2006.9-2010.7 Beijing Jiaotong University, Mechanical Engineering and Automation, B.S., Beijing, China.
2010.9-2013.6 Beihang University, Industrial and Manufacturing System Engineering, M.S., Beijing, China.
2013.9-2017.8 Institut National des Sciences Appliquées de Toulouse, Mechanical Engineering, Ph.D, Toulouse, France.
2016.8-2016.11 University of Florida, Department of Mechanical and Aerospace Engineering, visiting scholar.
2018.11-present, Beihang University, assistant professor.
2017.8-2018.10, Nanjing University of Science and Technology.
Intelligent fault diagnostics, prognostics and health management, model-based and data-driven prognostics approaches, cyber-physical system.
(1) Y. Wang, C. Gogu, N. Binaud, C. Bes, J.Fu, A model-based prognostics method of fatigue crack growth in fuselage panels, Chinese Journal of Aeronautics, In press (2018)
(2) Y. Wang, C. Gogu, N. Binaud, C. Bes, R.T. Haftka, N.H. Kim, Predictive airframe maintenance strategies using model-based prognostics, Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability, (2018).
(3) H. Feng, R. Chen, Y. Wang, Feature extraction for fault diagnosis based on wavelet packet decomposition: An application on linear rolling guide, Advances in Mechanical Engineering, 10 (2018)
(4) Y. Wang, C. Gogu, N.H. Kim, R.T. Haftka, N. Binaud, C. Bes, Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information, Reliability Engineering & System Safety, in press (2017).
(5) Y. Wang, C. Gogu, N. Binaud, C. Bes, R.T. Haftka, N.H. Kim, A cost driven predictive maintenance policy for structural airframe maintenance, Chinese Journal of Aeronautics, 30 (2017) 1242-1257.
(6) Y. Wang, N. Binaud, C. Gogu, C. Bes, J. Fu, Determination of Paris' law constants and crack length evolution via Extended and Unscented Kalman filter: An application to aircraft fuselage panels, Mechanical Systems and Signal Processing, 80 (2016) 262-281.
Submitted to journal:
Intelligent diagnostics of lubrication states of ball screw using multiple time-frequency features with LSTM network.
A review on prognostics techniques in industry application-literature in the recent five years
(1) Y. Wang, C. Gogu, N. Binaud, C. Bes, Predicting remaining useful life by fusing SHM data based on Extended Kalman Filter, 25th European safety and reliability conference, Taylor & Francis, 2015, pp. 7-10.
(2) L.Dominique.Cot, Y.Wang, C.Bes, C.Gogu, Scheduled and SHM Structure Airframe Maintenance Applications Using a New Probabilistic Model,7th European Workshop on Structural Health Monitoring,Nantes, France.
(3) Y.Wang, N.H.Kim, R.Haftka, Physics-based prognostics-promises and challenges. Proceedings of the Asia Pacific Conference of the Prognostics and Health Management Society 2017
(4) Y.Wang, C.Gogu, N.Binaud, C.Bes, Comparing structural airframe maintenance strategies based on probabilistic estimates of the remaining useful service life