Bayesian virtual sensing in structural dynamics
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[1] Jyrki Kullaa,et al. Virtual sensing of structural vibrations using dynamic substructuring , 2016 .
[2] Costas Papadimitriou,et al. Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering , 2011 .
[3] MengChu Zhou,et al. Nonlinear Bayesian estimation: from Kalman filtering to a broader horizon , 2017, IEEE/CAA Journal of Automatica Sinica.
[4] H. Lee,et al. Design of an FIR filter for the displacement reconstruction using measured acceleration in low‐frequency dominant structures , 2010 .
[5] C. Papadimitriou,et al. A dual Kalman filter approach for state estimation via output-only acceleration measurements , 2015 .
[6] Thiagalingam Kirubarajan,et al. Estimation with Applications to Tracking and Navigation , 2001 .
[7] Sung-Han Sim,et al. Displacement Estimation Using Multimetric Data Fusion , 2013, IEEE/ASME Transactions on Mechatronics.
[8] Daniel C. Kammer. Sensor placement for on-orbit modal identification and correlation of large space structures , 1991 .
[9] Jyrki Kullaa,et al. Sensor validation using minimum mean square error estimation , 2010 .
[10] Andrzej Lesniak,et al. Application of Kalman Filter to Noise Reduction in Multichannel Data , 2009, Schedae Informaticae.
[11] Sten Bay Jørgensen,et al. A systematic approach for soft sensor development , 2007, Comput. Chem. Eng..
[12] Xiao Lu,et al. Kalman filtering for delayed singular systems with multiplicative noise , 2016, IEEE/CAA Journal of Automatica Sinica.
[13] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[14] Peter Avitabile,et al. Full-Field Dynamic Stress/Strain from Limited Sets of Measured Data , 2011 .