OverviewProf. Jun Wang receivedthe B.Eng. degree in measurement and control technology and instrumentation from Wuhan University of Technology, Wuhan, Hubei, China, and the Ph.D. degree in instrument science and technology from University of Science and Technology of China, Hefei, Anhui, China, in 2010 and 2015, respectively.He is currently a Professor withDepartment of Signals and Control Engineering, School of Rail Transportation, Soochow University. His research interests include feature extraction, anomaly detection, intelligent fault diagnosis, prognostics and health management by analyzing sensors' signals. Prof. Wang is an associate editor of IEEE Sensors Journal, one of the young editorial members of Chinese Journal of Mechanical Engineering. He is a senior member of IEEE, a member of IEEE Instrumentation and Measurement Society. He has been awarded the Outstanding Young Scholar of Soochow University, Young Scientific and Technical Talents Promotion Project of Jiangsu Province, Innovative and Entrepreneurial Talent of Jiangsu Province, etc. Prof. Wanghave published over 60 journal papers, which have obtained nearly 3000 citations. Besides, 4 papers have been elected as ESI highly cited papers.
Ø Education Background
Ø Working Experience
Ø Funded Projects
Ø Teaching @Soochow University
Ø Honors and Awards
ØSelected Publications (*Corresponding author)
[1]Jun Wang, He Ren, Changqing Shen, Weiguo Huang, Zhongkui Zhu*, Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis, Reliability Engineering & System Safety, Mar. 2024, 243: 109879. DOI: 10.1016/j.ress.2023.109879. [2]He Ren,Jun Wang*, Weiguo Huang, Xingxing Jiang, Zhongkui Zhu, Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis, Engineering Applications of Artificial Intelligence, Nov. 2023, 126, Part D: 107117. DOI: 10.1016/j.engappai.2023.107117. [3]He Ren,Jun Wang*, Changqing Shen, Weiguo Huang, Zhongkui Zhu, Dual classifier-discriminator adversarial networks for open set fault diagnosis of train bearings, IEEE Sensors Journal, 2023, 23(18): 22040-22050. DOI: 10.1109/JSEN.2023.3301593. [4]Jun Dai,Jun Wang*,Linquan Yao, Weiguo Huang, Zhongkui Zhu, Categorical feature GAN for imbalanced intelligent fault diagnosis of rotating machinery, IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3525212. DOI:10.1109/TIM.2023.3298425. [5]He Ren,Jun Wang*, Zhongkui Zhu, Juanjuan Shi, Weiguo Huang, Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions, Mechanical Systems and Signal Processing, 2023, 200: 110579. DOI: 10.1016/j.ymssp.2023.110579. [6]Linghui Lu,Jun Wang*, Weiguo Huang, Changqing Shen, Juanjuan Shi, Zhongkui Zhu, Dual contrastive learning for semi-supervised fault diagnosis under extremely low label rate,IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3520512. DOI: 10.1109/TIM.2023.3284954. [7]王俊,王玉琦,轩建平,刘金朝,黄伟国*,朱忠奎.车辆传动系统变参小波流形融合故障诊断方法.交通运输工程学报, 2023, 23(1): 170–183.DOI: 10.19818/j.cnki.1671-1637.2023.01.013.(In Chinese) [8]He Ren,Jun Wang*, Jun Dai, Zhongkui Zhu, Jinzhao Liu, Dynamic balanced domain adversarial networks for cross domain fault diagnosis of train bearings, IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3514612. DOI: 10.1109/TIM.2022.3179468. [9]Guifu Du, Tao Jiang,Jun Wang*, Xingxing Jiang, Zhongkui Zhu, Improved multi-bandwidth mode manifold for enhanced bearing fault diagnosis, Chinese Journal of Mechanical Engineering, 2022, 35(1): 14. DOI: 10.1186/s10033-022-00677-5. [10]Xingxing Jiang,Jun Wang*, Changqing Shen, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu, Qian Wang, An adaptive and efficient VMD and its application for bearing fault diagnosis, Structural Health Monitoring, Sep. 2021, 20(5): 2708-2725. DOI: 10.1177/1475921720970856. [11]Jun