2002–2007  中國(guó)科學(xué)技術(shù)大學(xué) 精密機(jī)械與精密儀器系 博士
1997–2002  中國(guó)科學(xué)技術(shù)大學(xué) 精密機(jī)械與精密儀器系 學(xué)士

2025– 至今 上海交通大學(xué) 特聘教授
2018– 至今 上海交通大學(xué) 機(jī)械與動(dòng)力工程學(xué)院  教授，博導(dǎo)
2009–2018  中國(guó)科學(xué)技術(shù)大學(xué) 工程科學(xué)學(xué)院  副教授
2008–2009  美國(guó)馬薩諸塞大學(xué)阿默斯特分校博士后、康涅狄格大學(xué)博士后
2007–2008  香港中文大學(xué) 精密工程研究所 Research Associate

復(fù)雜裝備動(dòng)力學(xué)與智能運(yùn)維
動(dòng)態(tài)測(cè)試計(jì)算感知與智能傳感器
信號(hào)處理、大數(shù)據(jù)與感知智能

中國(guó)振動(dòng)工程學(xué)會(huì)動(dòng)態(tài)測(cè)試專業(yè)委員會(huì)副主任委員
IEEE儀器與測(cè)量學(xué)會(huì)上海/南京/武漢聯(lián)合分會(huì)主席、IEEE高級(jí)會(huì)員
IEEE儀器與測(cè)量學(xué)會(huì)信號(hào)與系統(tǒng)技術(shù)委員會(huì)委員
全國(guó)機(jī)械振動(dòng)、沖擊與狀態(tài)監(jiān)測(cè)標(biāo)準(zhǔn)化技術(shù)委員會(huì)二分委委員
中國(guó)振動(dòng)工程學(xué)會(huì)故障診斷專業(yè)委員會(huì)委員
中國(guó)振動(dòng)工程學(xué)會(huì)動(dòng)態(tài)信號(hào)分析專業(yè)委員會(huì)委員
中國(guó)機(jī)械工程學(xué)會(huì)設(shè)備智能運(yùn)維分會(huì)委員

國(guó)際國(guó)內(nèi)期刊編委：

《Frontiers in Physics》Associate Editor
《中國(guó)機(jī)械工程學(xué)報(bào)》編委
《儀器儀表學(xué)報(bào)》編委
《集成技術(shù)》編委
《Applied Sciences》編委
《振動(dòng)工程學(xué)報(bào)》青年編委
《動(dòng)力學(xué)與控制學(xué)報(bào)》青年編委
《制造技術(shù)與機(jī)床》青年編委

2026-2030 國(guó)家自然科學(xué)基金 重點(diǎn)項(xiàng)目，“多源激勵(lì)有限測(cè)點(diǎn)下船舶結(jié)構(gòu)全場(chǎng)振動(dòng)噪聲預(yù)報(bào)機(jī)理及方法”，負(fù)責(zé)人
2025-2027 上海市級(jí)重大專項(xiàng)課題，“在線健康監(jiān)測(cè)與壽命評(píng)估技術(shù)”，負(fù)責(zé)人
2023-2026 國(guó)家自然科學(xué)基金 面上項(xiàng)目，“人工智能超表面振動(dòng)感知理論與方法研究”，負(fù)責(zé)人
2022-2026 國(guó)家自然科學(xué)基金 創(chuàng)新研究群體項(xiàng)目，“復(fù)雜裝備動(dòng)力學(xué)與振動(dòng)控制”，核心成員
2021-2024 國(guó)家專項(xiàng)基礎(chǔ)研究項(xiàng)目課題，“強(qiáng)背景噪聲抑制與弱故障特征提取方法研究”，負(fù)責(zé)人
2020-2023 國(guó)家重點(diǎn)研發(fā)計(jì)劃 制造基礎(chǔ)技術(shù)與關(guān)鍵部件重點(diǎn)專項(xiàng)課題，“軸承故障信息智能表征與多故障深度遷移診斷”，負(fù)責(zé)人
2019-2022 國(guó)家自然科學(xué)基金 面上項(xiàng)目，“方向敏感仿生聲學(xué)超材料理論及噪聲源檢測(cè)研究”，負(fù)責(zé)人
2015-2018  國(guó)家自然科學(xué)基金 面上項(xiàng)目，“高速列車軸承復(fù)雜聲學(xué)環(huán)境下道旁故障診斷關(guān)鍵理論研究”（獲得機(jī)械學(xué)科“優(yōu)秀結(jié)題項(xiàng)目”），負(fù)責(zé)人
2013-2016  國(guó)家自然科學(xué)基金 面上項(xiàng)目，“結(jié)構(gòu)微缺陷振動(dòng)調(diào)制超聲效應(yīng)隨機(jī)共振增強(qiáng)檢測(cè)研究”，負(fù)責(zé)人
2011-2013  國(guó)家自然科學(xué)基金 青年項(xiàng)目，“設(shè)備狀態(tài)非平穩(wěn)流形分析及診斷方法研究”，負(fù)責(zé)人

專著章節(jié)
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.

