基本介绍

周余庆，1983年12月生，博士、副教授、硕士研究生导师。主要从事状态监测、制造过程智能监测、故障诊断、质量工程等方向的教学与科研工作，在基于机器学习的机械故障诊断与制造过程监测领域积累了一定的研究基础。在IEEE Transactions on Instrumentation and Measurement、Measurement、Journal of Intelligent Manufacturing等国内外期刊上发表论文40余篇；担任International Journal of Hydromechatronics期刊青年编委，SCI期刊客籍主编；以第一发明人授权发明专利8项、计算机软件著作权登记2项。

获奖情况

浙江省科技进步三等奖1项（排名第3）
     温州市科学技术三等奖2项（排名第3）

承担项目

主持国家自然科学青年基金1项、浙江省自然科学基金1项、温州市重大科技计划项目1项，主持企事业单位横向课题10余项；作为主要参与人承担国家级、省级及温州市重点项目7项。

学术任职

中国振动工程学会故障诊断专业委员会理事

中国振动工程学会动态测试专业委员会理事

学术成果

近五年已发表的论文情况：

[1]Qinsong Zhu, Bintao Sun, Yuqing Zhou*, Weifang Sun, Jiawei xiang. Sample Augmentation for Intelligent Milling Tool Wear Condition Monitoring Using Numerical Simulation and Generative Adversarial Network. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 3516610.  ( IF: 4.016, SCI二区)

[2]Weifang Sun, Yuqing Zhou*, Jiawei Xiang, Binqiang Chen, Wei Fang. Hankel matrix-based condition monitoring of rolling element bearings: an enhanced framework for time-series analysis. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 3512310. (IF: 4.016, SCI二区).

[3]Zhi Lei, Qinsong Zhu, Yuqing Zhou*, Bintao Sun, Weifang Sun, Xiaoming Pan. A GAPSO- Enhanced Extreme Learning Machine Method for Tool Wear Estimation in Milling Processes Based on Vibration Signals. International Journal of Precision Engineering and Manufacturing- Green Technology, 2021, 8: 745-759.  ( IF: 5.671, SCI 二区)

[4]Guoxiao Zheng, Weifang Sun, Hao Zhang, Yuqing Zhou*, Chen Gao. Tool wear condition monitoring in milling process based on data fusion enhanced long short-term memory network under different cutting conditions. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2021, 23(4): 612-618. ( IF: 2.176, SCI 三区)

[5]Gaofeng Zhi, Dedao He, Weifang Sun, Yuqing Zhou*, Xiaoming Pan, Chen Gao*. An edge-labeling graph neural network method for tool wear condition monitoring using wear image with small samples. Measurement science and technology, 2021, 32, 064006 ( IF: 2.046, SCI 三区).

[6]Qinsong Zhu, Weifang Sun, Yuqing Zhou*, Chen Gao*. A tool wear condition monitoring approach for end milling based on numerical simulation. Eksploatacja i Niezawodnosc - Maintenance and Reliability, 2021, 23(2): 371-380. ( IF: 2.176, SCI 三区)

[7]Yuqing Zhou*, Bintao Sun, Weifang Sun*, Zhi Lei. Tool Wear Condition Monitoring Based on a Two-layer Angle Kernel Extreme Learning Machine using Sound Sensor for Milling Process. Journal of Intelligent Manufacturing, 2020,9. DOI: 10.1007/s10845-020-01663-1, (in Press) ( IF: 6.485, SCI 二区)

[8]Yuqing Zhou*, Bintao Sun*, Weifang Sun. A tool condition monitoring method based on two-layer angle kernel extreme learning machine and binary differential evolution for milling. Measurement, 2020,166, 108186. (IF: 3.927, SCI 二区)

[9]Zhi Lei, Yuqing Zhou*, Bintao Sun, Weifang Sun. An Intrinsic Time- scale Decomposition-based Kernel Extreme Learning Machine Method to Detect Tool Wear Conditions in the Milling Process. International Journal of Advanced Manufacturing Technology, 2020, 106(3-4): 1203-1212. (IF: 3.226, SCI 三区)

[10]Yuqing Zhou, Wei Xue*. A Multisensor Fusion Method for Tool Condition Monitoring in Milling. Sensors, 2018, 18, 3866, 1-18. (IF: 3.576, SCI 三区)

[11]Yuqing Zhou, Wei Xue*. Review of Tool Condition Monitoring Methods in Milling Processes. International Journal of Advanced Manufacturing Technology, 2018, 96(5-8): 2509-2523. (IF: 3.226, SCI 三区)

[12]Chen Gao, Wei Xue, Yan Ren, Yuqing Zhou*. Numerical Control Machine Tool Fault Diagnosis Using Hybrid Stationary Subspace Analysis and Least Squares Support Vector Machine with a Single Sensor. Applied Sciences-Basel, 2017, 7(3): 1-12. (IF: 2.679, SCI 三区)