Wang, Guifu Du, Zhongkui Zhu, Changqing Shen, Qingbo He*, Fault diagnosis of rotating machines based on the EMD manifold, Mechanical Systems and Signal Processing, 2020, 135: 106443. DOI: 10.1016/j.ymssp.2019.106443. [12]Jun Dai,Jun Wang*, Weiguo Huang, Juanjuan Shi, Zhongkui Zhu, Machinery health monitoring based on unsupervised feature learning via generative adversarial networks, IEEE/ASME Transactions on Mechatronics, Oct. 2020, 25(5): 2252–2263. DOI: 10.1109/TMECH.2020.3012179. [13]Guifu Du,Jun Wang*, Xingxing Jiang, Dongliang Zhang, Longyue Yang, Yihua Hu, Evaluation of rail potential and stray current with dynamic traction networks in multitrain subway systems, IEEE Transactions on Transportation Electrification, Jun. 2020, 6(2): 784–796. DOI: 10.1109/TTE.2020.2980745. [14]Guifu Du,Jun Wang*, Zhongkui Zhu, Yihua Hu, Dongliang Zhang, Effect of crossing power restraint on reflux safety parameters in multitrain subway systems, IEEE Transactions on Transportation Electrification, 2019, 5(2): 490–501. DOI: 10.1109/TTE.2019.2899207. [15]Jun Wang, Wei Qiao*, Liyan Qu, Wind turbine bearing fault diagnosis based on sparse representation of condition monitoring signals, IEEE Transactions on Industry Applications, 2019, 55(2): 1844–1852. DOI: 10.1109/TIA.2018.2873576. [16]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, 2019, 40(9): 16–26. DOI: 10.19650/j.cnki.cjsi.J1905083.(In Chinese) [17]Jun Wang, Fangzhou Cheng, Wei Qiao*, Liyan Qu, Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions, IEEE Transactions on Industrial Electronics, 2018, 65(5): 4268–4278.DOI: 10.1109/TIE.2017.2767520. [18]Jun Wang, Yayu Peng, Wei Qiao*, Jerry L. Hudgins, Bearing fault diagnosis of direct-drive wind turbines using multiscale filtering spectrum, IEEE Transactions on Industry Applications, 2017, 53(3): 3029–3038. DOI: 10.1109/TIA.2017.2650142. [19]Jun Wang, Qingbo He*, Wavelet packet envelope manifold for fault diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 2016, 65(11): 2515–2526. DOI: 10.1109/TIM.2016.2566838. [20]Jun Wang, Yayu Peng, Wei Qiao*, Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines, IEEE Transactions on Industrial Electronics, 2016, 63(10): 6336–6346. DOI: 10.1109/TIE.2016.2571258. [21]Jun Wang, Qingbo He*, Fanrang Kong, Multiscale envelope manifold for enhanced fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 2015, 52-53: 376–392. DOI: 10.1016/j.ymssp.2014.07.021. [22]Jun Wang, Qingbo He*, Fanrang Kong, Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 2015, 64(2): 564–577. DOI: 10.1109/TIM.2014.2347217. [23]Jun Wang, Qingbo He*, Fanrang Kong, An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings, Journal of Sound and Vibration, 2014, 333(26): 7401–7421. DOI: 10.1016/j.jsv.2014.08.041. [24]Jun Wang, Qingbo He*, Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery, Journal of Sound and Vibration, 2014, 333(11): 2450–2464. DOI: 10.1016/j.jsv.2014.01.006. [25]Jun Wang, Qingbo He*, Fanrang Kong, A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects, Applied Acoustics, 2014, 85: 69–81. DOI: 10.1016/j.apacoust.2014.04.005. [26]Jun Wang, Qingbo He*, Fanrang Kong, Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis, Mechanical Systems and Signal Processing, 2013, 40(1): 237–256. DOI: 10.1016/j.ymssp.2013.03.007. [27]Qingbo He*,Jun Wang, Fei Hu, Fanrang Kong, Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement, Journal of Sound and Vibration, 2013, 332(21): 5635–5649. DOI: 10.1016/j.jsv.2013.05.026. [28]Qingbo He*,Jun Wang, Effects of multiscale noise tuning on stochastic resonance for weak signal detection, Digital Signal Processing, 2012, 22(4): 614–621. DOI: 10.1016/j.dsp.2012.02.008. [29]Qingbo He*,Jun Wang, Yongbin Liu, Daoyi Dai, Fanrang Kong, Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines, Mechanical Systems and Signal Processing, 2012, 28: 443–457. DOI: 10.1016/j.ymssp.2011.11.021. ØReferee for Funds, Journals and Conferences
