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创新资源平台
服务平台首页>协同创新>专家库>专家详情

严如强(教授)

所属单位:西安交通大学机械工程学院

担任职务:

擅长领域:

联系方式:13584054760 邮箱:登录后查看

研究领域(方向)

机械工程:

深度学习与智能制造

新一代人工智能与传感

仪器科学与技术:

生物医学信号检测及仪器

智能传感与信息处理
个人及工作简历

学历:

博士       2007      机械工程                    美国马萨诸塞大学

硕士       2002      精密仪器及机械         中国科学技术大学

学士       1997      机械电子工程             中国科学技术大学

工作简历:

2018/02 -             教授/博士生导师, 国际机械中心执行主任, 西安交通大学机械工程学院

2009/10 - 2018/01  教授/博士生导师, 东南大学仪器科学与工程学院

2011/02 - 2011/08  访问学者(Visiting Scholar), 美国康涅狄格大学

2009/02 - 2009/10  高级研究员 (Senior Research Scientist), 美国康涅狄格大学机械工程系

2006/08 - 2009/01  研究员 (Research Fellow), 美国马萨诸塞大学机械与工业工程系

2006/08 - 2008/08  客座研究员 (Guest Researcher), 美国国家标准与技术研究院传感器开发与应用实验室

科研项目
基于迁移学习的旋转机械故障诊断及寿命预测方法研究,国家自然科学基金面上项目。
学术及科研成果、专利、论文

(1) R. Yan, X. Chen, and S. Mukhopadhyay (Eds.), Structural Health Monitoring: Advanced Signal Processing Perspective, Springer, 2017.

(2) R. Gao, R. Yan, Wavelets: Theory and Applications for Manufacturing, Springer, 2011.

(3) R. Zhao, R. Yan*, Z. Chen, K. Mao, P. Wang, and R. Gao, “Deep learning and its applications to machine health monitoring”, Mechanical Systems and Signal Processing, Vol 115, pp.213-237, 2019.

(4) W. Sun, R. Zhao, R. Yan*, S. Shao, and X. Chen “Convolutional discriminative feature learning for induction motor fault diagnosis”, IEEE Transactions on Industrial Informatics, Vol. 13, No. 3, pp.1350-1359, June 2017.

(5) Y. Qian, R. Yan*, and R. Gao, “A multi-time scale approach to remaining useful life prediction in rolling bearing”, Mechanical Systems and Signal Processing, Vol. 83, pp.549-567, January 2017.

(6) Y. Qian and R. Yan*, “Remaining useful life prediction of rolling bearings using an enhanced particle filter”, IEEE Transactions on Instrumentation and Measurement, Vol. 64, No.10, pp.2696-2707, October 2015.

(7) R. Yan*, R. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: a review with applications”, Signal Processing, Vol. 96, Part A, pp. 1-15, March 2014.

主要成就

(1) R. Yan, X. Chen, and S. Mukhopadhyay (Eds.), Structural Health Monitoring: Advanced Signal Processing Perspective, Springer, 2017. (2) R. Gao, R. Yan, Wavelets: Theory and Applications for Manufacturing, Springer, 2011. (3) R. Zhao, R. Yan*, Z. Chen, K. Mao, P. Wang, and R. Gao, “Deep learning and its applications to machine health monitoring”, Mechanical Systems and Signal Processing, Vol 115, pp.213-237, 2019. (4) W. Sun, R. Zhao, R. Yan*, S. Shao, and X. Chen “Convolutional discriminative feature learning for induction motor fault diagnosis”, IEEE Transactions on Industrial Informatics, Vol. 13, No. 3, pp.1350-1359, June 2017. (5) Y. Qian, R. Yan*, and R. Gao, “A multi-time scale approach to remaining useful life prediction in rolling bearing”, Mechanical Systems and Signal Processing, Vol. 83, pp.549-567, January 2017. (6) Y. Qian and R. Yan*, “Remaining useful life prediction of rolling bearings using an enhanced particle filter”, IEEE Transactions on Instrumentation and Measurement, Vol. 64, No.10, pp.2696-2707, October 2015. (7) R. Yan*, R. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: a review with applications”, Signal Processing, Vol. 96, Part A, pp. 1-15, March 2014.