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

2018– 至今 5845cc威斯尼斯人 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

2013.08–2013.08 香港城市大学访问学者
2012.07–2013.02 美国威斯康星大学麦迪逊分校访问学者

机械装备故障诊断与智能运维
动态测试计算感知与智能传感器
信号处理、大数据与感知智能

2023-2026 国家自然科学基金面上基金项目“人工智能超表面振动感知理论与方法研究”，负责人
2022-2025 上海市优秀学术带头人计划项目“机械装备振动感知、溯源与诊断”，负责人
2022-2026 国家自然科学基金创新研究群体项目“复杂装备动力学与振动控制”，核心成员
2021-2024 国家“两机”重大专项基础研究项目“**传动部件***********”，课题负责人
2020-2023 国家重点研发计划制造基础技术与关键部件重点专项课题“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“******信号模型及其分离算法”，负责人
2019-2021 中组部国家“万人计划”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******”，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”（获得机械学科“优秀结题项目”），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”（获得机械学科“优秀结题项目”），主要完成人

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
133. K. Hu, Q. He*, C. Cheng, Z. Peng, "Adaptive Incremental Diagnosis Model for Intelligent Fault Diagnosis with Dynamic Weight Correction", Reliability Engineering & System Safety, 241, p. 109705, 2024.
132. J. Guo, Q. He*, D. Zhen, F. Gu, "Motor Current Signature Analysis Using Robust Modulation Spectrum Correlation Gram for Gearbox Fault Detection", IEEE Transactions on Industrial Informatics, Accepted.
131. X. Liao, T. Jiang, C. Li, X. Yu, Z. Peng, Q. He*, "Spatial Vibration Modulation Assisted Blade Damage Localization for Industrial Quadrotor UAVs" , IEEE Transactions on Industrial Electronics, 71(2): 2018–2027, 2024.

