何清波 教授
所在系所振动、冲击、噪声研究所
办公电话
通讯地址上海交通大学机械与动力工程学院A楼801室
电子邮件qbhe@sjtu.edu.cn
个人主页http://me.sjtu.edu.cn/teacher_directory1/heqingbo.html


教育背景 2002–2007 中国科学技术大学精密机械与精密仪器系博士
1997–2002 中国科学技术大学精密机械与精密仪器系学士

工作经历 2018– 至今 上海交通大学机械与动力工程学院 教授、博导
2009–2018 中国科学技术大学工程科学学院 副教授
2008–2009 美国马萨诸塞大学阿默斯特分校博士后、康涅狄格大学博士后
2007–2008 香港中文大学精密工程研究所Research Associate

出访及挂职经历 2013.08–2013.08 香港城市大学访问****
2012.07–2013.02 美国威斯康星大学麦迪逊分校访问****

研究方向 超材料结构动力学
振动噪声感知、辨识与控制
振动信号与信息处理
设备状态监测与故障诊断

科研项目 2020-2023 国家重点研发计划课题“轴承故障信息智能表征与多故障深度迁移诊断”，负责人
2019-2020 中国航发湖南动力机械研究所项目“主减速器多源信号模型及其分离算法”，负责人
2019-2021 中组部国家“****”青年拔尖人才支持计划，负责人
2019-2020 机械系统与振动国家重点实验室重点课题“装备振动智能检测技术与应用研究”，负责人
2019-2022 国家自然科学基金面上基金项目“方向敏感仿生声学超材料理论及噪声源检测研究”，负责人
2019-2020 装备预研教育部联合基金项目“***传动系统故障机理与诊断关键技术”，负责人
2018-2019 装备预研领域基金项目“主动声学超材料及其控制技术”，负责人
2016-2019 中国科学院青年创新促进会会员专项经费，负责人
2015-2018 国家自然科学基金面上基金项目“高速列车轴承复杂声学环境下道旁故障诊断关键理论研究”，负责人
2014-2016 教育部新世纪优秀人才支持计划，负责人
2013-2016 国家自然科学基金面上基金项目“结构微缺陷振动调制超声效应随机共振增强检测研究”，负责人
2011-2013 国家自然科学基金青年科学基金项目“设备状态非平稳流形分析及诊断方法研究”，负责人
2011-2013 教育部高等学校博士学科点专项科研基金新教师基金项目“设备状态评估的多尺度流形特征分析研究”，负责人
2011-2013 安徽高等学校省级优秀青年人才基金重点项目“机器振动相位特征及诊断研究”，负责人
2011-2013 国家自然科学基金面上基金项目“强噪声多声源陡畸变高速列车轴承声学诊断理论基础研究”，主要完成人

代表性论文专著 专著章节
4. Q. He* and X. Ding, “Time-frequency manifold for machinery fault diagnosis”, in Book: Structural Health Monitoring: An Advanced Signal Processing Perspective, Eds: R. Yan, X. Chen and S. C. Mukhopadhyay, Springer, 2017.
3. X. Wang and Q. He*, “Machinery fault signal reconstruction using time-frequency manifold”, ENGINEERING ASSET MANAGEMENT - SYSTEMS, PROFESSIONAL PRACTICES AND CERTIFICATION, Tse, PW; Mathew, J; Wong, K; Lam, R; Ko, CN, Berlin: Springer-Verlag, pp. 777-787, Germany, 2015.
2. J. Wang, Q. He*, and F. Kong, “Multi-scale manifold for machinery fault diagnosis”, ENGINEERING ASSET MANAGEMENT - SYSTEMS, PROFESSIONAL PRACTICES AND CERTIFICATION, Tse, PW; Mathew, J; Wong, K; Lam, R; Ko, CN, Berlin: Springer-Verlag, pp. 203-214, Germany, 2015.
1. S. Lu, Q. He*, and F. Kong, “Bearing defect diagnosis by stochastic resonance based on Woods-Saxon potential”, ENGINEERING ASSET MANAGEMENT - SYSTEMS, PROFESSIONAL PRACTICES AND CERTIFICATION, Tse, PW; Mathew, J; Wong, K; Lam, R; Ko, CN, Berlin: Springer-Verlag, pp. 99-108, Germany, 2015.
