近期论文

[1] G.M. Dong, J. Chen, F.G. Zhao, Incipient bearing fault feature extraction based on minimum entropy deconvolution and K-SVD. Journal of Manufacturing Science and Engineering, Transactions of the ASME, 2017, accepted for publication.
[2] G.M. Dong, F.G. Zhao, X.K. Zhang. Experimental study on monitoring the bolt group looseness in a clamping support structure model. Advances in Mechanical Engineering, 2017, 9(3): 1-12
[3] G.M. Dong, J. Chen, F.G. Zhao. A frequency-shifted bispectrum for rolling element bearing diagnosis, Journal of Sound and Vibration, 2015, 339: 396-418
[4] G.M. Dong, J. Chen, N, Zhang. Investigation into on-road vehicle parameter identification based on subspace methods, Journal of Sound and Vibration, 2014, 333(24): 6760-6779
[5] G.M. Dong, J. Chen. Noise resistant time frequency analysis and application in fault diagnosis of rolling element bearings, Mechanical Systems and Signal Processing, 2012, 33: 212–236
[6] G.M. Dong. and J. Chen. Study on cyclic energy indicator for degradation assessment of rolling element bearings, Journal of Vibration and Control, 2011, 17(12), 1805-1816
[7] G.M. Dong, N. Zhang, and H.P. Du. Investigation into Untripped Rollover of Light Vehicles in the Modified Fishhook and the Sine Maneuvers, Part II: Effects of Vehicle Inertia Property, Suspension and Tyre. Vehicle System Dynamics, 2011, 49(6), 949-968
[8] G.M. Dong and J. Chen. Vibration analysis and crack identification of a rotor with open cracks, Japan Journal of Industrial and Applied Mathematics, 2011, 28(1), 171-182
[9] G.M. Dong and J. Chen. Crack Identification in a Rotor with an Open Crack, Journal of Mechanical Science and Technology, 2009, 23(11): 2964-2972
[10] N. Zhang, G.M. Dong and H.P. Du. Investigation into Untripped Rollover of Light Vehicles in The Modified Fishhook and The Sine Maneuvers, Part I: Vehicle Modeling, Roll And Yaw Instability. Vehicle System Dynamics, 2008. 46(4): 271-293.
[11] H.D. Yuan, J. Chen, G.M. Dong. Machinery fault diagnosis based on time–frequency images and label consistent K-SVD. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, First Published April 27, 2017
[12] H.T. Zhou, J. Chen, G.M. Dong, H.C. Wang, H.D. Yuan. Bearing fault recognition method based on neighbourhood component analysis and coupled hidden Markov model. Mechanical Systems and Signal Processing, 2016,66: 568-581
[13] H.T. Zhou, J. Chen, G.M. Dong. Detection and diagnosis of bearing faults using shift-invariant dictionary learning and hidden Markov model. Mechanical Systems and Signal Processing, 2016,72: 65-79
[14] H.M. Jiang, J. Chen, G.M. Dong. An intelligent performance degradation assessment method for bearings. Journal of Vibration and Control, 2016: 1077546315624996.
[15] H.M. Jiang, J. Chen, G.M. Dong. Hidden Markov model and nuisance attribute projection based bearing performance degradation assessment. Mechanical Systems and Signal Processing, 2016, 72: 184-205.
[16] H.M. Jiang, J. Chen, G.M. Dong. Study on Hankel matrix-based SVD and its application in rolling element bearing fault diagnosis. Mechanical Systems and Signal Processing, 2015, 52: 338-359.
[17] T. Liu, J. Chen, G.M. Dong. Singular spectrum analysis and continuous hidden Markov model for rolling element bearing fault diagnosis, Journal of Vibration and Control, 2015, 21(8): 1506-1521
[18] H.F. Tang, J. Chen, G.M. Dong. Sparse representation based latent components analysis for machinery weak fault detection, Mechanical Systems and Signal Processing, 2014, 46（2）: 373-388
[19] H.C. Wang, J. Chen, G.M. Dong. Feature extraction of rolling bearing's early weak fault based on EEMD and tunable Q-factor wavelet transform, Mechanical Systems and Signal Processing, 2014, 48(1-2): 103-119
[20] H.C. Wang, J. Chen, G.M. Dong. Weak fault feature extraction of rolling bearing based on MED and sparse decomposition, Journal of Vibration and Control, 2014,20(8):1148-1162