所在部门
（系 部）工业工程系
（研究所）高端装备动力学与智能诊断维护研究所
E-Mail：swf@wzu.edu.cn

研究方向

机器视觉、制造系统检测技术

基本介绍

孙维方，男，1988年生，博士。2018年6月毕业于厦门大学航空航天学院并获得工学博士学位，同年进入温州大学机电工程学院。主要研究方向为：机器视觉、制造系统检测技术。截至目前，发表SCI期刊论文20余篇。

主要工作经历

2018年7月-至今，温州大学机电工程学院专技岗

获奖情况



承担项目

作为负责人承担项目情况：
浙江省自然科学基金：数字图像驱动的钛合金高速铣削表面质量自适应控制方法研究
温州市基础性工业科技项目：动态加工条件下自由曲面表面质量自适应控制方法研究
温州市重大科技创新攻关工业项目：大型室内游乐设施虚拟设计、制造与智能维护关键技术研究（合作单位负责人）
横向课题若干

学术任职

多个SCI学术期刊评审

学术成果

2020年以来发表论文和授权专利（截至2021年3月）
[1] W. Sun, Y. Zhou, J. Xiang, B. Chen, W. Feng. Hankel matrix-based condition monitoring of rolling element bearings: an enhanced framework for time-series analysis[J]. IEEE Transactions on Instrumentation and Measurement, 2021,70. (SCI收录)
[2] B. Chen, Y. Li, X. Cao, W. Sun, S. Zhuang. Recovery of undersampled force measurement from high-speed milling process using approximate sparsity in frequency domain[J]. Measurement, 2021,175. (SCI收录)
[3] W. Feng, K. Zhang, S. Cai, C. Sun, W. Sun, B. Liu. A force model for face grinding using digital graphic scanning (DGS) method[J]. The International Journal of Advanced Manufacturing Technology, 2021. (SCI收录)
[4] G. Zhi, D. He, W. Sun, Y. Zhou, X. Pan, C. Gao. An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples[J]. Measurement Science and Technology, 2021. (SCI收录)
[5] X. Cao, W. Liu, B. Yao, Q. Lan, W. Sun. detection and classification of surface defects of magnetic tile based on SE-U-Net[J]. International Journal of Performability Engineering, 2020. (EI收录，通讯作者)
[6] J. Huang, B. Chen, Y. Li, W. Sun. Fractal geometry of wavelet decomposition in mechanical signature analysis[J]. Measurement, 2020, 108571. (SCI收录，通讯作者)
[7] Y. Zhou, B.Sun, W. Sun, Z. Lei. Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process[J]. Journal of Intelligent Manufacturing, 2020. (SCI收录，通讯作者)
[8] W. Sun, X. Cao, B. Chen, Y. Zhou, Z. Shen, J. Xiang. A two-stage vision-based method for measuring the key parameters of ball screws[J]. Precision Engineering, 2020, 66: 76-86. (SCI收录)
[9]W. Sun, Y. Zhou, X. Cao, B. Chen, W. Feng, L. Chen. A two-stage method for bearing fault detection using graph similarity evaluation[J]. Measurement, 2020, 108138. （SCI收录）
[10] W. Sun, X. Cao. Curvature enhanced bearing fault diagnosis method using 2D vibration signal[J]. Journal of Mechanical Science and Technology, 2020,34: 2257-2266.（SCI收录）
[11] Z. Lei, Y. Zhou, B. Sun, W. Sun. An intrinsic timescale decomposition-based kernel extreme learning machine method to detect tool wear conditions in the milling process[J].The International Journal of Advanced Manufacturing Technology, 2020,106:12032-1212.（SCI收录）
[12] 孙维方,向家伟,周余庆，钟永腾.一种基于傅里叶变换的弹簧节距测量方法:中国,ZL 0.1[P].2020.04.07（发明专利）