王俊

发布时间：2024-09-29浏览次数：1842

  • 期刊论文


    30. J. Dai,  J. Wang*, W. Huang, J. Shi, Z. Zhu, Machinery health monitoring based on unsupervised feature learning via generative adversarial networks, IEEE/ASME Transactions on Mechatronics, DOI: 10.1109/TMECH.2020.3012179.

    29. G. Du, J. Wang*, 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. 2332–7782, Jun. 2020.

    28. W. Huang, N. Li, I. Selesnick, J. Shi, J. Wang, 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, 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*, 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*, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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*, 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, C. Shen, Z. Zhu, Bearing condition monitoring via multiple instantaneous frequency path extraction from enhanced time frequency representation, In Proceedings of2019 Prognostics and System Health Management Conference (PHM 2019), Qingdao, Shandong, China, Oct. 25-27, 2019.

    11. T. Jiang, J. Wang*, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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.