├── LICENSE ├── README.md ├── Rotating-machine-fault-data-set.docx ├── doc ├── CWRU.md ├── Connecticut.md ├── FEMTO_ST.md ├── IMS.md ├── MFPT.md ├── Paderborn.md ├── SEU.md └── XJTU_SY.md ├── images ├── CRUW.png ├── fig002.png ├── fig003.png ├── fig004.png ├── fig005.png ├── fig006.png ├── fig007.png ├── fig008.png ├── fig009.png ├── fig01.jpg ├── fig010.png ├── fig011.png ├── fig012.png ├── fig013.jpg ├── fig014.png ├── fig015.png ├── fig016.png ├── fig018.jpg ├── fig019.jpeg └── fig3.jpeg └── papers └── paperList.md /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Cuixiaolong 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | **RoadMap** 2 | 3 | 4 | - [1.简介](#1简介) 5 | - [2.美国-凯斯西储大学轴承数据中心轴承数据集](#2美国-凯斯西储大学轴承数据中心cwru) 6 | - [3.美国-机械故障预防技术学会MFPT](#3美国-机械故障预防技术学会mfpt) 7 | - [4.德国-帕德伯恩大学Paderborn轴承数据集](#4德国-帕德伯恩大学paderborn) 8 | - [5.法国-FEMTO-ST轴承退化数据集](#5法国-femto-st轴承退化数据集) 9 | - [6.美国-辛辛那提大学IMS轴承退化数据集](#6美国-辛辛那提大学ims轴承退化数据) 10 | - [7.美国-康涅狄格大学University of Connecticut齿轮数据集](#7美国-康涅狄格大学university-of-connecticut) 11 | - [8.中国-西安交通大学 轴承加速退化数据集XJTU-SY Bearing Datasets](#8中国-西安交通大学-轴承数据集xjtu-sy-bearing-datasets) 12 | - [9.中国-东南大学齿轮箱数据集](#9东南大学齿轮箱数据集) 13 | - [10.Acoustics and Vibration Database（振动与声学数据库）](#10acoustics-and-vibration-database振动与声学数据库) 14 | - [11.机械设备故障诊断数据集及技术资料大全](#11机械设备故障诊断数据集及技术资料大全) 15 | - [12.美国-宇航局预测数据存储库-CoE Datasets](#12coe-datasets美国宇航局预测数据存储库) 16 | - [13.中国-第三届工业大数据创新竞赛旋转机械数据集](#13第三届工业大数据创新竞赛) 17 | - [14.加拿大-渥太华大学轴承数据集](#14加拿大-渥太华大学) 18 | - [15.意大利-都灵理工大学轴承数据DIRG BearingData](#15-意大利-都灵理工大学轴承数据dirg_bearingdata) 19 | - [16.巴西-里约热内卢联邦大学 MAFAULDA轴承数据集](#16巴西-里约热内卢联邦大学-mafaulda) 20 | - [17.中国-武汉大学-转子数据](#17中国-武汉大学-转子数据) 21 | - [18.中国-电机振动数据(七月在线竞赛)](#18中国-电机振动数据七月在线竞赛) 22 | - [19.中国-轴承数据集(DC竞赛)](#19-中国-轴承数据集dc竞赛) 23 | - [20.中国-上海交通大学轴承数据集](#20-中国-上海交通大学轴承数据集) 24 | 25 | 26 | 27 | # Rotating-machine-fault-data-set 28 | 29 | Open rotating mechanical fault data set 30 | ====== 31 | 32 | ## 1.简介 33 | 众所周知，当下做机械故障诊断研究最基础的就是数据。笔者自2019年初开始致力于收集和整理有价值的机械故障诊断数据。 34 | 35 | 此处分享均为开源数据集，数据来自原始研究方，笔者只整理数据获取途径。如果研究中使用了数据集，请按照版权方要求作出相应说明和引用。 36 | 笔者自己也在筹划整理私有的数据集和研究成果，未来将以适当的方式共享。 37 | 38 | 在此，特别向公开研究数据的研究者表示感谢和致敬，共享是一种精神。如涉及侵权，请联系我删除（787452269@qq.com）。 39 | 40 | 很多优秀的论文都有数据分享，也有越来越多的研究者和企业选择开源自己的成果，本项目保持更新。星标是比较通用的数据集。 41 | 42 | 个别数据集下载可能比较困难或者已经无法下载，需要的可以联系我（邮箱：787452269@qq.com，微信：hustcxl），如版权方有要求，恕不提供。 43 | 44 | 这个仓库自发布以来，得到很多同仁们的好评和认可，来自学术界和工业界的都有。因此个人正在筹划建立一个工业设备预测与健康管理（PHM）交流平台，交流主题包括但不限于： 45 | - 数据集的使用； 46 | - 信号处理与特征提取； 47 | - 智能诊断算法； 48 | - 寿命预测； 49 | - 工业应用案例； 50 | - 行业最紧迫的需求； 51 | - PHM行业发展动态。 52 | 53 | 欢迎有识之士添加我微信或QQ，我将分组加群，欢迎分享宣传自己的成果和见解，也欢迎参与一起学习和讨论。我会不定期就当前研究热点组织相关的专题讨论。线上为主，时机成熟也可考虑组织线下研讨。 54 | 55 | 关于数据使用的问题和心得，欢迎在`Issues`中提问讨论。欢迎`fork`,`Watch`,`star`。 56 | 57 | > 注：给索要数据的朋友，希望是真的试过了无法获取再来索要。伸手党确实不受欢迎。另外，也欢迎提供公开的新数据源。 58 | 59 | ## 2.[☆美国-凯斯西储大学轴承数据中心CWRU）](./doc/CWRU.md) 60 | ![](./images/fig3.jpeg) 61 | 由美国凯斯西储大学完成试验，是当前轴承振动信号处理、故障诊断方面论文使用最为广泛的标准数据集。故障特征明显，可参考的文献资料多。可以作为方法的基础检验数据集。GitHub上也有很多以该数据集为例子的项目，值得借鉴学习。后续会逐渐对该数据集的使用情况进行总结综述。欢迎提供素材。 62 | 63 | ## 3.[☆美国-机械故障预防技术学会MFPT](./doc/MFPT.md) 64 | ![](./images/fig01.jpg) 65 | 由美国机械故障预防技术学会提供，NRG Systems总工程师Eric Bechhoefer博士代表MFPT组装和准备数据，已提供轴承故障数据集以促进轴承分析的研究。 66 | 该数据集包括来自轴承试验台的数据: 67 | - 正常轴承数据 68 | - 不同载荷下的外圈故障数据 69 | - 不同载荷下的内圈故障数据 70 | - 以及三个真实故障案例数据 71 | 72 | ## 4.[☆德国-帕德伯恩大学Paderborn](./doc/Paderborn.md) 73 | ![](./images/fig002.png) 74 | 由德国帕德博恩大学 Christian Lessmeier,Enge-Rosenblatt, Bayer, & Zimmer, 于2014年设计完成实验。 75 | 76 | ## 5.[☆法国-FEMTO-ST轴承退化数据集](./doc/FEMTO_ST.md) 77 | ![](./images/fig003.png) 78 | ![](./images/fig004.png) 79 | 80 | 由FEMTO-ST研究所建立的PHM IEEE 2012数据挑战期间使用的数据集。 81 | 82 | ## 6.[☆美国-辛辛那提大学IMS轴承退化数据](./doc/IMS.md) 83 | ![](./images/fig005.png) 84 | 由美国辛辛那提大学李杰教授团队分享。 85 | The IMS bearing dataset has been collected on an endurance test rig of the University of Cincinnati and relased in 2014. 86 | . The test rig has the following characteristics: 87 | - 4 double row bearings of type Rexnord ZA-2115, 88 | - 2000 rpm stationary speed, 89 | - 6000 lbs load applied onto the shaft and bearing by a spring mechanism, 90 | - PCB 253B33 High sensitivity Quart ICP accelerometers. 91 | 92 | ## 7.[美国-康涅狄格大学University of Connecticut](./doc/Connecticut.md) 93 | ![](./images/fig006.png) 94 | 95 | 由美国康涅狄格大学唐炯教授团队分享。齿轮箱故障数据。 96 | 97 | ## 8.[中国-西安交通大学 轴承数据集XJTU-SY Bearing Datasets](./doc/XJTU_SY.md) 98 | ![](./images/fig007.png) 99 | 100 | 由西安交通大学雷亚国课题组王彪博士整理。为轴承寿命退化数据。 101 | 102 | ## 9.[东南大学齿轮箱数据集](./doc/SEU.md) 103 | ![](./images/fig008.png) 104 | 105 | * github连接：https://github.com/cathysiyu/Mechanical-datasets 106 | 由东南大学严如强团队博士生邵思雨完成。 107 | 论文：**“Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning”** 108 | 109 | Gearbox dataset is from Southeast University, China. 110 | These data are collected from Drivetrain Dynamic Simulator. 