西储大学轴承故障数据下载和整理

西储大学轴承故障数据下载和整理

 

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西储大学轴承数据介绍

 

西储大学轴承数据官网

 

python2 版本 cwru库github地址

 

Github数据下载地址

 

python2 可以直接安装使用CWRU库，该库的功能是下载数据，并且切分成可供训练和评估的训练集和测试集数据

 

python2安装方式：

 

pip安装：

 

$ pip install --user cwru

github下载源代码安装:

 

 

$ python setup.py install

 

使用

 

 

import cwru
data = cwru.CWRU("12DriveEndFault", "1797", 384)

可以使用data.X_train, data.y_train, data.X_test, data.y_test, data.labels, data.nclasses 来训练和评估模型

 

CWRU的参数：

exp：'12DriveEndFault', '12FanEndFault', '48DriveEndFault'

rpm：'1797', '1772', '1750', '1730'

length：信号的长度

 

python3版本：

由于python3 和python2 版本的差异，对原python2代码修改：

 

import os
import glob
import errno
import random
import urllib.request as urllib
import numpy as np
from scipy.io import loadmat


class CWRU:
    def __init__(self, exp, rpm, length):
        if exp not in ('12DriveEndFault', '12FanEndFault', '48DriveEndFault'):
            print("wrong experiment name: {}".format(exp))
            exit(1)
        if rpm not in ('1797', '1772', '1750', '1730'):
            print("wrong rpm value: {}".format(rpm))
            exit(1)
        # root directory of all data
        rdir = os.path.join('Datasets/CWRU',
                            exp,
                            rpm)
        print(rdir)

        fmeta = os.path.join(os.path.dirname(__file__), 'metadata.txt')
        all_lines = open(fmeta).readlines()
        lines = []
        for line in all_lines:
            l = line.split()
            if (l[0] == exp or l[0] == 'NormalBaseline') and l[1] == rpm:
                lines.append(l)

        self.length = length  # sequence length
        self._load_and_slice_data(rdir, lines)
        # shuffle training and test arrays
        self._shuffle()
        self.labels = tuple(line[2] for line in lines)
        self.nclasses = len(self.labels)  # number of classes

    def _mkdir(self, path):
        try:
            os.makedirs(path)
        except OSError as exc:
            if exc.errno == errno.EEXIST and os.path.isdir(path):
                pass
            else:
                print("can't create directory '{}''".format(path))
                exit(1)

    def _download(self, fpath, link):
        print("Downloading to: '{}'".format(fpath))
        urllib.URLopener().retrieve(link, fpath)

    def _load_and_slice_data(self, rdir, infos):
        self.X_train = np.zeros((0, self.length))
        self.X_test = np.zeros((0, self.length))
        self.y_train = []
        self.y_test = []
        for idx, info in enumerate(infos):

            # directory of this file
            fdir = os.path.join(rdir, info[0], info[1])
            self._mkdir(fdir)
            fpath = os.path.join(fdir, info[2] + '.mat')
            if not os.path.exists(fpath):
                self._download(fpath, info[3].rstrip('\n'))

            mat_dict = loadmat(fpath)
            # key = filter(lambda x: 'DE_time' in x, mat_dict.keys())[0]
            fliter_i = filter(lambda x: 'DE_time' in x, mat_dict.keys())
            fliter_list = [item for item in fliter_i]
            key = fliter_list[0]
            time_series = mat_dict[key][:, 0]
            idx_last = -(time_series.shape[0] % self.length)
            clips = time_series[:idx_last].reshape(-1, self.length)
            n = clips.shape[0]
            n_split =int((3 * n / 4))
            self.X_train = np.vstack((self.X_train, clips[:n_split]))
            self.X_test = np.vstack((self.X_test, clips[n_split:]))
            self.y_train += [idx] * n_split
            self.y_test += [idx] * (clips.shape[0] - n_split)

    def _shuffle(self):
        # shuffle training samples
        index = list(range(self.X_train.shape[0]))
        random.Random(0).shuffle(index)
        self.X_train = self.X_train[index]
        self.y_train = tuple(self.y_train[i] for i in index)

        # shuffle test samples
        index = list(range(self.X_test.shape[0]))
        random.Random(0).shuffle(index)
        self.X_test = self.X_test[index]
        self.y_test = tuple(self.y_test[i] for i in index)