期刊论文
[2021]
97. Z. Liu, Q. He*, Z. Peng, "Interactive Visual Simulation Modeling for Structural Response Prediction and Damage Detection", IEEE Transactions on Industrial Electronics, Accepted.
96. Q. Li, X. Ding, Q. He, W. Huang, Y. Shao,“Manifold Sensing-based Convolution Sparse Self-Learning for Defective Bearing Morphological Feature Extraction”, IEEE Transactions on Industrial Informatics, In press.
95. Z. Liu, M. Pei, Q. He, Q. Wu, L. Jackson, L. Mao,“A novel method for polymer electrolyte membrane fuel cell fault diagnosis using 2D data”, Journal of Power Sources, 482, 228894, 2021.
94. C. Li, T. Jiang, Q. He*, Z. Peng,“Smart metasurface shaft for vibration source identification with a single sensor”, Journal of Sound and Vibration, 493, 115836，2021.
[2020]
93. M. Xu，G. Feng, Q. He, F. Gu, A. Ball,“Vibration Characteristics of Rolling Element Bearings with Different Radial Clearances for Condition Monitoring of Wind Turbine”, Applied Sciences, 10, 4731, 2020.
92. K. He, C. Zhang, Q. He, Q. Wu, L. Jackson, L. Mao,“Effectiveness of PEMFC historical state and operating mode in PEMFC prognosis”, International Journal of Hydrogen Energy, 45(56), pp. 32355-32366, 2020.
91. C. Li, T. Jiang, Q. He*, Z. Peng,“Stiffness-mass-coding metamaterial with broadband tunability for low-frequency vibration isolation”, Journal of Sound and Vibration, 489, p. 115685, 2020.
90. K. Noman, Q. He, Z. Peng, D. Wang, “A scale independent flexible bearing health monitoring index based on time frequency manifold energy & entropy”, Measurement Science and Technology, 31(11), p. 114003, 2020.
89. Z. Liu, Q. He*, Z. Li, Z. Peng, and W. Zhang, “Vision-based moving mass detection by time-varying structure vibration monitoring”, IEEE Sensors Journal, 20(19), pp. 11566-11577, October 2020.
88. S. Lu, G. Qian, Q. He*, F. Liu, Y. Liu, and Q. Wang, “In situ motor fault diagnosis using enhanced convolutional neural network in an embedded system”, IEEE Sensors Journal, 20(15), pp. 8287-8296, August 2020.
87. W. Xiong, Q. He*, and Z. Peng, "Fibonacci array-based focused acoustic camera for estimating multiple moving sound sources", Journal of Sound and Vibration, 478, p. 115351, July 2020.
86. Z. Liu, Q. He*, S. Chen, Z. Peng, and W. Zhang, “Time-varying motion filtering for vision-based non-stationary vibration measurement”, IEEE Transactions on Instrumentation and Measurement, 69(6), pp. 3907-3916, June 2020.
85. T. Jiang, C. Li, Q. He*, and Z. Peng, “Randomized resonant metamaterials for single-sensor identification of elastic vibrations”, Nature Communications, 11, p. 2353, May 2020.
84. H. Zhang and Q. He*, “Tacholess bearing fault detection based on adaptive impulse extraction in the time domain under fluctuant speed”, Measurement Science and Technology, 31, p. 074004, April 2020.
83. J. Wang, G. Du, Z. Zhu, C. Shen, and Q. He*, “Fault diagnosis of rotating machines based on the EMD manifold”, Mechanical Systems and Signal Processing, 135, p. 106443, January 2020.
[2019]
82. Y. Xiong, Z. Peng, W. Jiang, Q. He, W. Zhang, and G. Meng, “An effective accuracy evaluation method for LFMCW radar displacement monitoring with phasor statistical analysis”, IEEE Sensors Journal, 19(24), pp. 12224-12234, 2019.
81. X. Ding, Q. He*, Y. Shao, W. Huang, “Transient feature extraction based on time-frequency manifold image synthesis for machinery fault diagnosis”, IEEE Transactions on Instrumentation and Measurement, 68(11), pp. 4242-4252, 2019.