111 | This dataset contains 2 subdatasets, including bearing data and gear data, which are both acquired on Drivetrain Dynamics Simulator (DDS). 112 | There are two kinds of working conditions with rotating speed - load configuration set to be 20-0 and 30-2. 113 | Within each file, there are 8rows of signals which represent: 1-motor vibration, 2,3,4-vibration of planetary gearbox in three directions: x, y, and z, 5-motor torque, 6,7,8-vibration of parallel gear box in three directions: x, y, and z. Signals of rows 2,3,4 are all effective. 114 | 115 | ## 10.Acoustics and Vibration Database（振动与声学数据库） 116 | ![](./images/fig013.jpg) 117 | 提供一个收集振动故障数据集的公益性网站链接：http://data-acoustics.com/ 118 | 119 | 120 | ## 11.机械设备故障诊断数据集及技术资料大全 121 | ![](./images/fig014.png) 122 | 123 | 有比较多的机械设备故障数据资料：https://mekhub.cn/machine-diagnosis 124 | 125 | 126 | ## 12.CoE Datasets美国宇航局预测数据存储库 127 | ![](./images/fig015.png) 128 | 129 | * 链接：https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ 130 | 131 | ## 13.第三届工业大数据创新竞赛 132 | ![](./images/fig016.png) 133 | 134 | 需要参赛才能下载数据，数据使用需要获得版权方授权。多台压缩机，汽轮机的转子部件脱落数据。很实用。 135 | 比赛已经结束，无法下载。期待官方发布白皮书。获得授权可以分享数据。 136 | [工业大数据创新平台](http://www.industrial-bigdata.com/Challenge/title?competitionId=VLCSG1DRQB2I02HLQSOSKYH9AS646J8X) 137 | 138 | ## 14.加拿大-渥太华大学 139 | ![](./images/fig009.png) 140 | 141 | 该数据包含在时变转速条件下从不同健康状况的轴承收集的振动信号。总共有36个数据集。对于每个数据集，有两个实验设置：轴承健康状况和变化速度条件。 142 | 轴承的健康状况包括: 143 | （i）健康， 144 | （ii）内圈缺陷有缺陷，以及 145 | （iii）外圈缺陷有缺陷。 146 | 操作转速条件是: 147 | （i）增加速度， 148 | （ii）减小速度， 149 | （iii）增加然后减小速度，以及 150 | （iv）减小然后增加速度。 151 | 因此，设置有12种不同的情况。为了确保数据的真实性，每个实验设置收集3个试验，总共产生36个数据集。 152 | 每个数据集包含两个通道： 153 | 'Channel_1' 是由加速度计测量的振动数据， 154 | 'Channel_2'是由编码器测量的转速数据。 155 | 所有这些数据都以200,000Hz采样，采样持续时间为10秒。 156 | 157 | * 论文链接：(https://www.sciencedirect.com/science/article/pii/S2352340918314124?via%3Dihub) 158 | * 数据链接：(https://data.mendeley.com/datasets/v43hmbwxpm/1) 159 | * 已经发表的论文：[Bearing fault diagnosis under unknown time-varying rotational speed conditions via multiple time-frequency curve extraction](https://www.sciencedirect.com/science/article/pii/S0022460X17307678?via%3Dihub) 160 | * [A method for tachometer-free and resampling-free bearing fault diagnostics under time-varying speed conditions](https://www.sciencedirect.com/science/article/pii/S026322411831011X) 161 | 162 | ## 15. 意大利-都灵理工大学轴承数据DIRG_BearingData 163 | ![](./images/fig010.png) 164 | 165 | 在都灵理工大学机械和航空航天工程系 DIRG实验室设置的钻机收集的数据，专门用于测试高速航空轴承，其加速度采集在可变转速，径向载荷和损伤水平，与温度测量一起，可作为开放存取数据提供。 166 | * 数据链接:(ftp://ftp.polito.it/people/DIRG_BearingData/) 167 | * 论文链接:[The Politecnico di Torino rolling bearing test rig: Description and analysis of open access data](https://www.sciencedirect.com/science/article/pii/S0888327018306800?via%3Dihub) 168 | 169 | ## 16.巴西-里约热内卢联邦大学 MAFAULDA 170 | ![](./images/fig011.png) ![](./images/fig012.png) 171 | * 数据链接：(http://www02.smt.ufrj.br/~offshore/mfs/page_01.html) 172 | * Marins M A, Ribeiro F M L, Netto S L, et al. Improved similarity-based modeling for the classification of rotating-machine failures[J]. Journal of the Franklin Institute, 2018, 355(4): 1913-1930.[论文链接](https://www.sciencedirect.com/science/article/pii/S0016003217303678) 173 | * Saufi S R, bin Ahmad Z A, Leong M S, et al. Differential evolution optimization for resilient stacked sparse autoencoder and its applications on bearing fault diagnosis[J]. Measurement Science and Technology, 2018, 29(12): 125002. [论文链接](https://iopscience.iop.org/article/10.1088/1361-6501/aae5b2/meta) 174 | 175 | ## 17.中国-武汉大学-转子数据 176 | * 数据链接：(https://data.mendeley.com/datasets/p9bsmj4xwg/1) 177 | * Liu, D., et al., Feature extraction of rotor fault based on EEMD and curve code. Measurement, 2019. 135: p. 712-724.[论文链接](https://www.sciencedirect.com/science/article/pii/S0263224118311540?via%3Dihub) 178 | * Description of this data 179 | ''' 180 | These data are denoised signals processed by wavelet thresholding-based denoising. 181 | They are represented by a 2-dimensional matrix. 182 | Each row represents a vibration signal,and the first 45 rows belong to mormal rotor, the second contact-rubbing, the third unbalance and the final misalignment. 183 | Each column represents the length of data, 2048, or time, 1s. 184 | ''' 185 | 186 | ## 18.中国-电机振动数据（七月在线竞赛） 187 | * 数据链接：(http://jingsai.julyedu.com/v/25820185621432447/detail.jhtml) 188 | 189 | ![](./images/fig018.jpg) 190 | 在电机生产线上普遍采用人工听音的方法分辨良、次品，不仅成本高，而且重复、单调的听音工作极易引起人员疲劳，容易出现误判，若个别不良品混入整批成品中，会给工厂带来严重经济损失，甚至严重影响产品声誉。 191 | 192 | 本次大赛要求参赛者基于加速度传感器采集的振动信号，利用机器学习、深度学习等人工智能技术，设计智能检验的算法，要求算法对故障电机不能有漏识别，在召回100%的情况下，尽量提高预测准确率，以达到替代人工质检的目的。 193 | 194 | ## 19. 中国-轴承数据集（DC竞赛） 195 | * 数据链接：(https://www.dcjingsai.com/v2/cmptDetail.html?id=248) 196 | ![](./images/fig019.jpeg) 197 | 198 | [竞赛数据使用及demo](https://github.com/xiaosongshine/bearing_detection_by_conv1d) 199 | 200 | ## 20. 