王俊, 博士

副教授

苏州大学
轨道交通学院
工业测控与设备诊断技术研究所

地址:
江苏省苏州市相城区济学路8号，邮编：215131
苏州大学阳澄湖校区
交通大楼261室

邮箱: junking at mail.ustc.edu.cn; junking at suda.edu.cn
    

个人简介:

    王俊，男，博士，副教授，硕士生导师，苏州大学“优秀青年学者”，江苏省“双创博士”，江苏省科协青年科技人才托举工程资助培养对象。2015年6月博士毕业于中国科学技术大学精密机械与精密仪器系，获首届博士生国家奖学金、中科院院长（优秀）奖等多项荣誉。博士毕业后，在美国内布拉斯加大学林肯分校担任高级研究助理。2017年7月来到苏州大学轨道交通学院工作。目前主持国家级项目1项、省部级项目2项、市厅级项目2项。已累计在IEEE/ASME Transactions on Mechatronics、IEEE Transactions on Industrial Electronics、IEEE Transactions on Industry Applications、IEEE Transactions on Instrumentation and Measurement、IEEE Transactions on Transportation Electrification、Mechanical Systems and Signal Processing、Journal of Sound and Vibration、仪器仪表学报等故障诊断领域主流期刊和学术会议上发表论文40余篇，获授权专利10余件。是中国振动工程学会高级会员、中国振动工程学会故障诊断专业委员会理事、中国振动工程学会转子动力学专业委员会理事、中国仪器仪表学会会员、江苏省仪器仪表学会会员、江苏省仪器仪表学会故障诊断仪器专业委员会委员、美国电气与电子工程师学会（IEEE）会员。担任TMECH、TII、TIE、TIM、TPEL、MSSP、Neurocomputing等10余个国际权威SCI期刊论文评审人。主要研究方向为交通装备动态监测、故障诊断与智能维护。

教育与工作经历:

    2017.7至今   苏州大学，轨道交通学院，副教授
    2015.7-2017.6 美国内布拉斯加大学林肯分校，电气与计算机工程系，博士后研究员/高级研究助理
    2010.9-2015.6 中国科学技术大学，精密机械与精密仪器系，工学博士
    2006.9-2010.6 武汉理工大学，测控技术与仪器专业，工学学士
    2007.9-2010.6 武汉理工大学，工商管理专业，管理学学士（双学位）

研究兴趣:

    交通装备动态监控、故障诊断与智能维护:

    列车在线状态监测与智能诊断技术：微弱信号检测，多源异构信息融合，健康状态监测，故障智能诊断，剩余寿命预测等
    数据驱动技术：信号处理，信息融合，数据挖掘，凸优化，特征表示，模式识别等
    基础算法：时频分析，时频分解，稀疏表示，深度学习，流形学习等

主持的项目:

    国家自然科学基金青年科学基金项目，2019.01-2021.12
    江苏省自然科学基金青年基金项目，2018.07-2021.06
    中国博士后基金面上项目（一等资助），2019.01-2020.12
    江苏省博士后科研资助计划B类资助，2018.06-2019.12
    江苏省科协青年科技人才托举工程，2019.07-2021.06

荣誉与奖励:

    2019.09, 江苏省科协青年科技人才托举工程资助培养对象
    2018.09, 江苏省“高层次创新创业人才引进计划”“双创博士”项目
    2017.07, 苏州大学“优秀青年学者”（副教授）人才引进计划项目
    2014.10, 中科院院长优秀奖
    2012.12, 博士研究生国家奖学金

学术服务:

    中国振动工程学会——高级会员
    中国振动工程学会故障诊断专业委员会——理事
    中国振动工程学会转子动力学专业委员会——理事
    中国仪器仪表学会——会员
    江苏省仪器仪表学会——会员
    江苏省仪器仪表学会故障诊断仪器专业委员会——委员
    美国电气与电子工程师学会（IEEE）——会员
    10余个权威SCI期刊——论文评审专家

发表的论文:

    期刊论文

        33. X. Jiang, J. Wang* (王俊), C. Shen, J. Shi, W. Huang, Z. Zhu, Q. Wang, An adaptive and efficient VMD and its application for bearing fault diagnosis, Structural Health Monitoring, 2020, DOI: 10.1177/1475921720970856.

        32. W. Huang, Z. Song, C. Zhang, J. Wang (王俊), J. Shi, X. Jiang, Z. Zhu, Multi-source fidelity sparse representation via convex optimization for gearbox compound fault diagnosis, Journal of Sound and Vibration, 2020.