[2023]
130. X. Yu, Y. Yang, M. Du, Q. He*, Z. Peng, "Dynamic model-embedded intelligent machine fault diagnosis without fault data", IEEE Transactions on Industrial Informatics, 19(12), pp. 11466–11476, 2023.
129. C. Li, X. Liao. Z. Peng, G. Meng, and Q. He*, “Highly sensitive and broadband meta-mechanoreceptor via mechanical frequency-division multiplexing”, Nature Communications, 14, p. 5482, 2023.
128. J. Zhang, T. Li, Q. He*, Z. Peng, "DOA estimation of non-stationary and close-spaced sources based on unified parameterized model", IEEE Sensors Journal, 23(18): 21599–21609, 2023.
127. J. Guo, Q. He*, Y. Yang, D. Zhen, F. Gu, A. Ball, "A Local Modulation Signal Bispectrum for Multiple Amplitude and Frequency Modulation Demodulation in Gearbox Fault Diagnosis", Structural Health Monitoring, 22(5): 3189–3205, 2023.
126. JDMD Editorial Office, Fan, Z., Gao, R. X.#, He, Q.#, Huang, Y., Jiang, T., Peng, Z.#, Thévenaz, L.#, Xiong, Y., & Zhong, S.# New Sensing Technologies for Monitoring Machinery, Structures, and Manufacturing Processes, Journal of Dynamics, Monitoring and Diagnostics, 2(2): 69-88, 2023. （#The joint first authors.）
125. T. Li, Q. He*, Z. Peng, "Mono-Trend Mode Decomposition for Robust Feature Extraction from Vibration Signals of Rotating Machinery", Mechanical Systems and Signal Processing, 200: 110583, 2023.
124. T. Jiang, T. Li, H. Huang, Z. Peng, Q. He*, "Metamaterial-based analog recurrent neural network towards machine intelligence", Physical Review Applied, 19(6): 064065, 2023.
123. S. Lu, J. Lu, K. An, X. Wang, Q. He*, "Edge Computing on IoT for Machine Signal Processing and Fault Diagnosis: A Review", IEEE Internet of Things Journal, 10(13): 11093-11116, 2023.
122. J. Guo, Q. He*, D. Zhen, F. Gu，"Intelligent Fault Detection for Rotating Machinery Using Cyclic Morphological Modulation Spectrum and Hierarchical Teager Permutation Entropy", IEEE Transactions on Industrial Informatics, 19(4): 6196-6207, 2023.
121. T. Li , Z. Peng , H. Xu , Q. He*, "Parameterized domain mapping for order tracking of rotating machinery", IEEE Transactions on Industrial Electronics, 70(7), pp. 7406-7416, 2023.
120. X. Yu, C. Cheng, Y. Yang, M. Du, Q. He*, Z. Peng, "Maximumly weighted iteration for solving inverse problems in dynamics", International Journal of Mechanical Sciences, 247, p. 108169, 2023.
119. J. Guo, Q. He*, D. Zhen, F. Gu, A. Ball, "An iterative morphological difference product wavelet for weak fault feature extraction in rolling bearing fault diagnosis", Structural Health Monitoring, 22, pp. 296-318, 2023.
118. J. Guo, Q. He*, D. Zhen, F. Gu, A. Ball, "Multi-sensor Data Fusion for Rotating Machinery Fault Detection Using Improved Cyclic Spectral Covariance Matrix and Motor Current Signal Analysis", Reliability Engineering & System Safety, 230, p. 108969, 2023.
117. C. Cheng, D. Shan, Y. Teng, B. Zhao, Z. Peng, Q. He, Semisupervised Fault Diagnosis for Gearboxes: A Novel Method Based on a Hybrid Classification Network and Weighted Pseudo-Labeling, IEEE Sensors Journal, 23(14): 16373 - 16383, 2023.
116. Q. Wu, X. Ding, Q. Zhang, R. Liu, Shanshan Wu, Q. He, An Intelligent Edge Diagnosis System Based on Multiplication–Convolution Sparse Network, IEEE Sensors Journal, 23(21): 26753 - 26764, 2023.
115. T. Liu, B. Chen, W. Huang, L. Jackson, L. Mao, Q. He, Q. Wu. Assessment of Tool Wear With Insufficient and Unbalanced Data Using Improved Conditional Generative Adversarial Net and High-Quality Optimization Algorithm, IEEE Transactions on Industrial Electronics, 70(11): 11670 - 11680, 2023.
114. P. Zhou, S. Chen, Q. He, D. Wang, Z. Peng, "Rotating machinery fault-induced vibration signal modulation effects: A review with mechanisms, extraction methods and applications for diagnosis", Mechanical Systems and Signal Processing, 200: 110489, 2023.
113. S. Wei , Y. Yang , M. Du , Q. He , Z. Peng, "Varying Wave-shape Component Decomposition: Algorithm and Applications" , IEEE Transactions on Industrial Electronics, 70(10): 10648-10658, 2023.
112. H. Guan, K. Wei, W. Mao, Q. He, H. Zou, Study on the static and dynamic performance of active bump-metal mesh foil bearings, Mechanical Systems and Signal Processing, 184, p. 109690, 2023
[2022]
111. T. Li, Q. He*, Z. Peng, "Parameterized Resampling Time-Frequency Transform", IEEE Transactions on Signal Processing, 70, pp. 5791-5805, 2022.
110. T. Jiang, Q. He*, "Spatial information coding with artificially engineered structures for acoustic and elastic wave sensing", Frontiers in Physics, 10, p.1024964, 2022.
109. J. Guo, D. Zhen, F. Gu, Q. He*, "Spectral Correlation Detector for Periodic Impulse Extraction of Rotating Machinery", IEEE Transactions on Instrumentation and Measurement, 71, p. 3523013, 2022.
108. X. Yu, Y. Huangfu, Q. He*, Y. Yang, M. Du, Z. Peng, "Gearbox fault diagnosis under nonstationary condition using nonlinear chirp components extracted from bearing force", Mechanical Systems and Signal Processing, 180, p. 109440, 2022.
107. X. Yu, Y. Huangfu, Y. Yang, M. Du, Q. He*, Z. Peng, "Gear fault diagnosis using gear meshing stiffness identified by gearbox housing vibration signals", Frontiers of Mechanical Engineering, 17, p. 57, 2022.
106. T. Jiang, X. Liao, H. Huang, Z. Peng, and Q. He*, "Scattering-coded architectured boundary for computational sensing of elastic waves", Cell Reports Physical Science, 3, p. 100918, 2022.
105. X. Yu, Y. Yang, Q. He*, M. Du, Z. Peng, "Multiple frequency modulation components detection and decomposition for rotary machine fault diagnosis", IEEE Transactions on Instrumentation and Measurement, 71, p. 3502310, 2022.
104. C. Li, Z. Peng* and Q. He*, "Stimuli-responsive metamaterials with information-driven elastodynamics programming", Matter, 5, pp. 988-1003, 2022.
103. S. Huang, Y. Lin, W. Tang, R. Deng, Q. He, F. Gu, A. D. Ball, "Sensing with sound enhanced acoustic metamaterials for fault diagnosis", Frontiers in Physics, 10, p. 1027895, 2022.
102. S. Wei, Q. He, D. Wang, Z. Peng, "Two-level variational chirp component decomposition for capturing intrinsic frequency modulation modes of planetary gearboxes", Mechanical Systems and Signal Processing, 177, p. 109182, 2022.