80. K. Ouyang, W. Xiong, G. Liu, Q. He*, “Wayside acoustic fault diagnosis by eliminating Doppler distortion using short-time sparse singular value decomposition”, Proceedings of the Institution of Mechanical Engineers, Part C - Journal of Mechanical Engineering Science, 233(15), pp. 5499-5514, 2019.
79. X. Ding, Q. Li, L. Lin, Q. He, Y. Shao, “Fast time-frequency manifold learning and its reconstruction for transient feature extraction in rotating machinery fault diagnosis”, Measurement, 141, pp. 380-395, 2019.
78. W. Xiong, Q. He*，Z. Peng, “Separating multiple moving sources by microphone array signals for wayside acoustic fault diagnosis”, Journal of Vibration and Acoustics - Transactions on the ASME, 141(5), p. 051004, 2019.
77. W. Qian, Q. He*, Y. Ni, Z. Peng, R. Gao, D. P. Ren, Z. M. Qi, “Design of three degree-of-freedom biomimetic microphone array based on a coupled circuit”, Measurement Science and Technology, 30(6), p. 065101, 2019.
76. K. Ouyang, W. Xiong, Q. He*, Z. Peng, “Doppler distortion removal in wayside circular microphone array signals”, IEEE Transactions on Instrumentation and Measurement, 68(5), pp. 1238-1251, 2019.
75. T. Jiang, Q. He*, Z. Peng, “Proposal for the realization of a single-detector acoustic camera using a space-coiling anisotropic metamaterial”, Physical Review Applied, 11, p. 034013, 2019.
74. S. Chen, M. Du, Z. Peng, M. Liang, Q. He, W. Zhang, “High-accuracy fault feature extraction for rolling bearings under time-varying speed conditions using an iterative envelope-tracking filter”, Journal of Sound and Vibration, 448, pp. 211-229, 2019.
73. P. Zhou, M. Du, S. Chen, Q. He, Z. Peng, W. Zhang, “Study on intra-wave frequency modulation phenomenon in detection of rub-impact fault”, Mechanical Systems and Signal Processing, 122, pp. 342-363, 2019.
72. S. Lu #, Q. He #*, J. Wang, “A review of stochastic resonance in rotating machine fault detection”, Mechanical Systems and Signal Processing, 116, pp. 230-260, 2019.
71. Z. Liu, Q. He, S. Chen, X. Dong, Z. Peng, W. Zhang, “Frequency-domain intrinsic component decomposition for multimodal signals with nonlinear group delays”, Signal Processing, 154, pp. 57-63, 2019.
[2018]
70. S. Lu, Q. He and J. Zhao, “Bearing fault diagnosis of a permanent magnet synchronous motor via a fast and online order analysis method in an embedded system”, Mechanical Systems and Signal Processing, 113, pp. 36-49, 2018.
69. T. Jiang, Q. He* and Z. Peng, “Enhanced directional acoustic sensing with phononic crystal cavity resonance”, Applied Physics Letters, 112(26), p. 261902, 2018.
68. Q. He*, E. Wu, and Y. Pan, “Multi-scale stochastic resonance spectrogram for fault diagnosis of rolling element bearings”, Journal of Sound and Vibration, 420, pp. 174-184, 2018.
67. S. Zhang, Q. He*, K. Ouyang and W. Xiong, “Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement”, Mechanical Systems and Signal Processing, 100, pp. 224-241, 2018.
66. J. Guo, S. Lu, C. Zhai, and Q. He, “Automatic bearing fault diagnosis of permanent magnet synchronous generators in wind turbines subjected to noise interference”, Measurement Science and Technology, 29(2), p. 025002, Feb. 2018.
[2017]
65. X. Liu, Z. Hu, Q. He*, S. Zhang and J. Zhu, “Doppler distortion correction based on microphone array and matching pursuit algorithm for a wayside train bearing monitoring system”, Measurement Science and Technology, 28(10), p. 105006, Oct 2017.