        31. 黄伟国, 李仕俊, 毛磊, 王俊, 沈长青, 朱忠奎. 多源稀疏优化方法研究及其在齿轮箱复合故障检测中的应用. 机械工程学报, 2020.

        30. J. Dai,  J. Wang* (王俊), W. Huang, J. Shi, Z. Zhu, Machinery health monitoring based on unsupervised feature learning via generative adversarial networks, IEEE/ASME Transactions on Mechatronics, 25(5), pp. 2252–2263, Oct. 2020.

        29. G. Du, J. Wang* (王俊), X. Jiang, D. Zhang, L. Yang, Y. Hu, Evaluation of rail potential and stray current with dynamic traction networks in multitrain subway systems, IEEE Transactions on Transportation Electrification, 6(2), pp. 784–796, Jun. 2020.

        28. W. Huang, N. Li, I. Selesnick, J. Shi, J. Wang (王俊), L. Mao, X. Jiang, Z. Zhu, Non-convex group sparsity signal decomposition via convex optimization for bearing fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 69(7), pp. 4863–4872, Jul. 2020.

        27. J. Wang (王俊), G. Du, Z. Zhu, C. Shen, Q. He, Fault diagnosis of rotating machines based on the EMD manifold, Mechanical Systems and Signal Processing, 135: 106443, 2020.

        26. T. Jiang, J. Wang* (王俊), C. Shen, X. Jiang, Z. Zhu, Multi-bandwidth mode manifold for fault diagnosis of rolling bearings, IEEE Access, 7(1), pp. 179620–179633, De. 2019.

        25. 戴俊, 王俊*, 朱忠奎, 沈长青, 黄伟国. 基于生成对抗网络和自动编码器的机械系统异常检测. 仪器仪表学报, 40(9), pp. 16–26, 2019.

        24. 黄蕾, 杜贵府, 王俊, 田静. 城轨回流系统动态排流与钢轨电位控制仿真研究. 铁道标准设计, 63(10), pp. 1–8, 2019 .

        23. 沈长青, 汤盛浩, 江星星, 石娟娟, 王俊, 朱忠奎. 独立自适应学习率优化深度信念网络在轴承故障诊断中的应用研究. 机械工程学报, 55(7), pp.81–88, 2019.

        22. G. Du,  J. Wang* (王俊), Z. Zhu, Y. Hu, D. Zhang, Effect of crossing power restraint on reflux safety parameters in multitrain subway systems, IEEE Transactions on Transportation Electrification, 5(2), pp. 490–501, 2019.

        21. J. Wang (王俊), W. Qiao, L. Qu, Wind turbine bearing fault diagnosis based on sparse representation of condition monitoring signals, IEEE Transactions on Industry Applications, 55(2), pp. 1844–1852, 2019.

        20. L. Wang, G. Cai, J. Wang (王俊), X. Jiang, Z. Zhu, Dual-Enhanced Sparse Decomposition for Wind Turbine Gearbox Fault Diagnosis, IEEE Transactions on Instrumentation and Measurement, 68(2), pp. 450–461, 2019.

        19. X. Jiang, J. Wang (王俊), J. Shi, C. Shen, W. Huang, Z. Zhu, A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 116, pp. 668–692, 2019.

        18. S. Lu, Q. He, J. Wang (王俊), A review of stochastic resonance in rotating machine fault detection, Mechanical Systems and Signal Processing, 116, pp. 230–260, 2019.

        17. C. Shen, Y. Qi, J. Wang (王俊), G. Cai, Z. Zhu, An automatic and robust features learning method for rotating machinery fault diagnosis based on contractive autoencoder, Engineering Applications of Artificial Intelligence, 76, pp. 170–184, 2018.

        16. J. Wang (王俊), F. Cheng, W. Qiao, L. Qu, Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions, IEEE Transactions on Industrial Electronics, 65(5), pp. 4268–4278, 2018.

        15. F. Cheng, J. Wang (王俊), L. Qu, W. Qiao, Rotor current-based fault diagnosis for DFIG wind turbine drivetrain gearboxes using frequency analysis and a deep classifier, IEEE Transactions on Industry Applications, 54(2), pp. 1062–1071, 2018.

        14. Y. Peng, W. Qiao, L. Qu, J. Wang (王俊), Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system, IEEE Transactions on Industry Applications, 54(2), pp. 1072–1079, 2018.

        13. J. Wang (王俊), Y. Peng, W. Qiao, J. Hudgins, Bearing fault diagnosis of direct-drive wind turbines using multiscale filtering spectrum, IEEE Transactions on Industry Applications, 53(3), pp. 3029–3038, 2017.

        12. J. Wang (王俊), Q. He, Wavelet packet envelope manifold for fault diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 65(11), pp. 2515–2526, 2016.