中国-上海交通大学轴承数据集 201 | 202 | * 数据链接：(http://mad-net.org:8765/explore.html?t=0.08906464297121186) 203 | 204 | * 相关论文 205 | 206 | (1)Zhu LM, Ding H, Zhu XY. Synchronous Averaging of Time-frequency Distribution with Application to Machine Condition Monitoring. Trans of the ASME, Journal of Vibration and Acoustics, 2007, 129(4): 441-447. [link](https://asmedigitalcollection.asme.org/vibrationacoustics/article/129/4/441/446861/Synchronous-Averaging-of-Time-Frequency) 207 | 208 | (2)Zhu LM, Ding H, Zhu XY. Extraction of Periodic Signal without External Reference by Time Domain Average Scanning. IEEE Trans. On Industrial Electronics, 2008, 55(2): 918-927.[link](https://ieeexplore.ieee.org/document/4444615) 209 | 210 | [:top:](#Rotating-machine-fault-data-set) 211 | 212 | -------------------------------------------------------------------------------- /Rotating-machine-fault-data-set.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustcxl/Rotating-machine-fault-data-set/e196afa8380aac4d29ce896261b798cdc59c835a/Rotating-machine-fault-data-set.docx -------------------------------------------------------------------------------- /doc/CWRU.md: -------------------------------------------------------------------------------- 1 | # CWRU数据说明 2 | ## 1.概述 3 | 4 | 由美国凯斯西储大学提供。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。 5 | 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。 6 | 7 | 8 | * 数据下载连接(https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website) 9 | 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。 10 | * 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 11 | * R语言中使用的方法：https://github.com/coldfir3/bearing_fault_analysis 12 | 13 | 14 | ## 2.试验条件 15 | For the tests, faults ranging in diameter from 0.007 to 0.028 in. (0.18−0.71 mm) were seeded on the drive- and fan-end bearings (SKF deep-groove ball bearings: 6205-2RS JEM and 6203-2RS JEM, respectively) of the motor using electro-discharge machining (EDM). The faults were seeded on the rolling elements and on the inner and outer races, and each faulty bearing was reinstalled (separately) on the test rig, which was then run at constant speed for motor loads of 0−3 horsepower (approximate motor speeds of 1797−1720 rpm). Table 2 shows the relevant bearing details and fault frequencies. During each test, acceleration was measured in the vertical direction on the housing of the drive-end bearing (DE), and in some tests acceleration was also measured in the vertical direction on the fan-end bearing housing (FE) and on the motor supporting base plate (BA). The sample rates used were 12 kHz for some tests and 48 kHz for others, as explained further in Section 3.2. Further details regarding the test set-up can be found at the CWRU Bearing Data Center website. 16 | 17 | ### Table 2. Bearing details and fault frequencies. 18 | Fault frequencies (multiple of shaft speed) 19 | 20 | | Position on rig | Model number | BPFI | BPFO | FTF | BSF | 21 | | :--- | :--- | :--- | :--- | :--- | :--- | 22 | | Drive end | SKF 6205-2RS JEM | 5.415 | 3.585 | 0.3983 | 2.357 | 23 | | Fan end | SKF 6203-2RS JEM | 4.947 | 3.053 | 0.3816 | 1.994 | 24 | 25 | ## 3.数据使用情况 26 | 选了一些发表在优秀刊物上比较有代表性的论文。当前尚且按照入手的角度不同从基准综述研究，信号处理与特征增强以及分类与模式识别三个方向分类。但是很多论文实际上是相互交叉的。 27 | ### 基准研究 28 | * 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) 29 | * 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) 30 | ### 信号处理与特征工程 31 | 32 | * 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) 33 | 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 34 | 35 | * 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) 36 | 基于双谱的emd应用于非平稳振动信号的轴承故障诊断 37 | 38 | * 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) 39 | 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 40 | 41 | * 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) 42 | 基于熵的故障分类利用西储大学案例数据:一项基准研究 43 | 44 | * 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) 45 | 提出了一种基于经验小波变换的轴承故障诊断方法 46 | 47 | 48 | ### 分类与识别 49 | 50 | * 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) 51 | 利用形态算子和模糊推理对轴承故障进行早期分类 52 | 53 | * 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) 54 | 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 55 | 56 | * 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) 57 | 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 58 | 59 | ## 3.数据特点 60 | 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。 61 | 62 | 63 | [<<返回主目录](../README.md) 64 | -------------------------------------------------------------------------------- /doc/Connecticut.md: -------------------------------------------------------------------------------- 1 | # 美国康涅狄格大学（University of Connecticut）齿轮数据集 2 | 3 | ## 1.