提交数据集说明

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diff --git a/doc/CWRU.md b/doc/CWRU.md
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+# CWRU数据说明
+## 1.概述
+
+      CWRU数据集美国凯斯西储大学提供。截止到 2015 年，仅机械故障诊断领域顶级期刊《Mechanical Systems and Signal Processing》就发表过 41 篇使用CWRU 轴承数据进行故障诊断的文章。在基于深度学习的轴承故障诊断领域。当下，轴承故障诊断算法更新较快，为了评价被提出算法的优越性，最客观的方式就是使用第三方标准数据库与当下主流算法比较。试验中使用2马力Reliance Electric电动机进行实验，并且在靠近和远离电动机轴承的位置处测量加速度数据。每个实验都仔细记录了电机的实际测试条件以及轴承故障状态。
+使用电火花加工（EDM）为电机轴承提供故障。在内滚道，滚动元件（即滚珠）和外滚道处分别引入直径0.007英寸至0.040英寸直径的故障。将故障轴承重新安装到测试电机中，并记录0至3马力（电机速度为1797至1720 RPM）的电机负载的振动数据。
+
+
+* 数据下载连接(https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website)
+CWRU数据集是使用最为广泛的，文献较多。不一一举例。其中University of New South Wales 的Wade A. Smith在2015年进行了比较全面的总结和对比。比较客观的综述和分析了使用数据进行诊断和分析研究的情况。官方网站提供的是.mat格式的数据，MATLAB直接使用比较方便。
+* Github上有人分享了在python中自动下载和使用的方法。https://github.com/Litchiware/cwru
+* R语言中使用的方法：https://github.com/coldfir3/bearing_fault_analysis
+
+
+## 2.试验条件
+        CWRU轴承数据集采集实验台由1.5kW的电机、驱动端轴承、风扇端轴承、扭矩传感器、测功机、加速度传感器和电子控制器组成。待检测的轴承支撑着电动机的转轴，驱动端轴承型号为SKF6205，风扇端轴承型号为SKF6203，本文中使用驱动端轴承数据集。通过电火花加工模拟轴承的多种健康状况，电动机风扇端和驱动端的轴承座上方各放置一个加速度传感器用来采集故障轴承的振动加速度信号。振动信号通过16通道的数据记录仪采集得到，采样频率为12kHz，功率和转速通过扭矩传感器测得。
+
+## 3.数据基本情况
+			该数据集包括四种轴承不同轴承健康状态，即**正常状态、内圈故障、外圈故障和滚动体故障**。分别有7mils、14mils和21mils**三种故障直径**（1mils=0.0254mm）。该电动机在0hp、1hp、2hp、3hp**四种不同的负载**和1730r/min、1750r/min、1772r/min、1797r/min**四种不同转速**下收集振动信号。
+### 基准研究
+* Smith W A, Randall R B. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study[J]. Mechanical Systems and Signal Processing, 2015,64-65:100-131.[论文链接](https://www.sciencedirect.com/science/article/pii/S0888327015002034)
+* Boudiaf A, Moussaoui A, Dahane A, et al. A comparative study of various methods of bearing faults diagnosis using the case Western Reserve University data[J]. Journal of Failure Analysis and Prevention, 2016, 16(2): 271-284. [论文链接](https://link.springer.com/article/10.1007/s11668-016-0080-7)
+### 信号处理与特征工程
+
+ * Su W, Wang F, Zhu H, et al. Rolling element bearing faults diagnosis based on optimal Morlet wavelet filter and autocorrelation enhancement[J]. Mechanical systems and signal processing, 2010, 24(5): 1458-1472.[论文链接](https://www.sciencedirect.com/science/article/pii/S0888327009003835)  
+ 基于最优小波滤波和自相关增强的滚动轴承故障诊断方法
+
+ * Saidi L, Ali J B, Fnaiech F. Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis[J]. ISA transactions, 2014, 53(5): 1650-1660.[论文链接](https://www.sciencedirect.com/science/article/pii/S0019057814001220)  
+ 基于双谱的emd应用于非平稳振动信号的轴承故障诊断  
+
+ * Zhu K, Song X, Xue D. A roller bearing fault diagnosis method based on hierarchical entropy and support vector machine with particle swarm optimization algorithm[J]. Measurement, 2014, 47: 669-675.[论文链接](https://www.sciencedirect.com/science/article/pii/S0263224113004569)  
+ 提出了一种基于层次熵和支持向量机的滚动轴承故障诊断方法
+
+ * Li Y, Wang X, Si S, et al. Entropy based fault classification using the Case Western Reserve University data: A benchmark study[J]. IEEE Transactions on Reliability, 2019.[论文链接](https://ieeexplore.ieee.org/abstract/document/8662701)  
+ 基于熵的故障分类利用西储大学案例数据:一项基准研究  
+
+ * Kedadouche M, Liu Z, Vu V H. A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis[J]. Measurement, 2016, 90: 292-308.[论文链接](https://www.sciencedirect.com/science/article/pii/S0263224116301361)  