64. X. Ding and Q. He*, “Energy-fluctuated multiscale feature learning with deep ConvNet for intelligent spindle bearing fault diagnosis”，IEEE Transactions on Instrumentation and Measurement, 66(8), pp. 1926–1935, Aug 2017.
63. S. Zhang, Q. He*, H. Zhang, K. Ouyang, and F. Kong, “Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings”, Proceedings of the Institution of Mechanical Engineers, Part C - Journal of Mechanical Engineering Science, 232(14), pp. 2664–2680, July 2017.
62. S. Lu, Q. He*, T. Yuan, and F. Kong, “Online fault diagnosis of motor bearing via stochastic–resonance-based adaptive filter in an embedded system”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), pp. 1111–1122, July 2017.
61. X. Wang, J. Guo, S. Lu, C. Shen, and Q. He, “A computer-vision-based rotating speed estimation method for motor bearing fault diagnosis”, Measurement Science and Technology, 28(6), pp. 065012, Jun. 2017.
60. Q. He* and T. Jiang, “Complementary multi-mode low-frequency vibration energy harvesting with chiral piezoelectric structure”, Applied Physics Letters, 110(21), p. 213901, 2017.
59. Q. He*, Y. Xu, S. Lu and Y. Shao, “Frequency-shift vibro-acoustic modulation driven by low-frequency broadband excitations in a bistable cantilever oscillator”, Measurement Science and Technology, 28(3), p. 037002, 2017.
58. Q. He*, Y. Shao, and Z. Liao, “Nonlinear damage localization in structures using nonlinear vibration modulation of ultrasonic-guided waves”, Journal of Vibration and Acoustics - Transactions on the ASME, 139(2), p. 021001, 2017.
57. S. Zhang, Q. He*, H. Zhang, K. Ouyang, “Doppler correction using short-time MUSIC and angle interpolation resampling for wayside acoustic defective bearing diagnosis”, IEEE Transactions on Instrumentation and Measurement, 66(4), pp. 671–680, 2017.
56. T. Jiang and Q. He*, “Dual-directionally tunable metamaterial for low-frequency vibration isolation”, Applied Physics Letters, 110(2), p. 021907, 2017.
55. S. Lu, Q. He*, H. Zhang, F. Kong, “Rotating machine fault diagnosis through enhanced stochastic resonance by full-wave signal construction”, Mechanical Systems and Signal Processing, 85, pp. 82–97, 2017.
[2016]
54. S. Lu, Q. He*, D. Dai, and F. Kong, “Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance”, Journal of Vibration and Control, 22(20), pp. 4227-4246, Dec. 2016.
53. S. Lu, X. Wang, Q. He, F. Liu, and Y. Liu, “Fault diagnosis of motor bearing with speed fluctuation via angular resampling of transient sound signals”, Journal of Sound and Vibration, 385, pp. 16-32, December 2016.
52. X. Ding and Q. He*, “Time-frequency manifold sparse reconstruction: A novel method for bearing fault feature extraction”, Mechanical Systems and Signal Processing, 80, pp. 392–413, Dec. 2016.
51. S. Lu, J. Guo, Q. He, F. Liu, Y. Liu, and J. Zhao, “A novel contactless angular resampling method for motor bearing fault diagnosis under variable speed”, IEEE Transactions on Instrumentation and Measurement, 65(11), pp. 2538-2549, Nov. 2016.
50. J. Wang and Q. He*, “Wavelet packet envelope manifold for fault diagnosis of rolling element bearings”, IEEE Transactions on Instrumentation and Measurement, 65(11), pp. 2515-2526, Nov. 2016.
49. S. Zhang, S. Lu, Q. He*, F. Kong, “Time-varying singular value decomposition for periodic transient identification in bearing fault diagnosis”, Journal of Sound and Vibration, 379, pp. 213–231, Sep. 2016.
48. Q. He*, and X. Ding, “Sparse representation based on local time-frequency template matching for bearing transient fault feature extraction”, Journal of Sound and Vibration, 370, pp. 424–443, May 2016.
47. Q. He*, and Y. Lin, “Assessing the severity of fatigue crack using acoustics modulated by hysteretic vibration for a cantilever beam”, Journal of Sound and Vibration, 370, pp. 306–318, May 2016.
