Yuyan Zhang

Lecturer

ADDRESS：Mechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry

PHONE：15138938650

E-mail：2020022@zzuli.edu.cn

Research Field and Interests：

Intelligent manufacturing, Deep learning, Equipment condition monitoring, Signal analysis

Education Background：

2017.9-2021.06 Xi'an Jiaotong University  Majored in Mechanical Engineering（PHD degree）

2014.9- 2017.6  Xiangtan University Majored in Mechanical electronic engineering（Master degree）

2008.9-2012.6  Xiangtan University Majored in Mechanical design, manufacturing and automation (Bachelor)

Teaching Courses：

Artificial Intelligence, Numerical Analysis Method, Modern Detection Technology and Application

Publications:

[1] Zhang Y, Zhang YF, Li H, Ming W, Du W, Wen X，Zhang Y，Yan L. Semi-Supervised Diagnosis Method for Coupling Faults of Key Rotating Components Based on EEMD-KPCA under Cross Working Conditions [J]. Measurement Science and Technology, 2023, Accepted.

[2] Zhang Y, Zhang Y, Li H, Yan L, Wen X, Wang H. Wind turbine blade cracking detection under imbalanced data using a novel roundtrip auto-encoder approach [J]. Applied Sciences, 2023, Accepted.

[3] Zhang Y, Gao L, Wen X, et al. Intelligent fault diagnosis of machine under noisy environment using ensemble orthogonal contractive auto-encoder [J]. Expert Systems with Applications, 2022, 203: 117408.

[4] Li H, Yan X, Zhang Y*(Corresponding Author), et al. Real-time detection method for welding parts completeness based on improved YOLOX in a digital twin environment [J]. Measurement Science and Technology, 2023, 34(5): 055004.

[5] Wen X, Qian Y, Lian X, Zhang Y*(Corresponding Author). Improved genetic algorithm based on multi-layer encoding approach for integrated process planning and scheduling problem [J]. Robotics and Computer-Integrated Manufacturing, 2023, 84: 102593.

[6] Zhang Y, Li X, Gao L, et al. Ensemble deep contractive auto-encoders for intelligent fault diagnosis of machines under noisy environment [J]. Knowledge-Based Systems, 2020,196:1-20.

[7] Zhang Y, Li X, Gao L, et al. A new subset based deep feature learning method for intelligent fault diagnosis of bearing [J]. Expert Systems with Applications, 2018, 110: 125-142.

[8] Zhang Y, Li X, Gao L, et al. Imbalanced data fault diagnosis of rotating machinery using synthetic oversampling and feature learning [J]. Journal of Manufacturing Systems, 2018, 48:34-50.

[9] Zhang Y, Li X, Gao L, et al. Intelligent fault diagnosis of rotating machinery using a new ensemble deep auto-encoder method. Measurement, 2020, 151: 1-16.

[10] Li M, Zhang Y, Zeng B, et al. The modified firefly algorithm considering fireflies visual range and its application in assembly sequences planning [J]. The International Journal of Advanced Manufacturing Technology, 2016, 82(5-8):   1381-1403.

[11] Wen L, Li X, Gao L, Zhang Y. A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990-5998.

Projects：

National Natural Science Foundation of China (5210051113), Science and Technology Research Project of Henan Province (JDG20210014), PhD Start-up Fund (JDG20200109).

Professional Affiliations:

Senior member of Chinese Graphic Society, Member of Industrial Big Data and Intelligent System Branch, China Mechanical Engineering Society, Member of Henan Vibration Engineering Society.