+ 提出了一种基于经验小波变换的轴承故障诊断方法 
+
+
+### 分类与识别
+
+* Raj A S, Murali N. Early classification of bearing faults using morphological operators and fuzzy inference[J]. IEEE Transactions on Industrial Electronics, 2012, 60(2): 567-574.[论文链接](https://ieeexplore.ieee.org/abstract/document/6153367)  
+利用形态算子和模糊推理对轴承故障进行早期分类
+
+* Afrasiabi S, Afrasiabi M, Parang B, et al. Real-Time Bearing Fault Diagnosis of Induction Motors with Accelerated Deep Learning Approach[C]//2019 10th International Power Electronics, Drive Systems and Technologies Conference (PEDSTC). IEEE, 2019: 155-159.[论文链接](https://ieeexplore.ieee.org/abstract/document/8697244)  
+采用加速深度学习方法对异步电机轴承故障进行实时诊断 
+
+* Zhang R, Tao H, Wu L, et al. Transfer learning with neural networks for bearing fault diagnosis in changing working conditions[J]. IEEE Access, 2017, 5: 14347-14357.[论文链接](https://ieeexplore.ieee.org/abstract/document/7961149)  
+基于神经网络的轴承故障转移学习方法，用于轴承在不同工况下的故障诊断
+
+## 3.数据特点
+ 人为制造的故障，特征明显，诊断相对容易。使用广泛，认可度高。可以作为算法检验的基础数据集。
+
+
+[<<返回主目录](../README.md)
diff --git a/doc/Connecticut.md b/doc/Connecticut.md
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+# 美国康涅狄格大学（University of Connecticut）齿轮数据集
+
+## 1.简介
+  由美国康涅狄格大学唐炯教授团队分享。
+* 数据链接:(https://figshare.com/articles/Gear_Fault_Data/6127874/1)
+* P. C, S. Z, J. T. Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning[J]. IEEE Access, 2018,6:26241-26253. [论文链接](https://ieeexplore.ieee.org/abstract/document/8360102)
+* 课题组研究介绍[Dynamics, Sensing, and Controls Lab](https://dscl.uconn.edu/)
+
+experimental data are collected from a benchmark two-stage gearbox with replaceable
+gears as shown in Figure 7. The gear speed is controlled by
+a motor. The torque is supplied by a magnetic brake which
+can be adjusted by changing its input voltage. A 32-tooth
+pinion and an 80-tooth gear are installed on the rst stage
+input shaft. The second stage consists of a 48-tooth pinion
+and 64-tooth gear. The input shaft speed is measured by a
+tachometer, and gear vibration signals are measured by an
+accelerometer. The signals are recorded through a dSPACE
+system (DS1006 processor board, dSPACE Inc.) with sampling frequency of 20 KHz. As shown in Figure 8, nine
+different gear conditions are introduced to the pinion on the
+input shaft, including healthy condition, missing tooth, root
+crack, spalling, and chipping tip with ve different levels
+of severity. 
+
+## 2.数据使用情况
+
+
+[<<返回主目录](../README.md)
diff --git a/doc/FEMTO_ST.md b/doc/FEMTO_ST.md
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+# FEMTO-ST 轴承退化数据集
+
+## 1.简介 
+ 2012年IEEE PHM 比赛数据。 
+* FEMTO-ST网站：https://www.femto-st.fr/en
+* github链接：https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset
+
+http://data-acoustics.com/measurements/bearing-faults/bearing-6/
+
+## 2.实验简介
+IEEE PHM 2012 Data Challenge was developed for the estimation of useful life of rotating deep groove ball bearings. Tests were carried out in 3 different loading conditions ranging from 1500-1800 rpm and 4-5kN bearing load in a experimental test setup which enabled accelerated degradation of the bearings. 6 sets of training data and 11 sets of test data with vibration and temperature signals provided. The aim of the challenge was to estimate the useful reaming life of the bearings in the 11 testing datasets.