46. H. Zhang, S. Lu, Q. He*, F. Kong, “Multi-bearing defect detection with trackside acoustic signal based on a pseudo time-frequency analysis and Dopplerlet filter”, Mechanical Systems and Signal Processing, 70–71, pp. 176–200, Mar. 2016.
45. Q. He*, H. Song, and X. Ding, “Sparse signal reconstruction based on time-frequency manifold for rolling element bearing fault signature enhancement”, IEEE Transactions on Instrumentation and Measurement, 65(2), pp. 482-491, Feb. 2016.
44. H. Zhang, S. Zhang, Q. He, F. Kong, “The Doppler Effect based acoustic source separation for a wayside train bearing monitoring system”, Journal of Sound and Vibration, 361, pp.307–329, Jan. 2016.
43. C. Wang, C. Shen, Q. He*, A. Zhang, F. Liu, and F. Kong, “Wayside acoustic defective bearing detection based on improved Dopplerlet transform and Doppler transient matching”, Applied Acoustics, 101(1), pp. 141–155, Jan. 2016.
[2015]
42. H. Zhang, Q. He*, and F. Kong, “Stochastic resonance in an underdamped system with pinning potential for weak signal detection”, Sensors, 15(9), pp. 21169–21195, 2015.
41. S. Lu, Q. He*, H. Zhang, and F. Kong, “Enhanced rotating machine fault diagnosis based on time-delayed feedback stochastic resonance”, Journal of Vibration and Acoustics - Transactions on the ASME, 137(5), p. 051008, 2015.
40. J. Wang, Q. He*, and F. Kong, “Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings”, IEEE Transactions on Instrumentation and Measurement, 64(2), pp. 564–577, 2015.
39. J. Wang, Q. He*, and F. Kong, “Multiscale envelope manifold for enhanced fault diagnosis of rotating machines”, Mechanical Systems and Signal Processing, 52–53, pp. 376–392, 2015.
38. S. Lu, Q. He*, and F. Kong, “Effects of underdamped step-varying second-order stochastic resonance for weak signal detection”, Digital Signal Processing, 36, pp. 93–103, 2015.
37. X. Ding, Q. He*, and N. Luo, “A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification”, Journal of Sound and Vibration, 335, pp. 367–383, 2015.
[2014]
36. F. Liu, C. Shen, Q. He*, A. Zhang, F. Kong, and Y. Liu, “Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system”, Proceedings of the Institution of Mechanical Engineers, Part C - Journal of Mechanical Engineering Science, 228 (18), pp. 3356-3373, 2014.
35. J. Wang, Q. He*, and F. Kong, “An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings”, Journal of Sound and Vibration, 333(26), pp. 7401–7421, 2014.
34. J. Wang, Q. He*, and F. Kong, “A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects”, Applied Acoustics, 85, pp. 69–81, 2014.
33. Q. He and S. Zhou, “Discriminant locality preserving projection chart for statistical monitoring of manufacturing processes”, International Journal of Production Research, 52(18), pp. 5286-5300, 2014.
32. C. Wang, F. Hu, Q. He*, A. Zhang, F. Liu, and F. Kong, “De-noising of wayside acoustic signal from train bearings based on variable digital filtering” Applied Acoustics, 83, pp. 127–140, 2014.
31. S. Lu, Q. He*, F. Kong, “On-line weak signal detection via adaptive stochastic resonance”, Review of Scientific Instruments, 85, 066111, 2014.
30. F. Liu, Q. He*, F. Kong, Y. Liu, “Doppler effect reduction based on time-domain interpolation resampling for wayside acoustic defective bearing detector system”, Mechanical Systems and Signal Processing, 46(2), pp. 253–271, 2014.
29. Q. He*, Y. Xu, S. Lu, and D. Dai, “Out-of-resonance vibration modulation of ultrasound with a nonlinear oscillator for microcrack detection in a cantilever beam”, Applied Physics Letters, 104(17), 171903, 2014.
28. J. Wang and Q. He*, “Exchanged ridge demodulation from time-scale manifold for enhanced fault diagnosis of rotating machinery”, Journal of Sound and Vibration, 333 (11), pp. 2450–2464, 2014.