期刊論文

168. J. Zhang, T. Li, B. Yu, Z. Jiao, Q. He*, Z. Peng, F. Wang, A novel multi-chirp rate demodulation-based time-frequency transform method for non-stationary underwater acoustic signals, Ocean Engineering, 350: 124170, 2026. 

167. T. Jiang*, T. Zhou, X. Wang, Z. Zhou, H. Jin, Q. He*, S. Zhang*, Self-reconfigurable chiral-encoded metastructure for single-channel dynamics identification, International Journal of Mechanical Sciences, 311: 111196, 2026.

166. B. Yu, J. Huang, H. Huang, J. Zhang, Q. He*, Enhancing low-frequency hydrodynamic flow sensing performance of artificial hair-like sensor via magnetic coupling, Journal of Sound and Vibration, 621: 119474, 2026

165. J. Yao, Q. He*, Z. Peng, A novel physics-guided approach for time-varying mesh stiffness estimation and data generation in gear fault diagnosis under imbalanced data, Advanced Engineering Informatics, 69: 103963, 2026.

[2025]

C4. 畢志昊,  姚錦濤,  何清波*,  彭志科. 模式分量能量特征賦能的齒輪傳動(dòng)系統(tǒng)無監(jiān)督故障溯源方法. 振動(dòng)工程學(xué)報(bào). 2025, 38 (6), pp. 1344-1353.

164. H. Huang, B. Yu and Q. He*, Vibration-localized auxiliary smart structure for bolt preload quantification, Smart Materials and Structures, 34(11): 115046, 2025.

163. T. Yang, J. Yao, J. Liu, Q. He*, Z. Peng*, Model and network dual-driven fault diagnosis framework for canned motor pumps under imbalanced data, Advanced Engineering Informatics, 68: 103615, 2025.

162. B. Yu, J. Zhang, H. Huang, F, Wang, Q. He*, Highly sensitive and multi-frequency artificial hair-like flow sensor array for hydrodynamic flow perception, Sensors and Actuators A: Physical, 392: 116705, 2025.

161. J. Yao, T. Yang, Z. Bi, J. Liu, Z. Peng, Q. He*, Coupled vibration model-driven intelligent fault diagnosis in canned motor pumps, International Journal of Mechanical Sciences, 291-292: 110181, 2025.

160. K. Hu, Q. He*, H. Xu, C. Cheng, Z. Peng, Dynamic domain adaptive ensemble for intelligent fault diagnosis of machinery, Knowledge-Based Systems, 314: 113209, 2025.

159. K. Hu, Q. Chen, J. Yao, Q. He*, Z. Peng, An interpretable deep feature aggregation framework for machinery incremental fault diagnosis, Advanced Engineering Informatics, 65: 103189, 2025.

158. J. Zhang, T. Li, B. Yu, Q. He*, Z. Peng, F. Wang, Parameterized demodulation-based sliding singular spectrum analysis for multi-component non-stationary signal decomposition, Digital Signal Processing, 160: 105046, 2025.

157. T. Li, N. Chen, Z. Peng, Q. He*,  Super Fourier analysis: A highly efficient framework for multivariate signal processing, Signal Processing, 231: 109899, 2025. 

156. Z. Bi, X. Yu, Y. Huangfu, J. Yao, P. Zhou, Q. He*, Z. Peng, Vibration source inversion-based fault diagnosis: Approach and application, Journal of Sound and Vibration, 597: 118818, 2025.

155. J. Guo, Q. He*, D. Zhen, F. Gu, Morphological Convolution Undecimated Wavelet: A Novel Frequency Demodulation Analysis Method for Bearing Fault Diagnosis, IEEE Transactions on Instrumentation and Measurement, 74: 3522008, 2025.

154. Liu, YF ; Cai, XR ; Wang, MM; Wang, XL; He, QB; Li, AJ ; Ding, XF; Han, J; Jin, MJ; Liu, JN; Jin, XJ; Shape memory alloy-based probe for measuring elastic modulus in biological tissues in the kPa-GPa range, Device, 3: 100899, 2025

153. S. Wei, Y. Zhu,Q. He, D. Wang, S. Liu, Z. Peng, An Interpretable Self-Guided Learning Model With Knowledge Distillation for Intelligent Fault Diagnosis of Rotating Machinery,  IEEE Transactions on Instrumentation and Measurement, 74, p. 3557213, 2025.