        11. J. Wang (王俊), Y. Peng, W. Qiao, Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines, IEEE Transactions on Industrial Electronics, 63(10), pp. 6336–6346, 2016.

        10. J. Wang (王俊), Q. He, F. Kong, Multiscale envelope manifold for enhanced fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 52-53, pp. 376–392, 2015.

        9. J. Wang (王俊), Q. He, F. Kong, Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 64(2), pp. 564–577, 2015.

        8. J. Wang (王俊), Q. He, F. Kong, An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings, Journal of Sound and Vibration, 333(26), pp. 7401–7421, 2014.

        7. J. Wang (王俊), Q. He, F. Kong, A new synthetic detection technique for wayside acoustic identification of railroad roller bearing defects, Applied Acoustics, 85, pp. 69–81, 2014.

        6. J. Wang (王俊), Q. He, Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery, Journal of Sound and Vibration, 333(11), pp. 2450–2464, 2014.

        5. J. Wang (王俊), Q. He, F. Kong, Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis, Mechanical Systems and Signal Processing, 40(1), pp. 237–256, 2013.

        4. Q. He, J. Wang (王俊), F. Hu, F. Kong. Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement, Journal of Sound and Vibration, 332(21), pp. 5635–5649, 2013.

        3. Q. He, J. Wang (王俊), Effects of multiscale noise tuning on stochastic resonance for weak signal detection, Digital Signal Processing, 22(4), pp. 614–621, 2012.

        2. Q. He, J. Wang (王俊), Y. Liu, D. Dai, F. Kong, Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines, Mechanical Systems and Signal Processing, 28, pp. 443–457, 2012.

        1. Q. He, Y. Liu, Q. Long, J. Wang (王俊), Time-frequency manifold as a signature for machine health diagnosis, IEEE Transactions on Instrumentation and Measurement, 61(5), pp. 1218–1230, 2012.

    会议论文

        13. J. Dai, J. Wang* (王俊), W. Huang, C. Shen, J. Shi, X. Jiang, Z. Zhu, Bearing anomaly detection based on generative adversarial network, In Proceedings of the Second World Congress on Condition Monitoring (WCCM), Singapore, Dec. 2-5, 2019: 122–129.

        12. R. Ding, J. Shi, J. Wang (王俊), C. Shen, Z. Zhu, Bearing condition monitoring via multiple instantaneous frequency path extraction from enhanced time frequency representation, In Proceedings of 2019 Prognostics and System Health Management Conference (PHM 2019), Qingdao, Shandong, China, Oct. 25-27, 2019.

        11. T. Jiang, J. Wang* (王俊), Z. Zhu, Adaptive multi-bandwidth mode manifold for bearing fault diagnosis, In Proceedings of Asia Pacific Conference of the Prognostics and Health Management 2019, Beijing, China, Jul. 22-24, 2019: 91–97.

        10. W. Guo, X. Jiang, J. Shi, J. Wang (王俊), C. Shen, W. Huang, Z. Zhu, An instantaneous frequency optimization strategy for bearing fault diagnosis under varying speed conditions, In Proceedings of 2019 International Conference on Sensing, Diagnostics, Prognostics, and Control, Beijing, China, Aug. 15-17, 2019.

        9. J. Wang (王俊), W. Qiao, L. Qu, Wind turbine bearing fault diagnosis based on sparse representation of condition monitoring signals, In Proceedings of 2017 IEEE Energy Conversion Congress and Exposition (ECCE 2017), Cincinnati, OH, USA, Oct. 1-5, 2017.

        8. F. Cheng, J. Wang (王俊), L. Qu, W. Qiao, Rotor current-based fault diagnosis for DFIG wind turbine drivetrain gearboxes using frequency analysis and a deep classifier, In Proceedings of IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, USA, Oct. 1-5, 2017.

        7. Y. Peng, W. Qiao, L. Qu, J. Wang (王俊), Sensor fault detection and isolation for a wireless sensor network-based remote wind turbine condition monitoring system, In Proceedings of IEEE Industry Applications Society Annual Meeting, Cincinnati, OH, USA, Oct. 1-5, 2017.

        6. Y. Peng, W. Qiao, L. Qu, J. Wang (王俊), Gearbox fault diagnosis using vibration and current information fusion, In Proceedings of 2016 IEEE Energy Conversion Congress and Exposition (ECCE 2016), Milwaukee, MI, USA, Sept. 18-22, 2016.

        5. J. Wang (王俊), Y. Peng, W. Qiao, Bearing fault diagnosis of direct-drive wind turbines using multiscale filtering spectrum, In Proceedings of 2017 IEEE Energy Conversion Congress & Exposition (ECCE 2016), Milwaukee, Wisconsin, USA, Sep. 18-22, 2016.

