轉自：https://blog.csdn.net/hustcxl/article/details/89394428
旋轉機械故障診斷公開數據集整理
眾所周知，當下做機械故障診斷研究最基礎的就是數據，再先進的方法也離不開數據的檢驗。筆者通過文獻資料收集到如下幾個比較常用的數據集並進行整理。鑒於目前尚未見比較全面的數據集整理介紹。數據來自原始研究方，筆者只整理數據獲取途徑。如果研究中使用了數據集，請按照版權方要求作出相應說明和引用。在此，公開研究數據的研究者表示感謝和致敬。如涉及侵權，請聯系我刪除（787452269@qq.com）。歡迎相關領域同仁一起交流。很多優秀的論文都有數據分享，本項目保持更新。星標是比較通用的數據集。個別數據集下載可能比較困難，需要的可以郵件聯系我，如版權方有要求，述不提供。本文在github地址為旋轉機械故障數據集
1.☆CWRU（凱斯西儲大學軸承數據中心）
數據下載連接（https://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website）
CWRU數據集是使用最為廣泛的，文獻較多。不一一舉例。其中University of New South Wales 的Wade A. Smith在2015年進行了比較全面的總結和對比[1]。比較客觀的綜述和分析了使用數據進行診斷和分析研究的情況。官方網站提供的是.mat格式的數據，MATLAB直接使用比較方便。
Github上有人分享了在python中自動下載和使用的方法。https://github.com/Litchiware/cwru
R語言中使用的方法：https://github.com/coldfir3/bearing_fault_analysis
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.
2.☆MFPT（機械故障預防技術學會）
NRG Systems總工程師Eric Bechhoefer博士代表MFPT組裝和准備數據。
數據鏈接：（https://mfpt.org/fault-data-sets/）
聲學和振動數據庫鏈接（http://data-acoustics.com/measurements/bearing-faults/bearing-2/）
MATLAB 文檔關於MFPT軸承數據的故障診斷舉例。
連接（https://ww2.mathworks.cn/help/predmaint/examples/Rolling-Element-Bearing-Fault-Diagnosis.html）
使用該數據集的相比於CWRU少一些。2012年更新。
一些對數據描述的論文[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), 2016: 194.
3.☆德國Paderborn大學
鏈接：https://mb.uni-paderborn.de/kat/forschung/datacenter/bearing-datacenter/
相關說明及論文[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, 2013.
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, 2016[C].
4.☆FEMTO-ST軸承數據集
由FEMTO-ST研究所建立的PHM IEEE 2012數據挑戰期間使用的數據集[5-7]。
FEMTO-ST網站：https://www.femto-st.fr/en
github鏈接：https://github.com/wkzs111/phm-ieee-2012-data-challenge-dataset
http://data-acoustics.com/measurements/bearing-faults/bearing-6/
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.
5.☆辛辛那提IMS
數據鏈接https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
相關論文[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: 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.
6.University of Connecticut
數據鏈接：https://figshare.com/articles/Gear_Fault_Data/6127874/1
數據描述：
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.
相關論文[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, 2018,6:26241-26253.
7.XJTU-SY Bearing Datasets（西安交通大學 軸承數據集）
由西安交通大學雷亞國課題組王彪博士整理。
鏈接：http://biaowang.tech/xjtu-sy-bearing-datasets/
使用數據集的論文[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, 2018:1-12.
8.東南大學
github連接：https://github.com/cathysiyu/Mechanical-datasets
由東南大學嚴如強團隊博士生邵思雨完成[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（振動與聲學數據庫）
提供一個手機振動故障數據集的公益性網站鏈接：http://data-acoustics.com/
10.機械設備故障診斷數據集及技術資料大全
有比較多的機械設備故障數據資料：https://mekhub.cn/machine-diagnosis
11.CoE Datasets美國宇航局預測數據存儲庫
鏈接：https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/
[藻類跑道數據集] [CFRP復合材料數據集] [銑削數據集]
[軸承數據集] [電池數據集] [渦輪風扇發動機退化模擬數據集] [PHM08挑戰數據集] [IGBT加速老化Sata集] [投石機]數據集] [FEMTO軸承數據組] [隨機電池使用數據組] [電容器電應力數據組] [MOSFET熱過載時效數據組] [電容器電應力數據組 - 2] [HIRF電池數據組]
參考文獻
[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, 2015,64-65:100-131.
[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, 2017,2017.
[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, 2013.
[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, 2016[C].
[5] Porotsky S, Bluvband Z. Remaining useful life estimation for systems with non-trendability behaviour: Prognostics & Health Management, 2012[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., 2012[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: 2012 IEEE Conference on Prognostics and Health Management, 2012[C].2012
18-21 June 2012.
[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: CM2016, 2016[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, 2006,289(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, 2018,6:26241-26253.
[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, 2018:1-12.
[12] S. S, S. M, R. Y, et al. Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning[J]. IEEE Transactions on Industrial Informatics, 2019,15(4):2446-2455.