152. S. Hong, Y. Xiong, Y. Gou, Q. He, Z. Peng, Robust Vision-Based Target Outline Reconstruction and In-Plane Trajectory Measurement, IEEE Transactions on Instrumentation and Measurement, 74, p. 3535812, 2025.

151. H. Meng, Y. Xiong, W. Tian, X. Tang, Q. He, Z. Peng,  Enhanced mmWave Radar Sound Sensing: A Passive Relay for Long-Range, Source-Independent Sound Acquisition, IEEE Internet of Things Journal, 12: 14309-14319, 2025.

150. Z. Jiao, K. Noman, Q. He, Z. Deng, Y. Li, K. Eliker, Fuzzy diversity entropy as a nonlinear measure for the intelligent fault diagnosis of rotating machinery, Advanced Engineering Informatics, 64: 103057, 2025.

149. P. Zhou, X. Yu, Y. Yang, Q. He, Z. Peng, Fault-induced gear meshing modulation sideband extraction and evaluation for fault diagnosis of planetary gearboxes, Science China Technological Sciences, 68: 1120305, 2025.

[2024]

C3. 李澤函，廖昕昕，黃浩，何清波*. 一種新型液壓導(dǎo)管應(yīng)變測(cè)量裝置設(shè)計(jì)與研究. 儀器儀表學(xué)報(bào). 2024, 45 (01), pp. 180-188.
C2. 何清波*，李天奇，彭志科. 旋轉(zhuǎn)機(jī)械故障診斷中的振動(dòng)信號(hào)模型綜述（邀請(qǐng)）. 振動(dòng)、測(cè)試與診斷. 2024, 44 (04), pp.629-639. 
C1. 于小洛，楊陽，杜明剛，何清波*，彭志科. 機(jī)械傳動(dòng)系統(tǒng)含偏差頻率分量協(xié)同檢測(cè)與分解方法. 機(jī)械工程學(xué)報(bào). 2024, 60 (15), pp.100-112.
148. B. Yu, H. Huang, F. Wang, Q. He*, A highly sensitive underwater hair-like sensor with design of spiral resonant sensing base, Sensors and Actuators A: Physical, 379: 115993, 2024.
147. T. Jiang*, T. Zhou, X. Wang, T. Li, H. Jin, S. Zhang*, Z. K. Peng, and Q. He*, Spatial coding metastructure for single-sensor impact region recognition, Smart Materials and Structures, 33: 105041, 2024.
146. J. Guo, Q. He*, D. Zhen, F. Gu, Multivariate Frequency Transfer Bispectrum Estimator for Gearbox Drive System Fault Diagnosis Using Motor Current Signature Analysis, IEEE Transactions on Energy Conversion, 39(3): 2106-2114, 2024.
145. C. Yang, Q. He*, Z. Li, M. Jia*, M. Gabbouj, Z. Peng*, Multichannel Fault Diagnosis of Planetary Gearbox via Difference-Average Symbol Transition Entropy and Twin Support Higher-Order Tensor Machine, IEEE Transactions on Instrumentation and Measurement, 73:  3507210, 2024.
144.  J. Guo, Q. He*, F. Gu, DNOCNet: A Novel End-to-End Network for Induction Motor Drive Systems Fault Diagnosis Under Speed Fluctuation Condition, IEEE Transactions on Industrial Informatics, 20(6):  8284 - 8293, 2024.  
143. J. Guo, Q. He*, F. Gu, A. D. Ball, DMWMN: A Deep Modulation Network for Gearbox Intelligent Fault Detection Under Variable Working Conditions,  IEEE Transactions on Systems, Man, and Cybernetics: Systems,  54(10): 6082 - 6092, 2024.
142. K. Hu, Z. Bi, Q. He*, Z. Peng, A feature extension and reconstruction method with incremental learning capabilities under limited samples for intelligent diagnosis, Advanced Engineering Informatics, 62: 102796, 2024.
141. H. Huang, and Q. He*, Bolt looseness localization with connection-stiffness-varying flange, Smart Materials and Structures, 33(9): 095005, 2024.
140. Z. Bi, Y. Yang, M. Du, X. Yu, Q. He*, Z. Peng,  Unsupervised hypersphere description approach for detecting and localizing anomalies in drivetrain with normal data, Measurement, 228: 114349, 2024.
139. 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.
138. 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, 20(2):  2671 - 2681, 2024.  
137. J. Guo, Q. He*, D. Zhen, F. Gu, A. Ball, Multiscale cyclic frequency demodulation-based feature fusion framework for multi-sensor driven gearbox intelligent fault detection, Knowledge-Based Systems, 283: 111203, 2024.
136. 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.
135. J. Xiao, X. Ding*, H. Pan, Y. Zhang, Q. He*, et al., Dual-band filtering and enhanced directional via tunable acoustic metamaterial antennas, Smart Materials and Structures, 33(5): 055015, 2024.
134. X. Ding*, S. Wu, Y. Li, Y. Zhang, Q. He*, et al.,  Parametric Doppler correction for wayside array acoustic signal  via short-time reconstruction, Mechanical Systems and Signal Processing, 207: 110902, 2024.
133. R. Liu, X. Ding, Q. Wu, Q. He, et al., An Interpretable Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(6): 3284-3295, 2024.
132. J. Xiao, X. Ding, W. Huang, Q. He, et al., Rotating machinery weak fault features enhancement via line-defect phononic crystal sensing, Mechanical Systems and Signal Processing, 220: 111657, 2024.
131. J. Xiao, X. Ding, Y. Wang, W. Huang, Q. He, et al., Gear fault detection via directional enhancement of phononic crystal resonators, International Journal of Mechanical Sciences, 278: 109453, 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.