101. X. Liao, Q. He, Z. Feng, "Dynamic mass isolation method utilized in self-moving precision positioning stage for improved speed performance", Review of Science Instruments, 93, p. 055004, 2022.
100. L. Mao, Z. Liu, D. Low, W. Pan, Q. He, L. Jackson, Q. Wu, "Evaluation Method for Feature Selection in Proton Exchange Membrane Fuel Cell Fault Diagnosis", IEEE Transactions on Industrial Electronics, 69(5), pp. 5277-5286, 2022.
99. X. Ding, Y. Li, J. Xiao, Q. He, X. Yang, Y. Shao, "Parametric Doppler correction analysis for wayside acoustic bearing fault diagnosis", Mechanical Systems and Signal Processing, 166, p. 108375, 2022.
[2021]
98. Z. Liu, Q. He*, Z. Peng, "Interactive visual simulation modeling for structural response prediction and damage detection", IEEE Transactions on Industrial Electronics, 69(1), pp. 868 - 878, 2022.
97. X. Yu, Z. Li, Q. He*, Y, Yang, M, Du, Z. Peng, "Gearbox fault diagnosis based on bearing dynamic force identification", Journal of Sound and Vibration, 511, p. 116360, 2021.
96. P. Jiang, T. Jiang, Q. He*, "Origami-based adjustable sound-absorbing metamaterial", Smart Materials and Structures, 30, p. 057002, 2021.
95. 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, p. 115836，2021.
94. K. Noman, D. Wang, Z. Peng, Q. He, "Oscillation based permutation entropy calculation as a dynamic nonlinear feature for health monitoring of rolling element bearing", Measurement, 172, p. 108891, 2021.
93. B. Zhao, C. Cheng, Z. Peng, Q. He, G. Meng, "Hybrid Pre-Training Strategy for Deep Denoising Neural Networks and Its Application in Machine Fault Diagnosis", IEEE Transactions on Instrumentation and Measurement, 70, p. 3526811, 2021.
92. 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, 17(5), pp. 3069-3078, 2021.
91. 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, p. 228894, 2021.
90. B. Zhao, C. Cheng, G. Tu, Z. Peng, Q. He, G. Meng, "An Interpretable Denoising Layer for Neural Networks Based on Reproducing Kernel Hilbert Space and its Application in Machine Fault Diagnosis", Chinese Journal of Mechanical Engineering, 34(1), p. 44, 2021.
[2020]
89. 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.
88. 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.
87. 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.
86. 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.
85. 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.
84. 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.
83. 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.
82. 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.
81. 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.
[2019]
80. 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.
79. 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.
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. 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.
73. 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.
72. 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.
71. 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.
70. 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.
69. 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]
68. T. Jiang, Q. He* and Z. Peng, “Enhanced directional acoustic sensing with phononic crystal cavity resonance”, Applied Physics Letters, 112(26), p. 261902, 2018.
67. 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.
66. 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.
65. 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.
64. 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]
63. 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.
62. 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.
61. 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.
60. 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.
59. 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.
58. 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.
57. 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.
56. 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.
55. 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.
54. T. Jiang and Q. He*, “Dual-directionally tunable metamaterial for low-frequency vibration isolation”, Applied Physics Letters, 110(2), p. 021907, 2017.
53. 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]
52. 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.
51. 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.
50. 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.
49. 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.
48. 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.
47. 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.
46. 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.
45. 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.
44. 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.
43. 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.
42. 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.
41. 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]
40. 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.
39. 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.
38. 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.
37. 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.
36. 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]
35. 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.
34. 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.
33. 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.
32. 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.
31. 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.
30. S. Lu, Q. He*, F. Kong, “On-line weak signal detection via adaptive stochastic resonance”, Review of Scientific Instruments, 85, 066111, 2014.
29. 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.
28. 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.
27. 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.
26. 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.
25. 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.
24. 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.
23. 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.
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.