27. C. Wang, F. Kong, Q. He*, F. Hu, and F. Liu, “Doppler Effect removal based on instantaneous frequency estimation and time domain re-sampling for wayside acoustic defective bearing detector system”, Measurement, 50, pp. 346–355, 2014.
26. S. Lu, Q. He*, and F. Kong, “Stochastic resonance with Woods-Saxon potential for rolling element bearing fault diagnosis”, Mechanical Systems and Signal Processing, 45(2), pp. 488–503, 2014.
25. A. Zhang, F. Hu, Q. He*, C. Shen, F. Liu, and F. Kong, “Doppler shift removal based on instantaneous frequency estimation for wayside fault diagnosis of train bearings”, Journal of Vibration and Acoustics - Transactions on the ASME, 136(2), 021019, 2014.
24. D. Dai and Q. He*, “Structure damage localization with ultrasonic guided waves based on a time-frequency method”, Signal Processing, 96(A), pp. 21–28, 2014.
23. Q. He*, X. Wang, and Q. Zhou, “Vibration sensor data denoising using a time-frequency manifold for machinery fault diagnosis”, Sensors, 14(1), pp. 382–402, 2014.
22. S. Lu, Q. He*, F. Hu, and F. Kong, “Sequential multiscale noise tuning stochastic resonance for train bearing fault diagnosis in an embedded system”, IEEE Transactions on Instrumentation and Measurement, 63(1), pp. 106–116, 2014.
[2013]
21. J. Wang, Q. He*, and F. Kong, “Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis”, Mechanical Systems and Signal Processing, 40(1), pp. 237–256, 2013.
20. Q. He*, J. Wang, F. Hu, and F. Kong, “Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement”, Journal of Sound and Vibration, 332(21), pp. 5635–5649, 2013.
19. C. Shen, Q. He, F. Kong, and P. W. Tse, “A fast and adaptive varying-scale morphological analysis method for rolling element bearing fault diagnosis”, Proceedings of the Institution of Mechanical Engineers, Part C - Journal of Mechanical Engineering Science, 227(6), pp.1362–1370, 2013.
18. S. Lu, Q. He*, H. Zhang, S. Zhang, and F. Kong, “Signal amplification and filtering with a tristable stochastic resonance cantilever”, Review of Scientific Instruments, 84(2), 026110, 2013.
17. Q. He*, and X. Wang, “Time-frequency manifold correlation matching for periodic fault identification in rotating machines”, Journal of Sound and Vibration, 332(10), pp. 2611–2626, 2013.
16. Q. He*, “Vibration signal classification by wavelet packet energy flow manifold learning”, Journal of Sound and Vibration, 332(7), pp. 1881–1894, 2013.
15. Q. He*, “Time-frequency manifold for nonlinear feature extraction in machinery fault diagnosis”, Mechanical Systems and Signal Processing, 35(1–2), pp. 200–218, 2013.
14. P. Li, F. Kong, Q. He*, and Y. Liu “Multiscale slope feature extraction for rotating machinery fault diagnosis using wavelet analysis”, Measurement, 46(1), pp. 497–505, 2013.
[2012]
13. Q. He*, P. Li, and F. Kong, “Rolling bearing localized defect evaluation by multiscale signature via empirical mode decomposition”, Journal of Vibration and Acoustics - Transactions on the ASME, 134(6), 061013 (11 pp), 2012.
12. D. Dai and Q. He*, “Multiscale noise tuning stochastic resonance enhances weak signal detection in a circuitry system”, Measurement Science and Technology, 23(11), 115001 (8 pp), 2012.
11. Q. He*, and J. Wang, “Effects of multiscale noise tuning on stochastic resonance for weak signal detection”, Digital Signal Processing, 22(4), pp. 614–621, 2012.
10. Q. He*, Y. Liu, Q. Long, and J. Wang, “Time-frequency manifold as a signature for machine health diagnosis”, IEEE Transactions on Instrumentation and Measurement, 61(5), pp. 1218–1230, 2012.
9. F. Hu, Q. He, J. Wang, Z. Liu, and F. Kong, “Commutation sparking image monitoring for DC motor”, Journal of Manufacturing Science and Engineering - Transactions on the ASME, 134(2), 024501, 2012.
