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并進行整理。鑒于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組裝和準備數據。

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 文檔關于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于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.

總結

以上是生活随笔為你收集整理的旋转机械故障诊断公开数据集整理的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。