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彭业萍      助理教授

性别:

邮箱: yeping.peng@szu.edu.cn

办公室: 机电楼s627室

人才称号:

最终学位: 博士

办公电话: 0755-86950053

导师资格: 硕士生导师

研究领域:

机械与生物摩擦学系统磨损状态监测、磨粒三维重建与形态表征、图像处理与机器视觉检测技术

主讲本科课程:

控制系统仿真

主讲研究生课程:

计算机视觉、数字信号分析与处理

教育背景:

2014 – 2017,西安交通大学机械工程学院博士学位
2014 – 2015,澳大利亚新南威尔士大学机械与制造工程学院联合培养博士
2011 – 2014,西安交通大学机械工程学院硕士学位
2007 – 2011,哈尔滨工程大学机电工程学院学士学位

工作履历:

2017-至今,深圳大学机电与控制工程学院助理教授

主持项目:

一、省部级
[1] 广东省自然科学基金博士启动-纵向协同项目:融合多元磨粒特征信息的风电变速器磨损状态在线辨识方法(项目编号:2018A030310522),2018.01-2020.12,在研

二、市级
[1] 深圳市科技计划基础研究(自由探索)项目:面向航空发动机健康评估的轴承磨损状态演变规律研究(项目编号:JCYJ20170818100522101),2018.01-2020.03,在研

三、其他
[1] 深圳大学新引进教师科研启动项目:运动磨粒的三维图像重构及智能识别方法(项目编号:2017032),2017.06-2019.05,在研

代表期刊论文:

一、SCI期刊论文
[20] Yeping Peng, Junhao Cai, Tonghai Wu, Guangzhong Cao*, Ngaiming Kwok, Shengxi Zhou, Zhongxiao Peng. A hybrid convolutional neural network for intelligent wear particle classification. Tribology International, 2019, DOI: 10.1016/j.triboint.2019.05.029.
[19] Yeping Peng, Junhao Cai, Tonghai Wu, Guangzhong Cao*, Ngaiming Kwok, Shengxi Zhou, Zhongxiao Peng. Online wear characterisation of rolling element bearing using wear particle morphological features. Wear, 2019, DOI: 10.1016/j.wear.2019.05.005.
[18] Jundi Sun, Guangzhong Cao*, Sudan Huang*, Yeping Peng, Jiangbiao He, Qingquan Qian. Sliding-mode-observer-based position estimation for sensorless control. IEEE Access, 2019, 7: 61034-61045.
[17] Yeping Peng, Songbo Ruan, Guangzhong Cao*, Sudan Huang, Ngaiming Kwok, Shengxi Zhou. Automated product boundary defect detection based on image moment feature anomaly. IEEE Access, 2019, 7: 52731-52742.
[16] Yeping Peng*, Haiyan Shi, Hongkun Wu, Ruowei Li, Ngaiming Kwok, San Chi Liu, Shilong Liu, Md Arifur Rahnam. An optimum shift-and-weighted brightness mapping for low-illumination image restoration. Imaging Science Journal, 2019, 67(4): 187-201.
[15] Hongkun Wu*, Ruowei Li, Ngaiming Kwok, Yeping Peng, Tonghai Wu, Zhongxiao Peng. Restoration of low-informative image for robust debris shape measurement in on-line wear debris monitoring. Mechanical Systems and Signal Processing, 2019, 114: 539-555.
[14] Sudan Huang, Guangzhong Cao*, Yeping Peng, Chao Wu, Deliang Liang, Jiangbiao He. Design and analysis of a long-stroke planar switched reluctance motor for positioning applications. IEEE Access, 2019, 7: 22976-22987.
[13] Guangzhong Cao, Songbo Ruan, Yeping Peng*, Sudan Huang, Ngaiming Kwok. Large-complex-surface defect detection by hybrid gradient threshold segmentation and image registration. IEEE Access, 2018, 6: 36235-36246.
[12] Haiyan Shi, Shilong Liu, Hongkun Wu, Ruowei Li, Sanchi Liu, Ngaiming Kwok, Yeping Peng*. Oscillatory particle swarm optimizer. Applied Soft Computing, 2018, 73: 316-327.
[11] Yeping Peng, Tonghai Wu, Guangzhong Cao*, et al. A hybrid search-tree discriminant technique for multivariate wear debris classification. Wear, 2017, 392-393: 152-158.
[10] Yeping Peng, Tonghai Wu*, Shuo Wang, et al. Wear state identification using dynamic features of wear debris for online purpose. Wear, 2017, 376-377: 1885-1891.
[9] Yeping Peng, Tonghai Wu*, Shuo Wang, et al. A microfluidic device for three-dimensional wear debris imaging in on-line condition monitoring. Proceedings of the Institution of Mechanical Engineers, Part J- Journal of Engineering Tribology, 2017, 231(8): 965-974.
[8] Tonghai Wu*, Yeping Peng, Shuo Wang, et al. Morphological feature extraction based on multi-view images for wear debris analysis in on-line fluid monitoring. Tribology Transactions, 2017, 60(3): 408-418.
[7] Yeping Peng, Tonghai Wu*, Wang Shuo, et al. Oxidation wear monitoring based on the color extraction of on-line wear debris. Wear, 2015, 332-333: 1151~1157.  
[6] Yeping Peng, Tonghai Wu*, Wang Shuo, et al. Motion-blurred particle image restoration for on-line wear monitoring. Sensors, 2015, 15(4): 8173~8191.
[5] Tonghai Wu*, Yeping Peng, Hongkun Wu, et al. Full-life dynamic identification of wear state based on on-line wear debris image features. Mechanical Systems and Signal Processing, 2014, 42(1-2): 404-414.
[4] Tonghai Wu*, Yeping Peng, Chenxing Sheng, et al. Intelligent identification of wear mechanism via on-line ferrograph Images. Chinese Journal of Mechanical Engineering, 2014, 27(2): 411-417.
[3] Tonghai Wu*, Yeping Peng, Ying Du, et al. Dimensional description of on-line wear debris images for wear characterization. Chinese Journal of Mechanical Engineering, 2014, 27(6): 1280-1286.
[2] Hongkun Wu, Tonghai Wu*, Yeping Peng, et al. Watershed-based morphological separation of wear debris chains for on-line ferrograph analysis. Tribology Letters, 2013, 53(2): 411-420.
[1] Tonghai Wu*, Junqun Wang, Yeping Peng, et al. Description of wear debris from on-line ferrograph images by their statistical color. Tribology Transactions, 2012, 55(5): 606-14.