8. Q. He*, J. Wang, Y. Liu, D. Dai, and F. Kong, “Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines”, Mechanical Systems and Signal Processing, 28, pp. 443–457, 2012.
7. Q. He*, R. Du, and F. Kong, “Phase space feature based on independent component analysis for machine health diagnosis”, Journal of Vibration and Acoustics - Transactions on the ASME, 134(2), 021014 (11pp), 2012.
6. S. Liu, R. Gao, Q. He, J. Staudenmayer and P. Freedson, “Improved regression models for ventilation estimation based on chest and abdomen movements”, Physiological Measurement, 33(1), pp. 79–93, 2012.
[Before 2011]
5. Q. He*, Y. Liu, and F. Kong, “Machine fault signature analysis by midpoint-based empirical mode decomposition”, Measurement Science and Technology, 22(1), 015702 (11pp) , 2011.
4. Q. He, R. Yan, F. Kong, and R. Du, “Machine condition monitoring using principal component representations”, Mechanical Systems and Signal Processing, 23(2), pp. 446–466, 2009.
3. Q. He, S. Su, and R. Du, “Separating mixed multi-component signal with an application in mechanical watch movements”, Digital Signal Processing, 18(6), pp. 1013–1028, 2008.
2. Q. He, Z. Feng, and F. Kong, “Detection of signal transients using independent component analysis and its application in gearbox condition monitoring”, Mechanical Systems and Signal Processing, 21(5), pp. 2056–2071, 2007.
1. Q. He, F. Kong, and R. Yan, “Subspace-based gearbox condition monitoring by kernel principal component analysis”, Mechanical Systems and Signal Processing, 21(4), pp. 1755–1772, 2007.

教学工作 《机械动力学与振动学》本科生课程, 48学时
《数字信号处理》 研究生课程, 48学时

软件版权登记及专利 9. 一种基于空间折叠声学超材料的单传感器声学相机, 国家发明专利，专利号：3.4，授权日期：2020.07.31
8. 一种基于多尺度短时光滑分析的周期瞬态信号检测方法，国家发明专利，专利号：9.9，授权日期：2019.08.27
7. 一种多普勒声学信号的自适应学习校正方法，国家发明专利，专利号：6.3，授权日期：2019.04.26
6. 一种基于麦克风阵列的多普勒畸变声学信号的校正方法，国家发明专利，专利号：9.2，授权日期：2018.06.27
5. 一种基于时变奇异值分解的周期性暂态信号的检测方法，国家发明专利，专利号：1.3，授权日期：2018.06.13
4. 一种基于多尺度噪声调节的随机共振方法，国家发明专利，专利号：7.9，授权日期：2017.03.29
3. 一种瞬态信号消噪方法，国家发明专利，专利号：9.8，授权日期：2016.08.10
2. 一种动态信号分析方法及装置，国家发明专利，专利号：7.3，授权日期：2015.11.25
1. 一种周期信号增强检测装置及方法，国家发明专利，专利号：6.4，授权日期：2015.06.17

学术兼职 [1]《振动工程学报》青年编委
[2]《Applied Sciences》编委
[3] IEEE高级会员(Senior Member)
[4] IEEE仪器与测量学会上海/南京/武汉联合分会主席
[5] IEEE仪器与测量学会信号与系统技术委员会委员
[6] 中国振动工程学会故障诊断专业委员会理事
[7] 中国机械工程学会设备与维修工程分会委员

荣誉奖励 2019, TESConf 2019国际会议Finalist Best Paper Award
2019, 国家自然科学基金机械工程学科优秀结题项目（IMCC 2019）
2018, 国家“****”青年拔尖人才
2018, 上海交通大学晨星教授奖励计划
2016, ISFA 2016国际会议最佳论文奖（Best Paper Award）
2016, 中国科学院青年创新促进会
2014, 中国科学技术大学海外校友基金会青年教师事业奖
2014, 国家自然科学基金机械工程学科优秀结题项目（ICFDM 2014，核心骨干）
2013, 教育部新世纪优秀人才支持计划