科学研究：

研究方向：

主要从事机电系统设计与控制的研究。

承担科研项目情况：

1. 国家自然科学基金项目：基于小波理论和多时间尺度建模的旋转部件健康监测研究。

2. 教育部新世纪优秀人才计划项目：基于无线传感器网络的复杂制造系统与制造过程监测与控制。

3. 航空科学基金 项目：基于小波分析的航空发动机转静碰摩故障分析探索研究。

4. Canrig Drilling Technology Ltd，USA ：基于先进数字信号处理技术的石油勘探设备监测与诊断。

5. 东南大学校级SRTP项目：智能花盆设计。

科研成果：

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发明专利：

1.  基于多尺度包络频谱的机械状态监测及健康诊断方法 (Multi-Scale Enveloping Spectrogram (MuSEnS) signal processing for condition monitoring and the like), 美国专利No. 7602985, 2009年10月13日颁发, R. Gao and R. Yan.

论文专著：

近几年来在国际期刊和国际会议上发表论文20余篇。

出版专著：

“Wavelets: Theory and Applications for Manufacturing”，Springer-Verlag出版公司。

发表中文论文：

1 基于相关测度的小波选取及其在转子轴承故障诊断中的应用 严如强; 高晓旸 东南大学仪器科学与工程学院; 康涅狄格大学机械工程系 【会议】第十二届全国设备故障诊断学术会议论文集 2010-08-15

2 一种新型节能无线传感节点的设计与组网 孙行行; 卢伟; 钱宇宁; 严如强 东南大学仪器科学与工程学院 【期刊】南京信息工程大学学报(自然科学版) 2011-10-28

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English：

1 R. Yan and R. Gao, “Wavelet domain principal feature analysis for spindle health diagnosis”, Structural Health Monitoring, DOI: 10.1177/1475921710395806.

2 A. Malhi, R. Yan , and R. Gao, “Prognosis of defect propagation based on recurrent neural networks”, IEEE Transactions on Instrumentation and Measurement , Vol. 60, No. 3, pp. 703-711, March 2011.

3 J. Zhang, R. Yan , R. Gao, and Z. Feng, “Performance enhancement of ensemble empirical mode decomposition”, Mechanical Systems and Signal Processing, Vol. 24, No. 7, pp. 2104-2123, October, 2010..

4 R. Yan and R. Gao, “Harmonic wavelet-based data filtering for enhanced machine defect identification”, Journal of Sound and Vibration, Vol. 329, No. 15, pp. 3203-3217, July, 2010.

5 R. Yan and R. Gao, “Energy-based feature extraction for defect diagnosis in rotary machines”, IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 9, pp.3130-3139, September, 2009.

6 R. Yan , R. Gao, and C. Wang, “Experimental evaluation of a unified time-scale-frequency technique for bearing defect feature extraction”, ASME Journal of Vibration and Acoustics, Vol. 131, No. 4, pp.041012, August, 2009.

7 R. Yan and R. Gao, “Base wavelet selection for bearing vibration signal analysis”, International Journal of Wavelets, Multi-resolution, and Information Processing, Vol. 7, No. 4, pp.411-426, July, 2009.

8 R. Yan and R. Gao, “Multi-scale enveloping spectrogram for vibration analysis in bearing defect diagnosis”, Tribology International, Vol. 42, No. 2, pp. 293-302, February 2009.

9 D. Ball, R. Yan , T. Licht, A. Deshmukh, and R. Gao, “A strategy for decomposing large-scale energy constrained sensor networks for system monitoring”, Production Planning and Control, Vol. 19, No. 4, pp. 435-447, June 2008.

10 R. Yan and R. Gao, “Rotary machine health diagnosis based on empirical mode decomposition”, ASME Journal of Vibration and Acoustics, Vol. 130, No.2, 021007-1-12, April 2008.

11 R. Yan and R. Gao, “Approximate entropy as a diagnostic tool for machine health monitoring”, Mechanical Systems and Signal Processing, Vol. 21, No. 2, pp. 824-839, February 2007.

12 R. Yan and R. Gao, “Hilbert-Huang transform-based vibration signal analysis for machine health monitoring”, IEEE Transactions on Instrumentation and Measurement, Vol. 55, No. 6, pp. 1327-1334, December, 2006.

13 R. Yan and R. Gao, “An efficient approach to machine health evaluation based on harmonic wavelet packet transform”, Robotics and Computer Integrated Manufacturing, Vol. 21, pp. 291-301, August-October 2005.

14 R. Yan and R. Gao, “Complexity as a measure for machine health evaluation”, IEEE Transactions on Instrumentation and Measurement, Vol. 53, No. 4, pp. 1327-1334, August, 2004.

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荣誉奖励：

入选2009年教育部“新世纪优秀人才支持计划”。

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