王宏超

王宏超，博士，讲师

电话：15837163905

邮箱： hongchao1983@126.com

教育背景

2002.09 — 2006.06 郑州大学，机械工程及自动化专业，学士

2008.09 — 2011.06 郑州大学，机械电子工程，硕士

2011.09 — 2015.06 上海交通大学，机械设计，博士

工作履历

2006.08 —2008.06 山西长治清华机械厂，技术部

2015.07 —至今 beat365亚洲版官网，机械设计系

教授课程

《机械设计》、《精密机械设计基础》等

研究方向

旋转机械故障诊断、信号处理、人工智能、物联网

研究概况

从事故障诊断、人工智能、信号处理、物联网研究领域10多年，以第一作者发明论文30余篇，均被SCI或EI检索；主持河南省科技攻关项目一项；以第一发明人申报发明专利四项(受理)。

近年来主要承担项目

1. 基于稀疏表征学习字典大数据特征提取的旋转机械早期故障预警技术研究，河南省科技攻关项目（192102210105），2019，10万，主持。

近年来主要荣誉

无

教材和著作

无

近年来代表性专利

1. 一种基于特征向量基线法的滚动轴承故障检测方法，发明专利，2020(受理)。

2. 一种特征向量基线法的二级齿轮箱故障智能诊断方法，发明专利，2020(受理)。

3. 基于全矢谱特征提取的机械故障诊断方法和系统，发明专利，2019(实质审查)。

4. 基于大数据敏感特征优化选取的设备预警方法及系统，发明专利，2019(实质审查)。

近年来代表性论文

1. Wang Hongchao, Du Wenliao. A sparse underdetermined blind source separation method and its application in fault diagnosis of rotating machinery [J]. Complexity, 2020: 2428710. SCI.

2. Wang Hongchao, Du Wenliao. Fast spectral correlation based on sparse representation self-learning dictionary and its application in fault diagnosis of rotating machinery [J]. Complexity, 2020: 9857839. SCI.

3. Wang Hongchao, Du Wenliao. Feature extraction of latent fault components of rolling bearing based on self-learned sparse atomics and frequency band entropy [J]. Journal of Vibration and Control, 2020: online. SCI

4. Wang Hongchao, Du Wenliao. Intelligent diagnosis of rolling bearing’ compound faults based on device state dictionary set sparse decomposition feature extraction-Hidden Markov Model [J].  Advances in mechanical engineering, 2020: online. SCI

5. Wang Hongchao, Du Wenliao. An improved spectrum correlation time-frequency analysis method and its application in fault diagnosis of rolling element bearing [J].  Journal of vibroengineering, 2020 (22): 792-803. EI

6. Wang Hongchao, Du Wenliao. A noise-resistant Wigner-Vile spectrum analysis method based on cyclostationarity and its application in fault diagnosis of rotating machinery [J]. Journal of vibroengineering, 2020: online. EI

7. Wang Hongchao, Du Wenliao. A frequency slice wavelet transform based on wavelet de-noising using neighboring coefficients method and its application in feature extraction of rolling bearing’ early weak fault [J]. Journal of vibroengineering, 2020 (22): 383-392. EI

8. Wang Hongchao, Du Wenliao. A new KSVD method based on self-adaptive matching pursuit and its application in fault diagnosis of rolling bearing weak fault [J].  International Journal of Distributed Sensor Networks, 2020: online. SCI.

9. Wang Hongchao* , Du Wenliao. A frequency slice wavelet transform based on wavelet de-noising using neighboring coefficients method and its application in feature extraction of rolling bearing’ early weak fault [J]. Journal of vibroengineering, 2020, 22 (2): 383-392. EI

10. Wang Hongchao*. The application of matching pursuit based on multi feature pattern set in the signal processing of rotating machinery [J]. Journal of vibration and control, 2019, 25 (13): 1974-1987. SCI

11. Wang Hongchao, Du Wenliao. Blind source extraction of rolling bearing’ multi-type faults based on self-learned sparse atomics [J]. Proceedings of the institution of Mechanical Engineers, Part C：Journal of Mechanical Engineering Science,2019,233 (13): 4531-4542. SCI.

