('100.mat');
drive_108 = load('108.mat');
drive_121 = load('121.mat');
drive_133 = load('133.mat');
drive_172 = load('172.mat');
drive_188 = load('188.mat');
drive_200 = load('200.mat');
drive_212 = load('212.mat');
drive_225 = load('225.mat');
drive_237 = load('237.mat');

% de_100 = drive_100.X100_DE_time(1:121048);
de_100 = drive_100.X100_DE_time(1:4:484192);
de_108 = drive_108.X108_DE_time(1:121048);
de_121 = drive_121.X121_DE_time(1:121048);
de_133 = drive_133.X133_DE_time(1:121048);
de_172 = drive_172.X172_DE_time(1:121048);
de_188 = drive_188.X188_DE_time(1:121048);
de_200 = drive_200.X200_DE_time(1:121048);
de_212 = drive_212.X212_DE_time(1:121048);
de_225 = drive_225.X225_DE_time(1:121048);
de_237 = drive_237.X237_DE_time(1:121048);

de_signals = [de_100,de_108,de_121,de_133,de_172,de_188,de_200,de_212,de_225,de_237];
signals = de_signals.';

save('c10signals.mat','signals');
whos('-file','c10signals.mat')

 python：

import numpy as np
import scipy.io as scio
from random import shuffle

def normalize(data):
    '''(0,1)normalization
    :param data : the object which is a 1*2048 vector to be normalized 
    '''
    s= (data-min(data)) / (max(data)-min(data))

    return  s


def cut_samples(org_signals):
    ''' get original signals to 10*120*2048 samples, meanwhile normalize these samples
    :param org_signals :a 10* 121048 matrix of ten original signals 
    '''

    results=np.zeros(shape=(10,120,2048))
    temporary_s=np.zeros(shape=(120,2048))

    for i in range(10):
        s=org_signals[i]
        for x in range(120):
            temporary_s[x]=s[1000*x:2048+1000*x]
            temporary_s[x]=normalize(temporary_s[x])     #顺道对每个样本归一化
        results[i]=temporary_s

    return results


def make_datasets(org_samples):
    '''输入10*120*2048的原始样本，输出带标签的训练集(占75%)和测试集(占25%)'''

    train_x=np.zeros(shape=(10,90,2048))
    train_y=np.zeros(shape=(10,90,10))
    test_x=np.zeros(shape=(10,30,2048))
    test_y=np.zeros(shape=(10,30,10))
    for i in range(10):
        s=org_samples[i]
        # 打乱顺序
        index_s = [a for a in range(len(s))]
        shuffle(index_s)
        s=s[index_s]
        # 对每种类型都划分训练集和测试集
        train_x[i]=s[:90]
        test_x[i]=s[90:120]
        # 填写标签
        label = np.zeros(shape=(10,))
        label[i] = 1
        train_y[i, :] = label
        test_y[i, :] = label

    #将十种类型的训练集和测试集分别合并并打乱
    x1 = train_x[0]
    y1 = train_y[0]
    x2 = test_x[0]
    y2 = test_y[0]
    for i in range(9):
        x1 = np.row_stack((x1, train_x[i + 1]))
        x2 = np.row_stack((x2, test_x[i + 1]))
        y1 = np.row_stack((y1, train_y[i + 1]))
        y2 = np.row_stack((y2, test_y[i + 1]))

    index_x1= [i for i in range(len(x1))]
    index_x2= [i for i in range(len(x2))]
    shuffle(index_x1)
    shuffle(index_x2)
    x1=x1[index_x1]
    y1=y1[index_x1]
    x2=x2[index_x2]
    y2=y2[index_x2]

    return x1, y1, x2, y2    #分别代表：训练集样本，训练集标签，测试集样本，测试集标签

def get_timesteps(samples):
    ''' get timesteps of train_x and test_X to 10*120*31*128
    :param samples : a matrix need cut to 31*128
    '''

    s1 = np.zeros(shape=(31, 128))
    s2 = np.zeros(shape=(len(samples), 31, 128))
    for i in range(len(samples)):
        sample = samples[i]
        for a in range(31):
            s1[a]= sample[64*a:128+64*a]
        s2[i]=s1

    return s2


# 读取原始数据，处理后保存
dataFile= 'G://study of machine learing//deep learning//LSTM//datasets//十个原始信号.mat'
data=scio.loadmat(dataFile)
org_signals=data['signals']
org_samples=cut_samples(org_signals)
train_x, train_y, test_x, test_y=make_datasets(org_samples)
train_x= get_timesteps(train_x)
test_x= get_timesteps(test_x)

saveFile = 'G://study of machine learing//deep learning//LSTM//datasets//datasets.mat'
scio.savemat(saveFile, {'train_x':train_x, 'train_y':train_y, 'test_x':test_x, 'test_y':test_y})

引用：

利用python整理凯斯西储大学（CWRU）轴承数据，制作数据集_Victor`Wu的博客-CSDN博客