+
+
+## 3.使用情况
+* Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, 2012[C].
+* Nectoux P, Gouriveau R, Medjaher K, et al. PRONOSTIA: An experimental platform for bearings accelerated degradation tests.: IEEE International Conference on Prognostics and Health Management, PHM'12., 2012[C]. IEEE Catalog Number: CPF12PHM-CDR.
+* E. S, H. O, A. S S V, et al. Estimation of remaining useful life of ball bearings using data driven methodologies: 2012 IEEE Conference on Prognostics and Health Management, 2012[C].2012
+18-21 June 2012.
+
+
+[<<返回主目录](../README.md)
+
diff --git a/doc/IMS.md b/doc/IMS.md
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+# 美国辛辛那提大学 IMS 轴承数据集
+
+## 1.简介
+* 数据链接https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
+* IMS链接： http://www.iucrc.org/center/nsf-iucrc-intelligent-maintenance-systems  
+  http://imscenter.net/
+
+
+## 2.实验介绍
+
+An  AC  motor,  coupled  by  a  rub  belt,  keeps  the  rotation  speed  constant.  The  four 
+bearings are  in  the  same  shaft  and  are  forced lubricated  by  a  circulation  system  that 
+regulates  the  flow  and  the  temperature.  It  is  announced on  the  provided  “Readme 
+Document  for  IMS  Bearing  Data”  in  the  downloaded  file, that  the  test was  stopped 
+when the accumulation of debris on a magnetic plug exceeded a certain level indicating 
+the possibility of an impending failure.
+The four bearings are all of the same type. There are double range pillow blocks 
+rolling elements bearing. 
+
+Three (3) data  sets  are  included in the data  packet  (IMS-Rexnord Bearing Data.zip). Each data  set 
+describes  a  test-to-failure  experiment.  Each  data  set  consists  of  individual  files  that  are  1-second 
+vibration  signal  snapshots  recorded  at  specific  intervals.  Each  file  consists  of  20,480  points  with  the 
+sampling rate set at 20 kHz.  The file name indicates when the data was collected. Each record (row) in 
+the data file is a data point.  Data collection was facilitated by NI DAQ Card 6062E.  Larger intervals of 
+time stamps (showed in file names) indicate resumption of the experiment in the next working day.
+
+### Table 1. Bearing characteristics
+- Rexnord ZA-2115 Characteristics 
+
+| Parameter name | imperial | metric|
+| :---: | :----- | :---- |
+| Pitch diameter | 2.815 inch  | 71.5mm  |
+| Rolling element diameter |  0.331 inch | 8.4mm |
+| Number of rolling element per row | 16 | 16 |
+| Contact angle  | 15.17° | 15.17° |
+| Static load | 6000 lbs | 26690 N |
+
+
+
+
+### Set No. 1:
+- Recording Duration:   October 22, 2003 12:06:24 to November 25, 2003 23:39:56
+- No. of Files:  2,156
+- No. of Channels:   8 
+- Channel Arrangement:   Bearing 1 – Ch 1&2; Bearing 2 – Ch 3&4; 
+                         Bearing 3 – Ch 5&6; Bearing 4 – Ch 7&8.
+- File Recording Interval:   Every 10 minutes (except the first 43 files were taken every 5 minutes)
+- File Format:   ASCII
+- Description:  At  the  end  of  the  test-to-failure  experiment,  inner  race  defect  occurred  in bearing 3 and roller element defect in bearing 4.
+
+### Set No. 2:
+- Recording Duration:   February 12, 2004 10:32:39 to February 19, 2004 06:22:39
+- No. of Files:  984
+- No. of Channels:   4
+- Channel Arrangement:   Bearing 1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing 4 – Ch 4.
+- File Recording Interval:   Every 10 minutes
+- File Format:   ASCII
+- Description:  At  the  end  of  the  test-to-failure  experiment,  outer  race  failure  occurred  in 
+bearing 1.