Updated in May 2024 Research
Teaching
Projects
PublicationsSelected Publications (*Corresponding author) [1] Jun Wang, He Ren, Changqing Shen, Weiguo Huang, Zhongkui Zhu*, Multi-scale style generative and adversarial contrastive networks for single domain generalization fault diagnosis, Reliability Engineering & System Safety, Mar. 2024, 243: 109879. DOI: 10.1016/j.ress.2023.109879. [2] He Ren,Jun Wang*, Weiguo Huang, Xingxing Jiang, Zhongkui Zhu, Domain-invariant feature fusion networks for semi-supervised generalization fault diagnosis, Engineering Applications of Artificial Intelligence, Nov. 2023, 126, Part D: 107117. DOI: 10.1016/j.engappai.2023.107117. [3] He Ren,Jun Wang*, Changqing Shen, Weiguo Huang, Zhongkui Zhu, Dual classifier-discriminator adversarial networks for open set fault diagnosis of train bearings, IEEE Sensors Journal, 2023, 23(18): 22040-22050. DOI: 10.1109/JSEN.2023.3301593. [4] Jun Dai,Jun Wang*,Linquan Yao, Weiguo Huang, Zhongkui Zhu, Categorical feature GAN for imbalanced intelligent fault diagnosis of rotating machinery, IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3525212. DOI:10.1109/TIM.2023.3298425. [5] He Ren,Jun Wang*, Zhongkui Zhu, Juanjuan Shi, Weiguo Huang, Domain fuzzy generalization networks for semi-supervised intelligent fault diagnosis under unseen working conditions, Mechanical Systems and Signal Processing, 2023, 200: 110579. DOI: 10.1016/j.ymssp.2023.110579. [6] Linghui Lu,Jun Wang*, Weiguo Huang, Changqing Shen, Juanjuan Shi, Zhongkui Zhu, Dual contrastive learning for semi-supervised fault diagnosis under extremely low label rate,IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3520512. DOI: 10.1109/TIM.2023.3284954. [7] 王俊,王玉琦,轩建平,刘金朝,黄伟国*,朱忠奎.车辆传动系统变参小波流形融合故障诊断方法.交通运输工程学报, 2023, 23(1): 170–183.DOI: 10.19818/j.cnki.1671-1637.2023.01.013.(In Chinese) [8] He Ren,Jun Wang*, Jun Dai, Zhongkui Zhu, Jinzhao Liu, Dynamic balanced domain adversarial networks for cross domain fault diagnosis of train bearings, IEEE Transactions on Instrumentation and Measurement, 2022, 71: 3514612. DOI: 10.1109/TIM.2022.3179468. [9] Guifu Du, Tao Jiang,Jun Wang*, Xingxing Jiang, Zhongkui Zhu, Improved multi-bandwidth mode manifold for enhanced bearing fault diagnosis, Chinese Journal of Mechanical Engineering, 2022, 35(1): 14. DOI: 10.1186/s10033-022-00677-5. [10] Xingxing Jiang,Jun Wang*, Changqing Shen, Juanjuan Shi, Weiguo Huang, Zhongkui Zhu, Qian Wang, An adaptive and efficient VMD and its application for bearing fault diagnosis, Structural Health Monitoring, Sep. 2021, 20(5): 2708-2725. DOI: 10.1177/1475921720970856. [11] Jun Wang, Guifu Du, Zhongkui Zhu, Changqing Shen, Qingbo He*, Fault diagnosis of rotating machines based on the EMD manifold, Mechanical Systems and Signal Processing, 2020, 135: 106443. DOI: 10.1016/j.ymssp.2019.106443. [12] Jun Dai,Jun Wang*, Weiguo Huang, Juanjuan Shi, Zhongkui Zhu, Machinery health monitoring based on unsupervised feature learning via generative adversarial networks, IEEE/ASME Transactions on Mechatronics, Oct. 2020, 25(5): 2252–2263. DOI: 10.1109/TMECH.2020.3012179. [13] Guifu Du,Jun Wang*, Xingxing Jiang, Dongliang Zhang, Longyue Yang, Yihua Hu, Evaluation of rail potential and stray current with dynamic traction networks in multitrain subway systems, IEEE Transactions on Transportation Electrification, Jun. 2020, 6(2): 784–796. DOI: 10.1109/TTE.2020.2980745. [14] Guifu Du,Jun Wang*, Zhongkui Zhu, Yihua