简介 4 | 由美国康涅狄格大学唐炯教授团队分享。 5 | * 数据链接:(https://figshare.com/articles/Gear_Fault_Data/6127874/1) 6 | * 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) 7 | * 课题组研究介绍[Dynamics, Sensing, and Controls Lab](https://dscl.uconn.edu/) 8 | 9 | experimental data are collected from a benchmark two-stage gearbox with replaceable 10 | gears as shown in Figure 7. The gear speed is controlled by 11 | a motor. The torque is supplied by a magnetic brake which 12 | can be adjusted by changing its input voltage. A 32-tooth 13 | pinion and an 80-tooth gear are installed on the rst stage 14 | input shaft. The second stage consists of a 48-tooth pinion 15 | and 64-tooth gear. The input shaft speed is measured by a 16 | tachometer, and gear vibration signals are measured by an 17 | accelerometer. The signals are recorded through a dSPACE 18 | system (DS1006 processor board, dSPACE Inc.) with sampling frequency of 20 KHz. As shown in Figure 8, nine 19 | different gear conditions are introduced to the pinion on the 20 | input shaft, including healthy condition, missing tooth, root 21 | crack, spalling, and chipping tip with ve different levels 22 | of severity. 23 | 24 | ## 2.数据使用情况 25 | 26 | 27 | [<<返回主目录](../README.md) 28 | -------------------------------------------------------------------------------- /doc/FEMTO_ST.md: -------------------------------------------------------------------------------- 1 | # FEMTO-ST 轴承退化数据集 2 | 3 | ## 1.简介 4 | 2012年IEEE PHM 比赛数据。 5 | * FEMTO-ST网站：https://www.femto-st.fr/en 6 | * github链接：https://github.com/Lucky-Loek/ieee-phm-2012-data-challenge-dataset 7 | 8 | http://data-acoustics.com/measurements/bearing-faults/bearing-6/ 9 | 10 | ## 2.实验简介 11 | 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. 12 | 13 | 14 | ## 3.使用情况 15 | * Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, 2012[C]. 16 | * 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. 17 | * 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 | 18-21 June 2012. 19 | 20 | 21 | [<<返回主目录](../README.md) 22 | 23 | -------------------------------------------------------------------------------- /doc/IMS.md: -------------------------------------------------------------------------------- 1 | # 美国辛辛那提大学 IMS 轴承数据集 2 | 3 | ## 1.简介 4 | * 数据链接https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ 5 | * IMS链接： http://www.iucrc.org/center/nsf-iucrc-intelligent-maintenance-systems 6 | http://imscenter.net/ 7 | 8 | 9 | ## 2.实验介绍 10 | 11 | An AC motor, coupled by a rub belt, keeps the rotation speed constant. The four 12 | bearings are in the same shaft and are forced lubricated by a circulation system that 13 | regulates the flow and the temperature. It is announced on the provided “Readme 14 | Document for IMS Bearing Data” in the downloaded file, that the test was stopped 15 | when the accumulation of debris on a magnetic plug exceeded a certain level indicating 16 | the possibility of an impending failure. 17 | The four bearings are all of the same type. There are double range pillow blocks 18 | rolling elements bearing. 19 | 20 | Three (3) data sets are included in the data packet (IMS-Rexnord Bearing Data.zip). Each data set 21 | describes a test-to-failure experiment. Each data set consists of individual files that are 1-second 22 | vibration signal snapshots recorded at specific intervals. Each file consists of 20,480 points with the 23 | sampling rate set at 20 kHz. The file name indicates when the data was collected. Each record (row) in 24 | the data file is a data point. Data collection was facilitated by NI DAQ Card 6062E. Larger intervals of 25 | time stamps (showed in file names) indicate resumption of the experiment in the next working day. 26 | 27 | ### Table 1. Bearing characteristics 28 | - Rexnord ZA-2115 Characteristics 29 | 30 | | Parameter name | imperial | metric| 31 | | :---: | :----- | :---- | 32 | | Pitch diameter | 2.815 inch | 71.5mm | 33 | | Rolling element diameter | 0.331 inch | 8.4mm | 34 | | Number of rolling element per row | 16 | 16 | 35 | | Contact angle | 15.17° | 15.17° | 36 | | Static load | 6000 lbs | 26690 N | 37 | 38 | 39 | 40 | 41 | ### Set No. 1: 42 | - Recording Duration: October 22, 2003 12:06:24 to November 25, 2003 23:39:56 43 | - No. of Files: 2,156 44 | - No. of Channels: 8 45 | - Channel Arrangement: Bearing 1 – Ch 1&2; Bearing 2 – Ch 3&4; 46 | Bearing 3 – Ch 5&6; Bearing 4 – Ch 7&8. 47 | - File Recording Interval: Every 10 minutes (except the first 43 files were taken every 5 minutes) 48 | - File Format: ASCII 49 | - Description: At the end of the test-to-failure experiment, inner race defect occurred in bearing 3 and roller element defect in bearing 4. 