[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
[21] T. Liu, J. Chen, G.M. Dong. Zero crossing and coupled hidden Markov model for a rolling bearing performance degradation assessment, Journal of Vibration and Control, 2014, 20(16): 2487-2500
[22] Y. Ming, J. Chen, G.M. Dong. Application of convolved blind separation based on second-order cyclic statistics in rolling element bearing feature extraction, Journal of Vibration and Control, 2014, 20(4): 617-633
[23] G. Chen, J. Chen, G.M. Dong. Chirplet Wigner–Ville distribution for time–frequency representation and its application, Mechanical Systems and Signal Processing, 2013, 41(1): 1-13.
[24] F.Y. Cong, J. Chen, G.M. Dong. Vibration model of rolling element bearings in a rotor-bearing system for fault diagnosis, Journal of Sound and Vibration, 2013, 332(8): 2081-2097.
[25] F.Y. Cong, J. Chen, G.M. Dong. Short-time matrix series based singular value decomposition for rolling bearing fault diagnosis, Mechanical Systems and Signal Processing, 2013, 34(1-2): 218-230.
[26] R.L. Jiang, J. Chen, G.M. Dong. The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelope spectrum, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2013, 227(5): 1116-1129
[27] T. Liu, J. Chen, G.M. Dong, X.N. Zhou, W.B. Xiao. The fault detection and diagnosis in rolling element bearings using frequency band entropy, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2013, 227(1): 87-99
[28] Y. Zhou, J. Chen, G.M. Dong. Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis, Mechanical Systems and Signal Processing, 2012, 26: 229–243
[29] H.F. Tang, J. Chen, G.M. Dong. Signal complexity analysis for fault diagnosis of rolling element bearings based on matching pursuit, Journal of vibration and control, 2012, 18(5): 671-683
[30] F.Y. Cong, J. Chen, G.M. Dong. Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing, Journal of Mechanical Science and Technology, 2012, 26(2): 301–306
[31] Y. Zhou, J. Chen, G.M. Dong. Wigner-Ville distribution based on cyclic spectral density and the application in rolling element bearings diagnosis, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2011, 225(C12): 2831-2847
[32] F.Y. Cong., J. Chen, and G.M. Dong. Experimental validation of Impact Energy Model for the rub-impact assessment in a rotor system. Mechanical Systems and Signal Processing, 2011, 25(7): 2549-2558
[33] Z.Y. Wang, J.Chen, G.M. Dong, Y. Zhou. Constrained independent component analysis and its application to machine fault diagnosis, Mechanical Systems and Signal Processing, 2011, 25(7): 2501-2512
[34] Y. Ming, J. Chen, G.M. Dong. Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum, Mechanical Systems and Signal Processing, 2011, 25 (5): 1773-1785
[35] F.Y. Cong., J. Chen, G.M. Dong. Research on the order selection of the autoregressive modelling for rolling bearing diagnosis, Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 2010, 224(C10): 2289-2297.
[36] Zhao, F.G., J. Chen, G.M. Dong. SOA-based remote condition monitoring and fault diagnosis system. International Journal of Advanced Manufacturing Technology, 2010, 46(9-12): 1191-1200.
[37] H.P. Du, N. Zhang, G.M. Dong, Stabilising vehicle lateral dynamics with considerations of parameter uncertainties and control saturation through robust yaw control, IEEE Transaction on Vehicle Technology, 2010, 59: 2593-2597.
[38] Pan, Y. N., Chen, J., Dong, G. M. A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means, Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2009, 223(11): 2687-2695.