12. 王宏超*，杜文辽. 基于强抗噪魏格纳威利分析的滚动轴承故障诊断[J]. 航空动力学报. 2019, 34 (4): 772-777. EI

13. Wang Hongchao, Du Wenliao. Fault diagnosis of Rolling Element Bearing compound faults based on Sparse No-Negative Matrix Factorization-Support Vector Data Description [J]. Journal of Vibration and Control, 2018, 24(2): 272-282..SCI.

14. Wang Hongchao* , Hao Fang. Fault diagnosis of rolling element bearing based on a new noise-resistant time-frequency analysis method [J]. Journal of vibroengineering, 2018, 20 (8): 2825-2838. EI.

15. Wang Hongchao* , Hao Fang. Fault diagnosis of rolling element bearing based on wavelet kernel principle component analysis-coupled hidden markov model [J]. Journal of vibroengineering, 2017, 19 (18): 5992-6006. EI.

16. Wang Hongchao* , Li Liwei, Gong Xiaoyun, et al. Blind source separation of rolling element bearing’ single channel compound fault based on shift invariant sparse coding [J]. Journal of vibroengineering, 2017, 19 (3): 1809-1822. SCI.

17. 王宏超*，向国权，郭志强，巩晓赟，杜文辽. 基于改进时频谱分析方法的滚动轴承复 合故障诊断[J].航空动力学报, 2017, 32 (7): 1698-1703. EI.

18. 王宏超*，郭志强，向国权，巩晓赟，杜文辽. 基于小波相邻系数降噪的滚动轴承早期 微弱故障时频特征提取[J].航空动力学报, 2017, 32 (5): 1266-1272. EI.

19. Wang Hongchao* . Feature extraction of rolling element bearing’s compound faults based on cyclic wiener filter with constructed reference signals [J]. Journal of Vibroengineering, 2016, 18 (5): 2880-2898. SCI.

20. Wang Hongchao* , Chen Jin, Dong Guangming. Fault diagnosis of bearing’ early weak fault based on minimum entropy de-convolution and fast Kurtogram algorithm [J]. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2015, 229 (16): 2890-2907. SCI.

21. 王宏超*，陈进，董广明. 强抗噪时频分析方法及其在滚动轴承故障诊断中的应用[J]. 机械工程学报. 2015, 51 (1): 90-96. EI.

22. [17] 王宏超*，陈进，董广明. 基于谱相关密度组合切片能量的滚动轴承故障诊断研究[J]. 振动与冲击. 2015, 34 (3): 114-117. EI.

23. [18] Wang Hongchao* , Chen Jin, Dong Guangming. Feature extraction of rolling bearing’ early weak fault based on EEMD and tunable Q-factor wavelet transform [J]. Mechanical Systems and Signal Processing, 2014, 48: 103-119. SCI.

24. [19] Wang Hongchao* , Chen Jin, Dong Guangming. Weak fault feature extraction of rolling bearing based on minimum entropy de-convolution and sparse decomposition [J]. Journal of Vibration and Control, 2014, 20 (8): 1148-1162. SCI.

25. Wang Hongchao* , Chen Jin. Performance degradation assessment of rolling bearing based on bispectrum and support vector data description [J]. Journal of Vibration and Control, 2014, 20 (13): 2032-2041. SCI.

26. 王宏超*，陈进，董广明. 一种盲源提取方法及其在滚动轴承故障特征提取中的应用[J]. 振动工程学报. 2014, 27 (5): 755-762. EI.

27. 王宏超*，陈进，董广明. 可调品质因子小波变换在转子早期碰摩故障诊断中应用[J]. 振动与冲击. 2014, 33 (10): 77-80. EI.

28. [23] 王宏超*，陈进，董广明. 基于快速 kurtogram 算法的共振解调方法在滚动轴承故障特征提取中的应用[J]. 振动与冲击. 2013, 32 (1): 35-37. EI.

29. 王宏超*，陈进，董广明. 基于补偿距离评估-小波核 PCA 的滚动轴承故障诊断[J]. 振动与冲击. 2013, 32 (18): 87-94. EI.