Dr. Qing Li

Dr. Qing Li


College of Mechanical Engineering

Donghua University

Shanghai, China


Dr. Qing Li’s technical interests lie in dynamic signal/ image processing, Big data processing, Machine learning, Mechanical fault diagnosis, and Advanced manufacturing technology. He has authored in excess of 25 archival journal papers, and professional conference articles in these areas, and 3 invention patents were authorized/published. Additionally, he is also a peer reviewer for more than 10 international journals such as Mechanical Systems and Signal Processing (SCI), Measurement (SCI), Materials & Design (SCI), Journal of Vibroengineering (SCI), Frontiers of Information Technology & Electronic Engineering (SCI), etc., and the member of IEEE, the member of the American Society of Mechanical Engineers (ASME), the member of Vibration Engineering Society of China (VESC) and also member of Communist Party of China, etc.

Research Interest

dynamic signal/ image processing, Big data processing, Machine learning, Mechanical fault diagnosis, and Advanced manufacturing technology.

Scientific Activities


Papers list:
[1]Qing Li, Xia Ji, Steven Y. Liang. Incipient fault feature extraction for rotating machinery based on improved AR-minimum entropy deconvolution combined with variational mode decomposition approach, Entropy, 2017, 19(7), 317. (SCI Archived).
[2]Qing Li, Steven Y. Liang. Incipient fault diagnosis of rolling bearings based on impulse-step impact dictionary and re-weighted minimizing nonconvex penalty Lq regular technique, Entropy, 2017,19, (8), 421. (SCI Archived).
[3]Qing Li,Steven Y. Liang, Jianguo Yang, Beizhi Li. Long range dependence prognostics for bearing vibration intensity chaotic time series, Entropy, 2016, 18(1), 23. (SCI Archived).
[4]Qing Li, Steven Y. Liang. Bearing incipient fault diagnosis based upon maximal spectral kurtosis TQWT and group sparsity total variation de-noising approach, Journal of
Vibroengineering, 2017. (SCI Archived)
[5]Qing Li, Steven Y. Liang. Degradation trend prognostics for rolling bearing using improved R/S statistic model and fractional Brownian motion approach, IEEE ACCESS, 2017, 11. (SCI Archived)
[6]Qing Li,Steven Y. Liang, Multiple faults detection for rotating machinery based on Bi-component sparse low-rank matrix separation approach, IEEE ACCESS, 2018. (SCI
[7]Qing Li, Wei Hu, Erfei Peng, Steven Y. Liang, Multichannel signals reconstruction based on tunable Q-factor wavelet transform-morphological component analysis and sparse Bayesian iteration for rotating machines, Entropy, 2018, 20(4), 263. (SCI Archived)
[8]Qing Li, Steven Y. Liang. Microstructure images restoration of metallic materials based upon KSVD and smoothing penalty sparse representation approach, Materials, 2018. (SCI Archived)
[9]Yanfei Lu, Qing Li, Zhipeng Pan, Steven Y. Liang. Prognosis of bearing degradation using gradient variable forgetting factor RLS combined with time series model, IEEE ACCESS, 2018.
(SCI Archived).
[10]Yanfei Lu, Qing Li, Steven Y. Liang. Physics-based intelligent prognosis for rolling bearing with fault feature extraction, The International Journal of Advanced Manufacturing Technology, 2018. (SCI Archived).
[11]Qing Li,Steven Y. Liang,Jianguo Yang. Bearing fault pattern recognition using harmonic wavelet sample entropy and hidden markov model, Journal of Shanghai Jiaotong University, 2016, 50(5):723-729+735.(EI Archived)
[12]Qing Li,Steven Y. Liang. Incipient fault diagnosis for large reducer taper roller bearings based on non-convex penalty regularization sparse low-rank matrix approach, Journal of mechanical engineering, 2018, 1.(EI Archived)
[13]Qing Li, Xia Ji, Steven Y. Liang, Bi-dimensional Empirical Mode Decomposition and Nonconvex Penalty Minimization Lq (q=0.5) Regular Sparse Representation-based
Classification for Image Recognition, Pattern Recognition and Image Analysis, 2018, 28(1): 59-70. (EI Archived).
[14]Qing Li, Steven Y. Liang, Wanqing Song. Revision of bearing fault characteristic spectrum using LMD and interpolation correction algorithm, Procedia CIRP, 2016. (EI Archived).
[15]Qing Li; Xia Ji; Steven Y. Liang, Pattern recognition of tool wear in high-speed milling based upon nonlinear analysis, IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC), Shenzhen, China, 2017, 511-515. (EI Archived).
[16]Qing Li,Steven Y. Liang,Incipient multi-fault diagnosis of rolling bearing using improved TQWT and sparse representation approach,IEEE International Conference on Signal and Image Processing, (ICSIP) , Singapore, 2017.(EI Archived).
[17]Qing Li, Xia Ji, Steven Y. Liang. Physical mechanism of material microstructure evolution based upon BEMD and image multi-scale entropy during heat treatment process, IEEE Information Technology, Networking, Electronic and Automation Control Conference, (ITNEC), Chengdu, China, 2017. 1129-1133. (EI Archived).
[18]Yanfei Lu, Qing Li, Steven Y. Liang. Adaptive prognosis of bearing degradation trend based on wavelet decomposition assisted ARMA model, IEEE Information Technology, Networking, Electronic and Automation Control Conference, (ITNEC), Chengdu, China, 2017. (EI Archived). Before 2015.
[1]Qing Li, Wanqing Song. Bearing vibration signal reconstruction based on LMD and nonconvex penalized Lq minimization compressed sensing[J].Journal of Central South University (Science and Technology) , 2015,46 (10):3696-3702. (EI Archived).
[2]Wanqing Song, Qing Li, Yuming Wang. Tool wear Detection Using Lipschitz Exponent and Harmonic wavelet[J].2013, Article ID 489261, Mathematical Problems in Engineering, (SCI Archived)
[3]Qing Li, Wanqing Song. Rolling Bearing Fault Diagnosis Using Empirical Mode Decomposition and EOSA Method[J]. Computer Measurement & Control, 2014, 22(4):
1006-1008. (in Chinese)
[4]Qing Li, Wanqing Song.Tool Wear State Monitoring Using Lipschitz Exponent[J]. Machinery Design & Manufacture, 2014, 6: 259-262. (in Chinese)
[5]Qing Li, Wanqing Song. A fourth shaft fixture design of hydraulic valve[J]. Manufacturing Automation, 2013, 35(9):109-112. (in Chinese)
[6]Qing Li, Wanqing Song. Tool wear state diagnosis with IMF singular value entropy, [J].Manufacturing Automation ,2013, 35(12):52-56. (in Chinese)
[7]Qing Li, Wanqing Song. Tool Wear State Monitoring Based on Lipschitz Exponent[C].Journal of Chinese design, 2014, 74-78. (in Chinese)
[8]Jiankai, Liang, Wanqing Song, Qing Li. Research on Cutting Tool Wear Based on Fractional. Brownian Motion[J]. International Journal of Mechanical Engineering and Applications. 2015, 3(1):1-5. Patents for invention:(3#)
[1]Qing Li, Wanqing Song. Long range dependence prognostics approach for short-term load forecasting. (Published and Patent Licensing).
[2]Steven Y. Liang, Qing Li, Jianguo Yang. A property test equipment for stepper motor. (Published online). Application date: 2016-03-11, and pubished date: 2016-07-27.
[3]Qing Li, Steven Y. Liang. Gearbox compound weak fault diagnosis based on a novel sparse separation method. (Under review, Pending). Application date: 2017-12-11.

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