王华庆 - 北京化工大学 - 机电工程学院

个人简介

姓名：王华庆职称：教授行政职务：科研院副院长个人学习、工作经历：博士毕业学校及时间：2009.3，三重大学（Mie University，Japan）王华庆博士（Mie University，2009）、教授、博士生导师，男，1973年生，1995年毕业于北京化工大学并留校任教，2005年10月经国家留学基金委遴选赴日本攻读博士学位，2009年3月获博士学位并归国，现为北京化工大学机电工程学院教授，2010年入选“北京市优秀人才培养资助计划”，2012年入选“教育部新世纪优秀人才支持计划”。主要研究方向为机械装备健康监测及故障智能诊断，研究领域包括机械动力学、信号处理与特征提取、智能算法、模式识别及压缩感知。近年来作为项目负责人主持国家级及省部级纵向项目10余项，其中国家自然基金项目2项、973子课题1项、总装基础预研项目1项、环保部公益项目1项；作为研究骨干参加“973”课题1项，国家自然科学基金重点项目2项，国家科技支撑计划2项，“863”课题1项。曾获"国家科技进步二等奖" 1项（2001年），"石化局科技进步一等奖"1项（1998年），“北京市教学三等奖”1项（2004年）。近年来发表论文100余篇（第一或通讯作者70余篇），其中SCI收录30余篇、EI收录20余篇，参编著作2部，获软件著作权1项，申请专利4项。现为中国振动工程学会故障诊断专委会常务理事兼副秘书长，中国设备管理协会设备诊断工程委员会秘书长，中国机械工程学会设备与维修工程分会理事。出国交流访问的学校及时间：主讲课程：设备故障诊断、理论力学、材料力学等主持项目：（1）国家自然科学基金面上项目（51375037，负责）：航空发动机主轴轴承故障特征提取、智能诊断及预示方法研究（2014.1-2017.12，经费82万）；（2）国家自然科学基金面上项目（51075025，负责）：机械故障无线传感网络监测及智能诊断方法研究（2011.1-2013.12，经费32万）；（3）环保部公益项目（2015A0092，负责）：“双高”产品VOCs深度治理的经济成本与环境效益研究（2015.5-2015.12，经费20万）；（4）中国石油科技创新基金（2015D-5006-0606，负责）：油气长输管道系统泄漏风险评估方法及应急救援策略（2015.10-2017.10，经费18万）（5）教育部新世纪人才资助项目（NCET-12-0759，负责）：（2013.1-2015.12，经费25万）；（6）国家“九七三”课题（2012CB026005，子课题负责人）：压缩机组复杂系统振动故障机理及可监测诊断设计方法-子课题：网络化健康监测与故障早期预警方法及关键技术（2012.1-2016.10，主持经费120万/总经费560万）;