軟件著作權(quán)：
2) 基于視覺振動(dòng)測(cè)量的結(jié)構(gòu)全場(chǎng)視覺模態(tài)分析系統(tǒng)軟件，軟著登字第6350993號(hào)
1) 基于動(dòng)態(tài)視覺的結(jié)構(gòu)局部損傷檢測(cè)與損傷放大可視化系統(tǒng)軟件，軟著登字第6350679號(hào)

專利：
16. 基于時(shí)頻域特征的復(fù)雜傳動(dòng)裝置故障診斷溯源方法及系統(tǒng)， 國(guó)家發(fā)明專利，專利號(hào)：ZL 02210719789.0, 授權(quán)日期：2023.07.28
15. 一種參數(shù)化全場(chǎng)視覺振動(dòng)模態(tài)分解方法, 國(guó)家發(fā)明專利，專利號(hào)：ZL 202011164566.X, 授權(quán)日期：2023.02.17
14. 基于辨識(shí)嚙合剛度的齒輪故障分類檢測(cè)方法及系統(tǒng), 國(guó)家發(fā)明專利，專利號(hào)：ZL 202210008540.9, 授權(quán)日期：2022.12.06
13. 一種非接觸式結(jié)構(gòu)局部損傷動(dòng)態(tài)視覺檢測(cè)方法, 國(guó)家發(fā)明專利，專利號(hào)：ZL 202011166633.1, 授權(quán)日期：2022.07.12
12. 基于軸承力辨識(shí)的測(cè)點(diǎn)不敏感故障檢測(cè)方法, 國(guó)家發(fā)明專利，專利號(hào)：ZL 202110041577.7, 授權(quán)日期：2022.01.04
11. 基于隨機(jī)化彈性波超材料的單傳感器振動(dòng)激勵(lì)辨識(shí)系統(tǒng)，國(guó)家發(fā)明專利，專利號(hào)：ZL 202010293372.3，授權(quán)日期：2021.09.07
10. 用于低頻域?qū)拵Ц粽竦闹鲃?dòng)編碼可調(diào)超材料系統(tǒng)，國(guó)家發(fā)明專利，專利號(hào)：ZL 202010696368.1，授權(quán)日期：2021.05.28
9. 一種基于空間折疊聲學(xué)超材料的單傳感器聲學(xué)相機(jī), 國(guó)家發(fā)明專利，專利號(hào)：ZL 201811497943.4，授權(quán)日期：2020.07.31
8. 一種基于多尺度短時(shí)光滑分析的周期瞬態(tài)信號(hào)檢測(cè)方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201611241809.9，授權(quán)日期：2019.08.27
7. 一種多普勒聲學(xué)信號(hào)的自適應(yīng)學(xué)習(xí)校正方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201710228196.3，授權(quán)日期：2019.04.26
6. 一種基于麥克風(fēng)陣列的多普勒畸變聲學(xué)信號(hào)的校正方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201610522049.2，授權(quán)日期：2018.06.27
5. 一種基于時(shí)變奇異值分解的周期性暫態(tài)信號(hào)的檢測(cè)方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201610520811.3，授權(quán)日期：2018.06.13
4. 一種基于多尺度噪聲調(diào)節(jié)的隨機(jī)共振方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201310723637.9，授權(quán)日期：2017.03.29
3. 一種瞬態(tài)信號(hào)消噪方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201310257929.8，授權(quán)日期：2016.08.10
2. 一種動(dòng)態(tài)信號(hào)分析方法及裝置，國(guó)家發(fā)明專利，專利號(hào)：ZL 201210574917.3，授權(quán)日期：2015.11.25
1. 一種周期信號(hào)增強(qiáng)檢測(cè)裝置及方法，國(guó)家發(fā)明專利，專利號(hào)：ZL 201310739306.4，授權(quán)日期：2015.06.17