50 | 51 | ### Set No. 2: 52 | - Recording Duration: February 12, 2004 10:32:39 to February 19, 2004 06:22:39 53 | - No. of Files: 984 54 | - No. of Channels: 4 55 | - Channel Arrangement: Bearing 1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing 4 – Ch 4. 56 | - File Recording Interval: Every 10 minutes 57 | - File Format: ASCII 58 | - Description: At the end of the test-to-failure experiment, outer race failure occurred in 59 | bearing 1. 60 | 61 | ### Set No. 3 62 | - Recording Duration: March 4, 2004 09:27:46 to April 4, 2004 19:01:57 63 | - No. of Files: 4,448 64 | - No. of Channels: 4 65 | - Channel Arrangement: Bearing1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing4 – Ch4; 66 | - File Recording Interval: Every 10 minutes 67 | - File Format: ASCII 68 | - Description: At the end of the test-to-failure experiment, outer race failure occurred in 69 | bearing 3. 70 | 71 | ### Table 2. Datasets description 72 | | | Number of files | Number of channels | Endurance duration | Duration of recorded signal | Announced damages at the end of the endurance | 73 | | :---: | :---: | :---: | :---: | :---: | :---: | 74 | | Dataset 1 | 2156 | 8 | 49680 min 34 days 12h | 36 min | Bearing 3: inner race Bearing 4: rolling element | 75 | | Dataset 2 | 984 | 4 | 9840 min 6 days 20h | 16 min | Bearing 1: outer race | 76 | | Dataset 3 | 4448 | 4 | 44480 min 31 days 10h | 74 min | Bearing 3: outer race | 77 | 78 | ### Table 3. Characteristic frequencies of the test rig 79 | 80 | | Characteristic frequencies | | 81 | | :--- | :--- | 82 | | Shaft frequency | 33.3 Hz | 83 | | Ball Pass Frequency Outer race (BPFO) | 236 Hz | 84 | | Ball Pass Frequency Inner race (BPFI) | 297 Hz | 85 | | Ball Spin Frequency (BSF) | 278Hz (2x139 Hz) | 86 | | Fundamental Train Frequency (FTF) | 15 Hz | 87 | 88 | 89 | ## 3.使用情况 90 | 91 | * 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]. 92 | * 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. 93 | 94 | * 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 95 | 96 | * 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 97 | 98 | * 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 99 | 100 | * 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 101 | 102 | * 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 103 | 104 | * Logical analysis of maintenance and performance data of physical assets, ID34, Yacout, S, Reliability and Maintainability Symposium (RAMS), 2012 Proceedings-Annual, 1--6, 2012 105 | 106 | * 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 107 | 108 | * 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 109 | 110 | * 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 111 | 112 | * 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 113 | 114 | * 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 115 | 116 | * 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 117 | 118 | [<<返回主目录](../README.md) 119 | -------------------------------------------------------------------------------- /doc/MFPT.md: -------------------------------------------------------------------------------- 1 | # MFPT 轴承数据集说明 2 | 3 | ## 1.概述 4 | NRG Systems总工程师Eric Bechhoefer博士代表MFPT组装和准备数据。 5 | * 数据链接：(https://mfpt.org/fault-data-sets/) 6 | * 声学和振动数据库链接(http://data-acoustics.com/measurements/bearing-faults/bearing-2/) 7 | * MATLAB 文档关于MFPT轴承数据的故障诊断举例。 8 | 连接(https://ww2.mathworks.cn/help/predmaint/examples/Rolling-Element-Bearing-Fault-Diagnosis.html) 9 | 使用该数据集的相比于CWRU少一些。于2012年更新。 10 | 11 | ## 2.试验说明 12 | The test rig was equipped with a NICE bearing with the following parameters: 13 | 14 | - Roller diameter: rd = 0.235 15 | - Pitch diameter: pd = 1.245 16 | - Number of elements: ne = 8 17 | - Contact angle: ca = 0 18 | 19 | The data set1,2 comprises the following, and can be downloaded as a zip file package he:Fault Data Sets 20 | 21 | - 3 baseline conditions: 270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps, for 6 seconds 22 | - 3 outer race fault conditions: 270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps for 6 seconds 23 | - 7 outer race fault conditions: 25, 50, 100, 150, 200, 250 and 300 lbs of load, input shaft rate 25 Hz, sample rate of 48,828 sps for 3 seconds (bearing resonance was found be less than 20 kHz) 24 | - 7 inner race fault conditions: 0, 50, 100, 150, 200, 250 and 300 lbs of load, input shaft rate of 25 Hz, sample rate of 48,828 sps for 3 seconds 25 | - 5 data analysis (.m) files that relate to Eric Bechhoefer’s introductory paper referred to below 26 | - Three2 real world example files are also included: an intermediate shaft bearing from a wind turbine (data structure holds bearing rates and shaft rate), an oil pump shaft bearing from a wind turbine, and a real world planet bearing fault). 