Update MFPT.md

doc/MFPT.md

@@ -6,12 +6,28 @@ NRG Systems总工程师Eric Bechhoefer博士代表MFPT组装和准备数据。
 * 声学和振动数据库链接(http://data-acoustics.com/measurements/bearing-faults/bearing-2/)
 * MATLAB 文档关于MFPT轴承数据的故障诊断举例。
 连接(https://ww2.mathworks.cn/help/predmaint/examples/Rolling-Element-Bearing-Fault-Diagnosis.html)
-使用该数据集的相比于CWRU少一些。2012年更新。
+使用该数据集的相比于CWRU少一些。于2012年更新。
 
 ## 2.试验说明
 
 ## 3.使用情况
 * 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)
+
+* 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)  
+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  
+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。
+
+* 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/)
+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  
+
+* 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)
+
+* 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)
+
+* 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)
+
+* 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)
+
 ## 4.数据特点
 
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