Mingwei Sun

Prof. Mingwei Sun

College of Artificial Intelligence, Nankai University, Tianjin 300350, China

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 September 2020 in Neurocomputing
Verified
Journal Article
Closed access
ZCZengqiang Chen
Zengqiang Chen
BQBeibei Qin
Beibei Qin
Mingwei SunMingwei Sun
Mingwei Sun
Qinglin SunQinglin Sun
Qinglin Sun

1 January 2022 in Mathematical Biosciences and Engineering
Verified
Journal Article
Open access
PDF file
YZYajing Zhou
Yajing Zhou
XLXinyu Long
Xinyu Long
Mingwei SunMingwei Sun
Mingwei Sun
ZCZengqiang Chen
Zengqiang Chen
Rolling bearings are the core components of mechanical and electrical systems. A practical fault diagnosis scheme is the key to ensure operational safety. There are excessive characteristic parameters with remarkable randomness and severe signal coupling in the rolling bearing operation, which makes the fault diagnosis to be challenging. To deal with this problem, the Gramian angular field (GAF) and DenseNet are combined to perform feature extraction and fault diagnosis. The GAF can convert 1-dimensional time series into an image, which can guarantee the completeness of feature information without temporal dependence. The GAF images are then trained by using the DenseNet to generate a data set network. In this process, the transfer learning (TL), which can solve the problem of insufficient samples, is integrated to the DenseNet to enhance its extensibility. The comparative simulations are carried out to illustrate the effectiveness of the proposed method.

1 March 2014 in ISA Transactions
Verified
Journal Article
Closed access
YWYongkun Wang
Yongkun Wang
Mingwei SunMingwei Sun
Mingwei Sun
ZWZenghui Wang
Zenghui Wang
ZLZhongxin Liu
Zhongxin Liu
ZCZengqiang Chen
Zengqiang Chen

16 February 2021 in IEEE Transactions on Industrial Informatics
Verified
Journal Article
Closed access
MPMinnan Piao
Minnan Piao
YWYing Wang
Ying Wang
Mingwei SunMingwei Sun
Mingwei Sun
XZXinhua Zhang
Xinhua Zhang
ZCZengqiang Chen
Zengqiang Chen
YYYongyi Yan
Yongyi Yan
Inertia load change, torque disturbance, and sophisticated mechanical nonlinearity impose great challenge on the high-precision control design of motor servo systems. To achieve better tracking and disturbance rejection performance, a practical control scheme, which combines a fixed-time-convergent generalized extended state observer (FGESO) with a fixed-time-convergent state feedback control law, is proposed in this article. First, an FGESO is designed to estimate the lumped disturbance and the fixed-time convergence of the estimation errors can be achieved. In the FGESO, there are three parameters and the correction term is selected as a continuous switching function of the output estimation error, which can facilitate the tuning process and greatly alleviate the chattering problem. Second, a control law based on the bilimit homogeneity technique is provided to regulate the tracking errors within a finite time independent of the initial condition. The fixed-time stability of the closed-loop system can be rigorously proved based on the Lyapunov theory. To facilitate the application of the proposed method, a practical two-step tuning procedure is established. Finally, extensive simulations and experiments are performed on a brushless direct current motor to validate the effectiveness and robustness of the proposed method.

1 February 2021 in Neurocomputing
Verified
Journal Article
Closed access
YZYuemin Zheng
Yuemin Zheng
ZCZengqiang Chen
Zengqiang Chen
ZHZhaoyang Huang
Zhaoyang Huang
Mingwei SunMingwei Sun
Mingwei Sun
Qinglin SunQinglin Sun
Qinglin Sun