27 | 28 | - Note1: The data is stored in a Matlab® double-precision, binary format *.mat file. The data structure holds the load, shaft rate, sample rate and a vector of “g” data. 29 | 30 | - Note2: The initial data uploaded to the website in October 2012 included errors, in that the sample rate was defined as 50 Hz, when in fact it was 25 Hz. New data sets correcting this error were uploaded on 27 Feb 13. In addition, a third real world example was added] 31 | ## 3.使用情况 32 | * Lee D, Siu V, Cruz R, et al. Convolutional neural net and bearing fault analysis[C]//Proceedings of the International Conference on Data Mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 194.[论文链接](https://pdfs.semanticscholar.org/6e45/f39b1e50cfd10deaabd1d786fac827c3543a.pdf) 33 | 34 | * Lessmeier C, Kimotho J K, Zimmer D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification[C]//Proceedings of the European conference of the prognostics and health management society. 2016: 05-08.07.[论文链接](https://pdfs.semanticscholar.org/79c0/7f2be8dd894deb572070f674e514d3dd1caa.pdf) 35 | 利用电机电流信号监测机电传动系统轴承损坏情况:数据驱动分类的基准数据集 36 | 对CWRU: Bearing Data Center/ Seeded Fault Test Data，FEMTO Bearing Data Set，MFPT Fault Data Sets，Bearing Data Set IMS四个数据集进行了分析和介绍。 37 | 38 | * Verstraete D, Ferrada A, Droguett E L, et al. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings[J]. Shock and Vibration, 2017, 2017.[论文链接](https://www.hindawi.com/journals/sv/2017/5067651/abs/) 39 | 通过对滚动轴承的时频图像分析深度学习实现了故障诊断 40 | 41 | * Yu H, Wang K, Li Y. Multiscale Representations Fusion With Joint Multiple Reconstructions Autoencoder for Intelligent Fault Diagnosis[J]. IEEE Signal Processing Letters, 2018, 25(12): 1880-1884.[论文链接](https://ieeexplore.ieee.org/abstract/document/8513874) 42 | 43 | * Sobie C, Freitas C, Nicolai M. Simulation-driven machine learning: Bearing fault classification[J]. Mechanical Systems and Signal Processing, 2018, 99: 403-419. [论文链接](https://www.sciencedirect.com/science/article/pii/S0888327017303357) 44 | 45 | * Li H, Zhao J, Liu J, et al. Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox[J]. Journal of Vibroengineering, 2016, 18(8).[论文链接](http://web.b.ebscohost.com/ehost/detail/detail?vid=0&sid=8cbc911d-7aff-49ef-8ba4-b2665a2fcf1f%40pdc-v-sessmgr03&bdata=Jmxhbmc9emgtY24mc2l0ZT1laG9zdC1saXZl#AN=120525722&db=aph) 46 | 47 | * Barbini L, Ompusunggu A P, Hillis A J, et al. Phase editing as a signal pre-processing step for automated bearing fault detection[J]. Mechanical Systems and Signal Processing, 2017, 91: 407-421.[论文链接](https://www.sciencedirect.com/science/article/pii/S0888327016305192#b0095) 48 | 49 | ## 4.数据特点 50 | 51 | [<<返回主目录](../README.md) 52 | -------------------------------------------------------------------------------- /doc/Paderborn.md: -------------------------------------------------------------------------------- 1 | # 德国 帕德博恩大学 Bearing DataCenter 2 | 3 | ## 1.简介 4 | 数据下载链接：[https://groups.uni-paderborn.de/kat/BearingDataCenter/](https://groups.uni-paderborn.de/kat/BearingDataCenter/) 5 | 研究所和KAt-DataCenter链接的名称：Christian Lessmeier等，KAt-DataCenter：mb.uni-paderborn.de/kat/datacenter， 6 | 设计和驱动技术主席，帕德博恩大学。如需商业用途，请联系作者。 7 | 8 | 9 | ## 2.试验条件 10 | 11 | The test rig consists of several modules: an electric motor 12 | (1), a torque-measurement shaft (2), a rolling bearing test 13 | module (3), a flywheel (4) and a load motor (5), see 14 | Figure 4. The ball bearings with different types of damage 15 | are mounted in the bearing test module to generate the 16 | experimental data. 17 | The rolling bearing module provides the possibility of using 18 | a test bearing under a constant radial load, which can be 19 | continuously adjusted up to 10 kN before each experiment. 20 | An adapter gives the possibility to measure the vibration of 21 | the inner housing, which holds the test bearing in the main 22 | direction of the load. The precise design of the bearing 23 | module and additional features, such as the possibility to 24 | simulate tilting faults or the use of roller bearings, are 25 | described by Lessmeier, Enge-Rosenblatt, Bayer, & 26 | Zimmer, 2014. 27 | The motor (1) is a 425 W Permanent Magnet Synchronous 28 | Motor (PMSM) with a nominal torque of T = 1.35 Nm, a 29 | nominal speed of n = 3,000 rpm, a nominal current of 30 | I = 2.3 A and a pole pair number p = 4 (Type SD4CDu8S-009, Hanning Elektro-Werke GmbH & Co. KG). It is 31 | operated by a frequency inverter (KEB Combivert 07F5E 32 | 1D-2B0A) with a switching frequency of 16 kHz. This 33 | standard industrial inverter is used to provide conditions 34 | similar to motors used in the industry because the current 35 | signals show significant noise due to the pulse-width 36 | modulation of the inverter. (Lessmeier, Piantsop Mbo'o, 37 | Coenen, Zimmer, & Hameyer, 2012) 38 | Figure 5 shows the schema of the measurement procedure 39 | and the recorded measurands. The motor phase currents are 40 | measured by a current transducer of the type LEM CKSR 41 | 15-NP with an accuracy of 0.8 % of I 42 | PN = 15 A. The MCS 43 | are then filtered by a 25 kHz low-pass filter and converted 44 | from an analogue to a digital signal with a sampling rate of 45 | 64 kHz. The current transducers are used instead of the 46 | internal ammeters of the inverter because of their easy 47 | signal access as the currents can be measured externally 48 | between motor and inverter. 