二、中文核心期刊论文
[3] 钱秋阳, 曹广忠, 彭业萍*, 钟祥永. EtherCAT无线网关设计与实现[J]. 自动化仪表, 2018, 39(12): 48-51+59.
[2] 李世雄, 曹广忠, 李庆, 彭业萍*, 吕洁印. 基于锚点的边缘检测优化算法研究[J]. 电子测量与仪器学报, 2018, 32(11): 9-16.
[1] 武通海, 彭业萍, 盛晨兴, 吴教义. 基于在线铁谱图像的磨损机理智能辨识[J]. 机械工程学报, 2014, (05): 212-212.

代表会议论文:

[3] Ngaiming Kwok, Haiyan Shi, YingHao Yu, Yeping Peng, Shilong Liu, Ruowei Li, Hongkun Wu. Image contrast enhancement using weighted uniform histogram for maximum information. 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2018, Beijing, China. (IEEE)
[2] Ngaiming Kwok, Haiyan Shi, Yeping Peng, Hongkun Wu, Ruowei Li, Shilong Liu, Md Arifur Rahman. Single-scale center-surround Retinex based restoration of low-illumination images with edge enhancement. 9th International Conference on Graphic and Image Processing (ICGIP), 2017, Qingdao, China. (SPIE)
[1] Songbo Ruan, Yeping Peng, Guangzhong Cao*, Sudan Huang, Xiangyong Zhong. Man-machine interaction for an unmanned tower crane using wireless multi-controller. International Conference on Intelligent Robotics and Applications (ICIRA), 2017, Lecture Notes in Computer Science, 10462: 381-392. (Springer, Cham)

代表专利:

[9] 彭业萍, 蔡俊豪, 曹广忠, 曹树鹏. 润滑油磨粒在线监测方法、终端及存储介质[P]. 申请号: 201810771554.X.
[8] 武通海, 彭业萍, 吴虹堃, 程俊. 一种基于数字视频的快速铁谱分析方法[P]. 专利号: ZL201310343023.8.
[7] 武通海, 彭业萍, 吴虹堃, 谢友柏. 一种基于视频获取的润滑油磨粒在线监测与分析方法[P]. 专利号: ZL201310141313.4.
[6] 武通海, 彭业萍, 张小刚, 张乐. 基于数字图像处理的磨损原位测量装置及方法[P]. 专利号: ZL201210076357.9.
[5] 武通海, 吴虹堃, 彭业萍, 谢友柏. 一种基于视频获取的润滑油磨粒在线监测探头[P]. 专利号: ZL201310141314.9.
[4] 武通海, 吴虹堃, 彭业萍. 面向在线铁谱图像自动识别的磨粒链自适应分割方法[P]. 专利号: ZL201310675708.2.
[3] 武通海, 王龙鑫, 彭业萍, 王硕. 一种基于磨粒图像颜色提取的在线氧化磨损状态监测方法[P]. 申请号: 201610052551.1.
[2] 武通海, 张小刚, 张乐, 彭业萍, 张亚丽. 基于电导率测量的在线润滑油微量水分传感器[P]. 专利号: ZL201210084173.7.
[1] 武通海, 张小刚, 张乐, 彭业萍, 张亚丽. 基于电导率测量的直插式润滑油微量水分传感器[P]. 专利号: ZL201210085008.3.

获得荣誉:

1.武通海,陈渭,彭业萍,等. 重大机械设备磨损状态在线监测系统,第十七届中国国际工业博览会高校展区优秀展品一等奖,中国国际工业博览会高校展区组委会教育部科技发展中心, 2015.
2.彭业萍. 2015年全国青年摩擦学学术会议优秀论文奖,中国机械工程学会摩擦学分会,2015.