2023至今 《声学/力学超材料》研究生前沿课程, 48学时
2021至今 《机械振动学》本科生课程, 48学时
2021-2022 《科学研究与创新实践》本科生课程, 16学时
2019至今 《数字信号处理》研究生课程, 48学时
2018-2020 《机械动力学与振动学》本科生课程, 48学时

软件著作权：
2) 基于视觉振动测量的结构全场视觉模态分析系统软件，软著登字第6350993号
1) 基于动态视觉的结构局部损伤检测与损伤放大可视化系统软件，软著登字第6350679号

专利：
16. 基于时频域特征的复杂传动装置故障诊断溯源方法及系统， 国家发明专利，专利号：ZL 02210719789.0, 授权日期：2023.07.28
15. 一种参数化全场视觉振动模态分解方法, 国家发明专利，专利号：ZL 202011164566.X, 授权日期：2023.02.17
14. 基于辨识啮合刚度的齿轮故障分类检测方法及系统, 国家发明专利，专利号：ZL 202210008540.9, 授权日期：2022.12.06
13. 一种非接触式结构局部损伤动态视觉检测方法, 国家发明专利，专利号：ZL 202011166633.1, 授权日期：2022.07.12
12. 基于轴承力辨识的测点不敏感故障检测方法, 国家发明专利，专利号：ZL 202110041577.7, 授权日期：2022.01.04
11. 基于随机化弹性波超材料的单传感器振动激励辨识系统，国家发明专利，专利号：ZL 202010293372.3，授权日期：2021.09.07
10. 用于低频域宽带隔振的主动编码可调超材料系统，国家发明专利，专利号：ZL 202010696368.1，授权日期：2021.05.28
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：ZL 201811497943.4，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：ZL 201611241809.9，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：ZL 201710228196.3，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：ZL 201610522049.2，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：ZL 201610520811.3，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：ZL 201310723637.9，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：ZL 201310257929.8，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：ZL 201210574917.3，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：ZL 201310739306.4，授权日期：2015.06.17

[1] IEEE仪器与测量学会上海/南京/武汉联合分会主席、IEEE高级会员
[2] IEEE仪器与测量学会信号与系统技术委员会委员
[3] 中国振动工程学会故障诊断专业委员会委员
[4] 中国机械工程学会设备智能运维分会委员
[5] 中国振动工程学会动态测试专业委员会委员
[6] 中国振动工程学会动态信号分析专业委员会委员
[7] 全国高校机械工程测试技术研究会在线检测技术分会副理事长
[8] 期刊编委：《Frontiers in Physics》Associate Editor、《中国机械工程学报》编委、《集成技术》编委、《振动工程学报》青年编委、《动力学与控制》青年编委、《制造技术与机床》青年编委、《Applied Sciences》编委

2022, 5845cc威斯尼斯人优秀班主任
2022, 上海市青年优秀学术带头人
2022, 第十一届上银优秀机械博士论文奖指导教师
2021, DAMAS 2021最佳论文奖（Best Paper Award）
2020~2023, 爱思唯尔“中国高被引学者”
2020, 安徽省自然科学二等奖（排名1）
2019, TESConf 2019最佳论文提名奖（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“万人计划”青年拔尖人才
2018, 5845cc威斯尼斯人晨星教授奖励计划
2016, ISFA 2016最佳论文奖（Best Paper Award）
2016, 中国科学院青年创新促进会
2015, 中国科学技术大学优秀班主任
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）
2013, 教育部新世纪优秀人才支持计划