研究领域

机械装备健康监测及故障智能诊断，研究领域包括信号处理与特征提取、智能诊断、模式识别及压缩感知。

近期论文

查看导师新发文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

[1] Gang Tang, Wei Hou, Huaqing Wang*, Ganggang Luo and Jianwei Ma，Compressive Sensing of Roller Bearing Faults via Harmonic Detection from Under-Sampled Vibration Signals, Sensors, 2015, 15, 25648-25662. (SCI, IF:2.0) [2] G.Tang, Q. Yang, H. Wang*,G.Luo, J.Ma, Sparse classification of rotatin machinery faults based on compressive sensiong strategy, Mechatronics(accepted), 2015 (SCI) [3] Lingli Cui*,ChunguangWu, Chunqing Ma, Huaqing Wang*, Diagnosis of Roller Bearings Compound Fault Using Underdetermined Blind Source Separation Algorithm Based on Null-Space Pursuit, shock and vibration, Article ID 131489，8 pages, 2015. (SCI) [4] Liuyang Song, Peng Chen* and Huaqing Wang*, Automatic Decision Method of Optimum Symptom Parameters and Frequency Bands for Intelligent Machinery Diagnosis, Advances in Mechanical Engineering, Volume 2014, Article ID 603408, 13 pages. (SCI) [5] Hongtao Xue, Zhongxing Li, Huaqing Wang* and Peng Chen, Intelligent Diagnosis Method for Centrifugal Pump System Using Vibration Signal and Support Vector Machine, shock and vibration, Volume 2014, Article ID 407570, 14 pages. (SCI) [6] Lingli Cui*, Na Wu, Daiyi Mo, Huaqing Wang*, and Peng Chen, CQFB and PBP in Diagnosis of Local Gear Fault, Advances in Mechanical Engineering, Volume 2014, Article ID 670725, 12 pages. (SCI) [7] H. Q. Wang*, P. Chen. Fuzzy Diagnosis Method for Rotating Machinery in Variable Rotating Speed, IEEE Sensors Journal, 11(1): 23-34, 2011. (SCI, IF:1.58) [8] K. Li, P. Chen* and H. Q. Wang*. Intelligent Diagnosis Method for Rotating Machinery Using Wavelet Transform and Ant Colony Optimization, IEEE Sensors Journal, 12(7), 2474-2484, 2012. (SCI, IF:1.58) [9] H. Q. Wang and P. Chen. Intelligent Diagnosis Method for Rolling Element Bearing Faults Using Possibility Theory and Neural Network, Computers & Industrial Engineering, 60(4), 511-518, 2011. (SCI, IF:1.49) [10] H. Q. Wang, Ruitong Li, Gang Tang*, Hongfang Yuan, Qingliang Zhao, Xi Cao. A Compound Fault Diagnosis for Rolling Bearings Method Based on Blind Source Separation and Ensemble Empirical Mode Decomposition, Plos One), 9(10): e109166, 2014 (SCI, IF:3.5) [11] H. Q. Wang,Wei Hou,Gang Tang*, Hong-Fang Yuan Qing-Liang Zhao and Xi Cao. Fault Detection Enhancement in Rolling Element Bearings via Peak-Based Multiscale Decomposition and Envelope, Mathematical Problems in Engineering, Volume 2014, Article ID 329458, 11 pages(SCI, IF: 1.4) [12] Meijiao Li, H. Q. Wang*, Gang Tang, Hongfang Yuan and Yang Yang. An Improved Method Based on CEEMD for Fault Diagnosis of Rolling Bearing, Advances in Mechanical Engineering, Volume 2014, Article ID 676205, 10 pages (SCI) [13] Jing Wang, Lingli CUI, H. Q. Wang*, and Peng CHEN. Improved complexity based on time-frequency analysis in bearing quantitative diagnosis, Advances in Mechanical Engineering, 2013,Vol.2013, Article ID 258506, 11 pages (SCI) [14] Lingli Cui*,DaiyiMo， HuaqingWang*, and Peng Chen. Resonance-Based Nonlinear Demodulation Analysis Method of Rolling Bearing Fault, Advances in Mechanical Engineering, Volume 2013, Article ID 420694, 13 pages(SCI) [15] Z. Yang, L. Cai, L. X. Gao and H. Q. Wang*. Adaptive Redundant Lifting Wavelet Transform Based on Fitting for Fault Feature Extraction of Roller Bearings, Sensors, 12(4), 4381-4398, 2012. (SCI, IF:1.83) [16] L. Gao, F. Zai, S. Su, H. Q. Wang*, P. Chen and L. Liu. Study and Application of Acoustic Emission Testing in Fault Diagnosis of Low-Speed Heavy-Duty Gears, Sensors, 11(1), 599-611, 2011. (SCI, IF:1.83) [17] L. Gao, Z. J. Yang, L. Cai, H. Q. Wang* and P. Chen. Roller Bearings Fault Diagnosis Based on Nonlinear Redundant Lifting Wavelet Packet Analysis, Sensors, 11(1), 260-277, 2011. (SCI, IF:1.83) [18] L. Cui, C. H. Kang, H. Q. Wang* and P. Chen. Application of Composite Dictionary Multi-Atom Matching in Gear Fault Diagnosis, Sensors, 11(6), 5981-6002, 2011. (SCI, IF:1.83) [19] X. Cao, H. Yuan, H. Q. Wang*, P. Chen. A Study on PC/104 Embedded Rotor Automatic Balancing System Based on Iterative Learning Control, Advanced Science Letters, 4(8), 2822-2827, 2011. (SCI, IF:1.23) [20] L. Gao, Z. Q. Ren, W. L. Tang, H. Q. Wang* and P. Chen. Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR, Sensors, 10(5), 4602-4621, 2010. (SCI, IF:1.83) [21] H. Q. Wang*, K. Li, H. Sun, P. Chen. Feature Extraction Method Based on Pseudo Wigner-Ville distribution for Rotating Machinery in Variable Operating Conditions, Chinese Journal of Mechanical Engineering, 24(4), 661-668, 2011. (SCI)