旋转机械故障诊断公开数据集整理
众所周知，当下做机械故障诊断研究最基础的就是数据，再先进的方法也离不开数据的检验。笔者通过文献资料收集到如下几个比较常用的数据集并进行整理。鉴于目前尚未见比较全面的数据集整理介绍。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。在此，公开研究数据的研究者表示感谢和致敬。如涉及侵权，请联系我删除（787452269@）。欢迎相关领域同仁一起交流。很多优秀的论文都有数据分享，本项目保持更新。星标是比较通用的数据集。个别数据集下载可能比较困难，需要的可以邮件联系我，如版权方有要求，述不提供。本文在github地址为旋转机械故障数据集
1.☆CWRU（凯斯西储大学轴承数据中心）
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在进行了比较全面的总结和对比[1]。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。/Litchiware/cwruR语言中使用的方法：/coldfir3/bearing_fault_analysisSmith 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, ,64-65:100-131.
2.☆MFPT（机械故障预防技术学会）
NRG Systems总工程师Eric Bechhoefer博士代表MFPT组装和准备数据。
数据链接：（/fault-data-sets/）声学和振动数据库链接（http://data-/measurements/bearing-faults/bearing-2/）MATLAB 文档关于MFPT轴承数据的故障诊断举例。
连接（/help/predmaint/examples/Rolling-Element-Bearing-Fault-Diagnosis.html）
使用该数据集的相比于CWRU少一些。更新。
一些对数据描述的论文[2]。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), : 194.
3.☆德国Paderborn大学
链接：https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/相关说明及论文[3, 4]。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, .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: Proceedings of the European conference of the prognostics and health management society, [C].4.☆FEMTO-ST轴承数据集
由FEMTO-ST研究所建立的PHM IEEE 数据挑战期间使用的数据集[5-7]。FEMTO-ST网站：https://www.femto-st.fr/engithub链接：/wkzs111/phm-ieee--data-challenge-datasethttp://data-/measurements/bearing-faults/bearing-6/Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, [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., [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: IEEE Conference on Prognostics and Health Management, [C].
18-21 June .
5.☆辛辛那提IMS
数据链接https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/相关论文[8, 9]。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: CM, [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, ,289(4):1066-1090.6.University of Connecticut
数据链接：/articles/Gear_Fault_Data/6127874/1数据描述：Time domain gear fault vibration data (DataForClassification_TimeDomain)
And Gear fault data after angle-frequency domain synchronous analysis (DataForClassification_Stage0)
Number of gear fault types=9={‘healthy’,‘missing’,‘crack’,‘spall’,‘chip5a’,‘chip4a’,‘chip3a’,‘chip2a’,‘chip1a’}
Number of samples per type=104
Number of total samples=9x104=903
The data are collected in sequence, the first 104 samples are healthy, 105th ~208th samples are missing, and etc.相关论文[10]。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, ,6:26241-26253.
7.XJTU-SY Bearing Datasets（西安交通大学 轴承数据集）
由西安交通大学雷亚国课题组王彪博士整理。
链接：http://biaowang.tech/xjtu-sy-bearing-datasets/使用数据集的论文[11]。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, :1-12.
8.东南大学
github连接：/cathysiyu/Mechanical-datasets由东南大学严如强团队博士生邵思雨完成[12]。“Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning”
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.Acoustics and Vibration Database（振动与声学数据库）
提供一个手机振动故障数据集的公益性网站链接：http://data-/
10.机械设备故障诊断数据集及技术资料大全
有比较多的机械设备故障数据资料：/machine-diagnosis
11.CoE Datasets美国宇航局预测数据存储库
链接：https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/[藻类跑道数据集] [CFRP复合材料数据集] [铣削数据集]
[轴承数据集] [电池数据集] [涡轮风扇发动机退化模拟数据集] [PHM08挑战数据集] [IGBT加速老化Sata集] [投石机]数据集] [FEMTO轴承数据组] [随机电池使用数据组] [电容器电应力数据组] [MOSFET热过载时效数据组] [电容器电应力数据组 - 2] [HIRF电池数据组]
参考文献
[1]mith 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, ,64-65:100-131.[2]rstraete 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, ,.[3] 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, .[4] 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: Proceedings of the European conference of the prognostics and health management society, [C].[5] Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, [C].[6] 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., [C]. IEEE Catalog Number: CPF12PHM-CDR.[7] E. S, H. O, A. S S V, et al. Estimation of remaining useful life of ball bearings using data driven methodologies: IEEE Conference on Prognostics and Health Management, [C].18-21 June .[8] 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: CM, [C].[9] 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, ,289(4):1066-1090.[10] 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, ,6:26241-26253.[11] 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, :1-12.[12] S. S, S. M, R. Y, et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, ,15(4):2446-2455.
转载自/hustcxl/article/details/89394428
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