Books：
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
Journal Papers：
[2018]
74. 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
73. 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
72. 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): 025002, Feb. 2018
[2017]
71. 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): 105006, Oct 2017
70. 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): 1926–1935, Aug 2017
69. 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): 2664–2680, July 2017
68. 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): 1111–1122, July 2017
67. 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): 065012, Jun. 2017
66. 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
65. 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
64. Q. He*, Y. Shao, and Z. Liao, “Nonlinear damage localization in structures using nonlinear vibration modulation of ultrasonic-guided waves”, ASME Transactions, Journal of Vibration and Acoustics, 139(2), p. 021001, 2017
63. 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): 671–680, 2017
62. T. Jiang and Q. He*, “Dual-directionally tunable metamaterial for low-frequency vibration isolation”, Applied Physics Letters, 110(2), p. 021907, 2017
61. 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]
60. 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
59. 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
58. 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
57. 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
56. 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
55. 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
54. 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
53. 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
52. 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
51. 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
50. 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
49. 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]
48. H. Zhang, Q. He*, F. Kong, “Stochastic resonance in an underdamped system with pinning potential in domain wall for weak sensor signal detection”, Sensors, 15(9), pp. 21169–21195, 2015
47. K. Ouyang, S. Lu, S. Zhang, H. Zhang, Q. He*, F. Kong*, “Online Doppler Effect Elimination Based on Unequal Time Interval Sampling for Wayside Acoustic Bearing Fault Detecting System”, Sensors, 15(9), pp. 21075–21098, 2015
46. S. Lu, Q. He*, H. Zhang, and F. Kong, “Enhanced rotating machine fault diagnosis based on time-delayed feedback stochastic resonance”, ASME Transactions, Journal of Vibration and Acoustics, 137(5), p. 051008, 2015
45. 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
44. 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
43. 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
42. 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]
41. 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”, Journal of Mechanical Engineering Science, 228 (18), pp. 3356-3373, 2014
40. 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
39. 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
38. 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
37. 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
36. Q. He*, X. Ding, and Y. Pan, “Machine fault classification based on local discriminant bases and locality preserving projections”, Mathematical Problems in Engineering, vol. 2014, Article ID 923424, 12 pages, 2014
35. H. Zhang, Q. He*, S. Lu, F. Kong, “Stochastic resonance with a joint Woods-Saxon and Gaussian potential for bearing fault diagnosis”, Mathematical Problems in Engineering, vol. 2014, Article ID 315901, 17 pages, 2014
34. S. Lu, Q. He*, F. Kong, “On-line weak signal detection via adaptive stochastic resonance”, Review of Scientific Instruments, 85, 066111, 2014
33. 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
32. F. Liu, C. Shen, Q. He*, A. Zhang, Y. Liu, and F. Kong*, “Wayside bearing fault diagnosis based on a data-driven Doppler effect eliminator and transient model analysis”, Sensors, 14(5), pp. 8096–8125, 2014
31. 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
30. 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
29. 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
28. 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
27. 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”, ASME Transactions, Journal of Vibration and Acoustics, 136(2), 021019, 2014
26. 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
25. 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
24. 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]
23. 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
22. 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
21. 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”, Journal of Mechanical Engineering Science, 227(6), pp.1362–1370, 2013
20. 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
19. 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
18. Q. He*, “Vibration signal classification by wavelet packet energy flow manifold learning”, Journal of Sound and Vibration, 332(7), pp. 1881–1894, 2013
17. 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
16. 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]
15. Q. He*, P. Li, and F. Kong, “Rolling bearing localized defect evaluation by multiscale signature via empirical mode decomposition”, ASME Transactions, Journal of Vibration and Acoustics, 134(6), 061013 (11 pp), 2012
14. 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
13. 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
12. 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
11. F. Hu, Q. He, J. Wang, Z. Liu, and F. Kong, “Commutation sparking image monitoring for DC motor”, ASME Transactions, Journal of Manufacturing Science and Engineering, 134(2), 024501, 2012
10. 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
9. Q. He*, R. Du, and F. Kong, “Phase space feature based on independent component analysis for machine health diagnosis”, ASME Transactions, Journal of Vibration and Acoustics, 134(2), 021014 (11pp), 2012
8. 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]
7. 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
6. 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
5. Q. He, and R. Du, “Mechanical watch signature analysis based on wavelet decomposition”, International Journal of Wavelets, Multiresolution and Information Processing, 7(4), pp. 491–512, 2009
4. 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
3. Q. He, S. Su, and R. Du, “Mono-component signal extraction and analysis of mechanical watch movements”, Int. J. of Control and Intelligent Systems, 36(2), pp. 177–186, 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