+
+### Set No. 3
+- Recording Duration:   March 4, 2004 09:27:46 to April 4, 2004 19:01:57
+- No. of Files:  4,448
+- No. of Channels:   4
+- Channel Arrangement:   Bearing1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing4 – Ch4;
+- File Recording Interval:   Every 10 minutes
+- File Format:   ASCII
+- Description:  At  the  end  of  the  test-to-failure  experiment,  outer  race  failure  occurred  in 
+bearing 3.
+
+### Table 2. Datasets description
+|  | Number of files | Number of channels | Endurance duration | Duration of  recorded signal | Announced damages at the end of the endurance  |
+| :---: | :---: | :---: | :---: | :---: | :---: | 
+| Dataset 1 | 2156 | 8 |  49680 min 34 days 12h | 36 min | Bearing 3: inner race Bearing 4: rolling element |
+| Dataset 2 | 984  | 4 |  9840 min 6 days 20h | 16 min | Bearing 1: outer race |
+| Dataset 3 | 4448 | 4 | 44480 min 31 days 10h | 74 min | Bearing 3: outer race |
+
+### Table 3. Characteristic frequencies of the test rig
+
+| Characteristic frequencies | |
+| :--- | :--- |
+| Shaft frequency |  33.3 Hz |
+| Ball Pass Frequency Outer race (BPFO)  | 236 Hz |
+| Ball Pass Frequency Inner race (BPFI)  | 297 Hz |
+| Ball Spin Frequency (BSF)   | 278Hz (2x139 Hz) |
+| Fundamental Train Frequency (FTF) | 15 Hz |
+
+
+## 3.使用情况
+
+* Gousseau W, Antoni J, Girardin F, et al. Analysis of the Rolling Element Bearing data set of the Center for Intelligent Maintenance Systems of the University of Cincinnati: CM2016, 2016[C].
+* Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006,289(4):1066-1090.
+
+* A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models, Tobon-Mejia, Diego Alejandro and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, Gerard, Reliability, IEEE Transactions on, Vol. 61 No. 2, 491--503, 2012
+
+* Health condition monitoring of machines based on hidden markov model and contribution analysis, Yu, Jianbo, Instrumentation and Measurement, IEEE Transactions on, Vol. 61 No. 8, 2200--2211, 2012
+
+* Local and nonlocal preserving projection for bearing defect classification and performance assessment, Yu, Jianbo, Industrial Electronics, IEEE Transactions on, Vol. 59 No. 5, 2363--2376, 2012
+
+* Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets, Eker, OF and Camci, F and Jennions, IK, European Conference of Prognostics and Health Management Society, 2012
+
+* Remaining useful life estimation for systems with non-trendability behaviour, Porotsky, Sergey and Bluvband, Zigmund, Prognostics and Health Management (PHM), 2012 IEEE Conference on, 1--6, 2012
+
+* Logical analysis of maintenance and performance data of physical assets, ID34, Yacout, S, Reliability and Maintainability Symposium (RAMS), 2012 Proceedings-Annual, 1--6, 2012
+
+* Power wind mill fault detection via one-class $\nu$-SVM vibration signal analysis, Martinez-Rego, David and Fontenla-Romero, Oscar and Alonso-Betanzos, Amparo, Neural Networks (IJCNN), The 2011 International Joint Conference on, 511--518, 2011
+
+* cbmLAD-using Logical Analysis of Data in Condition Based Maintenance, Mortada, M-A and Yacout, Soumaya, Computer Research and Development (ICCRD), 2011 3rd International Conference on, 30--34, 2011
+
+* Hidden Markov Models for failure diagnostic and prognostic, Tobon-Mejia, DA and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, G{\'e}rard, Prognostics and System Health Management Conference (PHM-Shenzhen), 2011, 1--8, 2011
+
+* Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend, Wang, Fengtao and Zhang, Yangyang and Zhang, Bin and Su, Wensheng, Multimedia and Signal Processing (CMSP), 2011 International Conference on, 12--16, 2011
+
+* A Mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic, Tobon-Mejia, Diego Alejandro and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, Gerard, Automation Science and Engineering (CASE), 2010 IEEE Conference on, 338--343, 2010
+
+* Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Qiu, Hai and Lee, Jay and Lin, Jing and Yu, Gang, Journal of Sound and Vibration, Vol. 289 No. 4, 1066--1090, 2006
+
+[<<返回主目录](../README.md)