| 王宏超 副教授 学位：工学博士 职称：副教授、硕士生导师 毕业院校：上海交通大学 办公地点：郑州轻工业大学西三楼307 联系方式：hongchao1983@126.com |
个人简介
从事故障诊断、人工智能、信号处理、物联网研究领域20余年，以第一作者发表论文40余篇，均被SCI或EI检索；主持河南省科技攻关项目4项、企业横向项目2项；以第一发明人申报发明专利4项(授权)。 |
研究方向
故障诊断 人工智能 信号处理 物联网 |
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 |
开设课程
《机械设计》 《精密机械设计基础》 |
科研项目
复杂行星齿轮传动系统动态特性及其服役性能评估关键技术研究，河南省科技攻关项目（252102221044，已结项），2025，10万，主持。 TBM主传动系统主轴承典型故障大数据预警技术研究，河南省科技攻关项目（232102221039，已结项），2023，10万，主持。 基于稀疏表征的水泥行业齿轮箱故障单通道欠定盲源分离技术研究，河南省科技攻关项目（232102221039，已结项），2022，立项指导，主持。 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，已结项），2019，10万，主持。 火电大型机组诊断知识库开发，企业横向项目（JDG20260020，在研），2026，12万，主持。 主驱动系统专家知识库及诊断系统开发，企业横向项目（JDG20220046，已结项），2022，25万，主持。 |
论文专著与专利
[1] Wang Hongchao, Jiang Jianwen*, et al. Quantum optimization–an attention hybrid framework for multi-source bearing fault diagnosis [J]. Measurement, 2026 (273): 121203. [2] Hongchao Wang, Guoqing Xue*, Bowen Liu, et al. Fault diagnosis of Autonomous Underwater Vehicle thruster based on semi supervised interference suppression network [J]. Ocean Engineering, 2025, Vol.341: 122658. [3] Hongchao Wang, Jianwen Jiang*, Li Yu, et al. Fault Diagnosis Based On Convolutional AutoEncoders Combined With Multivariate Information Fusion [J]. Journal of vibration and control, 2025, Vol.0 (0):1-15. [4] Hongchao Wang, Guoqing Xue*, Li Yu, et al. A limited sample fault diagnosis method for time-varying speed based on interference suppression [J]. Journal of Mechanical Science and Technology, 2025, 39 (9): 4887-4902. [5] Hongchao Wang, Guoqing Xue* and Wenliao Du. An adaptive model for time-varying speed fault diagnosis under strong noise interference [J]. Journal of Mechanical Science and Technology, 2024, 38 (6): 2831-2844. [6] Hongchao Wang, Guoqing Xue*, Li Yu, et al. Time-varying speed fault diagnosis based on dual-channel parallel multi-scale information [J]. Journal of Mechanical Science and Technology, 2024, 38 (11): 5961-5978. [7]杜文辽，杨凌凯，王宏超*等.基于多尺度动态加权多级残差卷积自编码的旋转机械信号降噪方法[J].机械工程学报, 2024, Vol.60, No.18: 53-63. [8] Hongchao Wang*, Wenliao Du, Haiyi Li, et al. Weak feature extraction of rolling element bearing based on self-adaptive blind deconvolution and enhanced envelope spectral [J]. Journal of Vibration and control, 2023, Vol. 29(3-4): 611-624. [9] Hongchao Wang*, Wenliao Du. Early weak fault diagnosis of rolling element bearing based on resonance sparse decomposition and multi-objective information frequency band selection method [J]. Journal of Vibration and control, 2022, Vol. 28(19-20): 2672-2776. [10] Wang Hongchao, Li Chuan*, Du Wenliao. Coupled hidden markov fusion of multichannel fast spectral coherence features for intelligent fault diagnosis of rolling element bearings [J]. IEEE transactions on instrumentation and measurement, 2021, 3517610: 10 pages. [11] Hongchao Wang, Zhiqiang Guo and Wenliao Du. Diagnosis of rolling element bearing based on multifractal detrended fluctuation analyses and continuous hidden markov model [J]. Journal of Mechanical Science and Technology, 2021, Vol. 35(8): 3313-3322. [12] 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: 14 pages. [13] Hongchao Wang*, Wenliao Du. 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, 2021, Vol. 27(1-2): 208-219. [14] Wang Hongchao, Du Wenliao. A sparse underdetermined blind source separation method and its application in fault diagnosis of rotating machinery [J]. Complexity, 2020: 2428710. [15] 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. [16] 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. [17] 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. [18] 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. [19] 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. [20] 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. [21] 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. [22]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. [23] 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. [24] 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. [25] 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. [26] 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. [27]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. [28]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. [29] 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. [30] 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. [31]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. [32] 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. [33] 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. [34] 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. [35] 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. [36]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. [37]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. [38]王宏超*，陈进，董广明.基于最小熵解卷积与稀疏分解的滚动轴承微弱故障特征提取.机械工程学报，2013, 49 (1): 88-94. [39] 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. [40]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. 授权发明专利 |
荣誉获奖
大型机械装备状态监测与智能诊断关键技术及应用，河南省科技进步二等奖，2022，参与(第5)。 《机械故障诊断学》河南省研究生优质课程项目，河南省高等教育教学改革研究与实践项目，参加(第4)。 |