        4. J. Wang (王俊), Q. He, F. Kong, Multi-scale manifold for machinery fault diagnosis, In Proceedings of 8th World Congress on Engineering Asset Management and 3rd International Conference on Utility Management & Safety (8th WCEAM & 3rd ICUMAS), Hong Kong, China, Oct., 2013.

        3. J. Wang (王俊), Q. He, F. Kong, Time-scale manifold and its ridge analysis for machine fault diagnosis, In Proceedings of IEEE, Prognostics and System Health Management Conference 2012 (PHM-Beijing), Beijing, China, May, 2012.

        2. Q. He, J. Wang (王俊), Y. Liu, D. Dai, F. Kong, Bearing defect diagnosis by stochastic resonance with parameter tuning, In Proceedings of IEEE, Prognostics and System Health Management Conference 2011 (PHM-Shenzhen), Shenzhen, China, May, 2011.

        1. Q. He, Y. Liu, J. Wang (王俊), J. Wang, C. Gong, Time-frequency manifold for gear fault signature analysis, In Proceedings of IEEE, 2011 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2011), Hangzhou, China, May, 2011.

    授权的专利

        13. 沈长青, 陈博戬, 杨玉琪, 施瑞琪, 田静, 焦禹潼, 宋冬淼, 商晓峰, 江星星, 杜贵府, 王俊, 黄伟国, 石娟娟, 朱忠奎. 外置电梯控制装置及系统. 实用新型专利, 专利号: ZL202020268458.6, 授权公告日: 2020-10-16.

        12. 江星星, 沈长青, 周建芹, 宋冬淼, 郭文军, 杜贵府, 王俊, 石娟娟, 黄伟国, 朱忠奎. 中心频率收敛趋势作用下的故障诊断方法. 中国发明专利, 专利号: ZL201910750064.6, 授权公告日: 2020-05-01.

        11. 王俊, 戴俊, 黄伟国, 石娟娟, 朱忠奎. 基于潜在特征编码的机械异常检测方法. 中国发明专利, 专利号: ZL201910323189.0, 授权公告日: 2020-03-10.

        10. 王俊, 江涛, 杜贵府, 朱忠奎, 沈长青. 变分模态分解的变参信息融合方法. 中国发明专利, 专利号: ZL201810931952.3, 授权公告日: 2019-11-05.

        9. 王俊, 杜贵府, 朱忠奎, 沈长青, 陈郝勤. 流形融合经验模态分解方法. 中国发明专利, 专利号: ZL201810662526.4, 授权公告日: 2019-12-03.

        8. 杜贵府, 王俊, 江星星, 朱忠奎, 张栋梁, 李巧月. 一种轨道接头电阻检测车. 实用新型专利, 专利号: ZL 201920624009.8, 授权公告日: 2020-01-10.

        7. 杜贵府, 王俊, 朱忠奎, 江星星, 黄蕾. 回流系统钢轨电位限制装置. 实用新型专利, 专利号: ZL201920439318.8, 授权公告日: 2019-09-06.

        6. 丁荣梅, 石娟娟, 江星星, 吴楠, 沈长青, 王俊, 朱忠奎. 基于脊线概率分布和局部波动特征的瞬时转频估计方法的检测装置. 实用新型专利, 专利号: ZL201720609671.7, 授权公告日: 2018-01-23.

        5. 石娟娟, 吴楠, 江星星, 丁荣梅, 沈长青, 王俊, 朱忠奎. 基于脊线概率分布和局部波动的转频估计方法及检测装置. 中国发明专利, 专利号: ZL201710392666.X, 授权公告日: 2019-05-07.

        4. 江星星, 李宁, 沈长青, 石娟娟, 王俊, 杜贵府, 朱忠奎. 自适应变分模式分解的机械微弱故障诊断方法. 中国发明专利, 专利号: ZL201711376491.X, 授权公告日: 2019-06-27.

        3. 杜贵府, 王俊, 邓业林, 朱忠奎, 李巧月. 一种不同埋深管道共存下杂散电流非接触式检测方法. 中国发明专利, 专利号: ZL201810713116.8, 授权公告日: 2019-06-29.

        2. 何清波, 王俊, 潘媛媛. 一种基于多尺度噪声调节的随机共振方法. 中国发明专利, 专利号: ZL201310723637.9, 授权公告日: 2017-03-29.

        1. 何清波, 王俊, 汪湘湘. 一种动态信号分析方法及装置. 中国发明专利, 专利号: ZL201210574917.3, 授权公告日: 2015-11-25