Hu, Dongliang Zhang, Effect of crossing power restraint on reflux safety parameters in multitrain subway systems, IEEE Transactions on Transportation Electrification, 2019, 5(2): 490–501. DOI: 10.1109/TTE.2019.2899207. [15] Jun Wang, Wei Qiao*, Liyan Qu, Wind turbine bearing fault diagnosis based on sparse representation of condition monitoring signals, IEEE Transactions on Industry Applications, 2019, 55(2): 1844–1852. DOI: 10.1109/TIA.2018.2873576. [16] 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, 2019, 40(9): 16–26. DOI: 10.19650/j.cnki.cjsi.J1905083.(In Chinese) [17] Jun Wang, Fangzhou Cheng, Wei Qiao*, Liyan Qu, Multiscale filtering reconstruction for wind turbine gearbox fault diagnosis under varying-speed and noisy conditions, IEEE Transactions on Industrial Electronics, 2018, 65(5): 4268–4278.DOI: 10.1109/TIE.2017.2767520. [18] Jun Wang, Yayu Peng, Wei Qiao*, Jerry L. Hudgins, Bearing fault diagnosis of direct-drive wind turbines using multiscale filtering spectrum, IEEE Transactions on Industry Applications, 2017, 53(3): 3029–3038. DOI: 10.1109/TIA.2017.2650142. [19] Jun Wang, Qingbo He*, Wavelet packet envelope manifold for fault diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 2016, 65(11): 2515–2526. DOI: 10.1109/TIM.2016.2566838. [20] Jun Wang, Yayu Peng, Wei Qiao*, Current-aided order tracking of vibration signals for bearing fault diagnosis of direct-drive wind turbines, IEEE Transactions on Industrial Electronics, 2016, 63(10): 6336–6346. DOI: 10.1109/TIE.2016.2571258. [21] Jun Wang, Qingbo He*, Fanrang Kong, Multiscale envelope manifold for enhanced fault diagnosis of rotating machines, Mechanical Systems and Signal Processing, 2015, 52-53: 376–392. DOI: 10.1016/j.ymssp.2014.07.021. [22] Jun Wang, Qingbo He*, Fanrang Kong, Adaptive multiscale noise tuning stochastic resonance for health diagnosis of rolling element bearings, IEEE Transactions on Instrumentation and Measurement, 2015, 64(2): 564–577. DOI: 10.1109/TIM.2014.2347217. [23] Jun Wang, Qingbo He*, Fanrang Kong, An improved multiscale noise tuning of stochastic resonance for identifying multiple transient faults in rolling element bearings, Journal of Sound and Vibration, 2014, 333(26): 7401–7421. DOI: 10.1016/j.jsv.2014.08.041. [24] Jun Wang, Qingbo He*, Exchanged ridge demodulation of time-scale manifold for enhanced fault diagnosis of rotating machinery, Journal of Sound and Vibration, 2014, 333(11): 2450–2464. DOI: 10.1016/j.jsv.2014.01.006. [25] Jun Wang, Qingbo He*, Fanrang Kong, A new synthetic detection technique for trackside acoustic identification of railroad roller bearing defects, Applied Acoustics, 2014, 85: 69–81. DOI: 10.1016/j.apacoust.2014.04.005. [26] Jun Wang, Qingbo He*, Fanrang Kong, Automatic fault diagnosis of rotating machines by time-scale manifold ridge analysis, Mechanical Systems and Signal Processing, 2013, 40(1): 237–256. DOI: 10.1016/j.ymssp.2013.03.007. [27] Qingbo He*,Jun Wang, Fei Hu, Fanrang Kong, Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement, Journal of Sound and Vibration, 2013, 332(21): 5635–5649. DOI: 10.1016/j.jsv.2013.05.026. [28] Qingbo He*,Jun Wang, Effects of multiscale noise tuning on stochastic resonance for weak signal detection, Digital Signal Processing, 2012, 22(4): 614–621. DOI: 10.1016/j.dsp.2012.02.008. [29] Qingbo He*,Jun Wang, Yongbin Liu, Daoyi Dai, Fanrang Kong, Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines, Mechanical Systems and Signal Processing, 2012, 28: 443–457. DOI: 10.1016/j.ymssp.2011.11.021. Books&Patents
Books
Patents
Honors
Supervision |
 |
Jun Wang |
Degree：Ph.D.
Graduate School：University of Science and Technology of China
Email：junking@suda.edu.cn
|
|
|
10Access |