49 | 50 | At this scientific level of development, a high sampling rate 51 | and accuracy are additional advantages of this setup. 52 | Nevertheless, the used transducers are similar to the ones 53 | commonly used in industry applications, so that few 54 | difficulties are expected transferring the research outcomes 55 | to industrial CM systems. 56 | 57 | The acceleration of the bearing housing is measured at the 58 | adapter at the top end of the rolling bearing module using a 59 | piezoelectric accelerometer (Model No. 336C04, PCB 60 | Piezotronics, Inc.) and a charge amplifier (Type 5015A, 61 | Kistler Group) with a low-pass filter at 30 kHz. The signal 62 | is digitalized and saved synchronously to the MCS with a 63 | sampling rate of 64 kHz. 64 | 65 | The flywheel and the load machine simulate inertia and load 66 | of the driven equipment, respectively. The load motor is a 67 | PMSM with a nominal torque of 6 Nm (power of 1.7 kW). 68 | To record the operating conditions the following additional 69 | parameters are measured synchronously to the motor 70 | currents and vibration signal but with lower sampling rates: 71 | the radial force on the bearings (Compression and Tension 72 | Force Sensor Type K11, Lorenz, 10 kN), the load torque at 73 | the torque-measuring shaft, the rotational speed (Torque 74 | Transducer Model 305, Magtrol, 2 Nm) and the oil 75 | temperature in the bearing module. 76 | 77 | The rotational speed of the drive system, the radial force 78 | onto the test bearing and the load torque in the drive train 79 | are the main operation parameters. To ensure comparability 80 | of the experiments, fixed levels were defined for each 81 | parameter (Table 6). All three parameters were kept 82 | constant for the time of each measurement. At the basic 83 | setup (Set no. 0) of the operation parameters, the test rig 84 | runs at n = 1,500 rpm with a load torque of M = 0.7 Nm and 85 | a radial force on the bearing of F = 1,000 N. Three 86 | additional settings are used by reducing the parameters one 87 | by one to n = 900 rpm, M = 0.1 Nm and F = 400 N (set No. 88 | 1-3), respectively. For each of the settings, 20 measurements 89 | of 4 seconds each were recorded. Another parameter is the 90 | temperature, which was kept roughly at 45 -50 °C during all 91 | experiments. 92 | 93 | ### Table 6. Operating parameters 94 | 95 | | No. | Rotational speed [rpm] | Load Torque [Nm] | Radial force [N] | Name of Setting | 96 | | :---: | :---: | :---: | :---: | :---: | 97 | | 0 | 1500 | 0.7 | 1000 | N15_M07_F10 | 98 | | 1 | 900 | 0.7 | 1000 | N09_M07_F10 | 99 | | 2 | 1500 | 0.1 | 1000 | N15_M01_F10 | 100 | | 3 | 1500 | 0.7 | 400 | N15_M07_F04 | 101 | 102 | ### Table 7. Operating parameter of healthy (undamaged) bearings during run-in period. 103 | 104 | | Bearing Code | Run-in Period [h] | Radial Load [N] | Speed [min^-1] | 105 | | :---: | :---: | :---: | :---: | 106 | | K001 | >50 | 1000-3000 | 1500-2000 | 107 | | K002 | 19 | 3000 | 2900 | 108 | | K003 | 1 | 3000 | 3000 | 109 | | K004 | 5 | 3000 | 3000 | 110 | | K005 | 10 | 3000 | 3000 | 111 | | K006 | 16 | 3000 | 2900 | 112 | 113 | ## 3.使用情况 114 | * Bin Hasan M. Current based condition monitoring of electromechanical systems. Model-free drive system current monitoring: faults detection and diagnosis through statistical features extraction and support vector machines classification.[D]. University of Bradford, 2013. 115 | * Lessmeier C, Kimotho J K, Zimmer D, et al. Condition monitoring of bearing damage in electromechanical drive systems by 116 | using motor current signals of electric motors: a benchmark data set for data-driven classification: Proceedings of the 117 | European conference of the prognostics and health management society, 2016[C].[论文链接](https://mb.uni-paderborn.de/fileadmin/kat/PDF/Veroeffentlichungen/20160703_PHME16_CM_bearing.pdf) 118 | 119 | * Pandhare V, Singh J, Lee J. Convolutional Neural Network Based Rolling-Element Bearing Fault Diagnosis for Naturally Occurring and Progressing Defects Using Time-Frequency Domain Features[C]//2019 Prognostics and System Health Management Conference (PHM-Paris). IEEE, 2019: 320-326.[论文链接](https://ieeexplore.ieee.org/abstract/document/8756423) 120 | 121 | * Zhu Z, Peng G, Chen Y, et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis[J]. Neurocomputing, 2019, 323: 62-75.[论文链接](https://www.sciencedirect.com/science/article/pii/S0925231218311238) 122 | 基于强泛化胶囊网络的卷积神经网络用于轴承故障诊断 123 | 124 | * Chen Y, Peng G, Xie C, et al. ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis[J]. Neurocomputing, 2018, 294: 61-71.[论文链接](https://www.sciencedirect.com/science/article/pii/S092523121830300X) 125 | ACDIN:弥补人工与真实轴承损伤之间的差距，用于轴承故障诊断 126 | 127 | * Wu, J., et al., Sensors Information Fusion System with Fault Detection Based on Multi-Manifold Regularization Neighborhood Preserving Embedding. Sensors, 2019. 19(6): p. 1440. [论文链接](https://www.mdpi.com/1424-8220/19/6/1440) 128 | 基于多流形正则化保邻域嵌入的传感器信息融合系统 129 | 130 | 131 | ## 4.数据特点 132 | 133 | 134 | [<<返回主目录](../README.md) 135 | -------------------------------------------------------------------------------- /doc/SEU.md: -------------------------------------------------------------------------------- 1 | # 东南大学齿轮箱数据集 2 | 3 | ## 1.简介 4 | * github连接：https://github.com/cathysiyu/Mechanical-datasets 5 | 6 | 由东南大学严如强团队博士生邵思雨完成 7 | 8 | Gearbox dataset is from Southeast University, China. These data are collected from Drivetrain Dynamic Simulator. This dataset contains 2 subdatasets, including bearing data and gear data, which are both acquired on Drivetrain Dynamics Simulator (DDS). There are two kinds of working conditions with rotating speed - load configuration set to be 20-0 and 30-2. Within each file, there are 8rows of signals which represent: 1-motor vibration, 2,3,4-vibration of planetary gearbox in three directions: x, y, and z, 5-motor torque, 6,7,8-vibration of parallel gear box in three directions: x, y, and z. Signals of rows 2,3,4 are all effective. 9 | 10 | 11 | ## 2.数据使用情况 12 | * Shao S, McAleer S, Yan R, et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2018, 15(4): 2446-2455.[论文链接](https://ieeexplore.ieee.org/document/8432110) 13 | 14 | [<<返回主目录](../README.md) 15 | -------------------------------------------------------------------------------- /doc/XJTU_SY.md: -------------------------------------------------------------------------------- 1 | # 西安交通大学轴承数据集 2 | 3 | ## 1.简介 4 | 5 | 由西安交通大学雷亚国课题组王彪博士整理。 6 | 7 | * 数据链接：(http://biaowang.tech/xjtu-sy-bearing-datasets/) 8 | XJTU-SY bearing datasets can be downloaded by one of the following links: 9 | * Website: 10 | http://biaowang.tech 11 | 12 | * Google Drive: 13 | https://drive.google.com/open?id=1_ycmG46PARiykt82ShfnFfyQsaXv3_VK 14 | 15 | * Dropbox: 16 | https://www.dropbox.com/sh/qka3b73wuvn5l7a/AADr6oXKbafhOlrBLCNgonzua?dl=0 17 | 18 | * MediaFire: 19 | http://www.mediafire.com/folder/m3sij67rizpb4/XJTU-SY_Bearing_Datasets 20 | 21 | * MEGA: 22 | https://mega.nz/#F!H7pnGKBK!PR8qUShaLlJjwrPf3SlBjw 23 | 24 | * Baidu Netdisk: 25 | https://pan.baidu.com/s/1OaY82azTXHBwjiCjA_jRcw 26 | 27 | If you have any questions or suggestions, do not hesitate to contact: 28 | Mr. Biao Wang, wangbiaoxjtu@outlook.com 29 | 30 | 31 | 本次实验所用的轴承加速退化测试平台由联合实验室设计，昇阳科技制造。该平台可以开展各类滚动轴承或滑动轴承的加速退化实验，获取轴承的全寿命周期监测数据。本次实验对象为LDK UER204滚动轴承，共设计了三类实验工况（35Hz12kN/37.5Hz11kN/40Hz10kN），每类工况下各测试5个轴承。 32 | 33 | ### TABLE I OPERATINGCONDITIONS OF THETESTEDBEARINGS 34 | 35 | | Operating condition | Radial force(kN) | Rotating speed (rpm) | Bearing dataset | 36 | | :---: | :---: | :--- | :---| 37 | | Condition 1 | 12 | 2100 | Bearing 1_1 Bearing 1_2 Bearing 1_3 Bearing 1_4 Bearing 1_5| 38 | | Condition 2 | 11 | 2250 | Bearing 2_1 Bearing 2_2 Bearing 2_3 Bearing 2_4 Bearing 2_5| 39 | | Condition 3 | 10 | 2400 | Bearing 3_1 Bearing 3_2 Bearing 3_3 Bearing 3_4 Bearing 3_5| 40 | 41 | 42 | 43 | ## 2.数据使用情况 44 | 45 | * B. W, Y. L, N. L, et al. A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings[J]. IEEE Transactions on Reliability, 2018:1-12.[论文链接](https://ieeexplore.ieee.org/abstract/document/8576668) 46 | * Yaguo, L., et al., XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial. Journal of Mechanical Engineering, 2019. 55(16): p. 1.[link](http://www.cjmenet.com.cn/CN/10.3901/JME.2019.16.001) 47 | 48 | [<<返回主目录](../README.md) 49 | -------------------------------------------------------------------------------- /images/CRUW.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustcxl/Rotating-machine-fault-data-set/e196afa8380aac4d29ce896261b798cdc59c835a/images/CRUW.png -------------------------------------------------------------------------------- /images/fig002.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/hustcxl/Rotating-machine-fault-data-set/e196afa8380aac4d29ce896261b798cdc59c835a/images/fig002.png -------------------------------------------------------------------------------- /images/fig003.png: -------------------------------------------------------------------------------- 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