Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 152, Issue -, Pages -Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2020.107412
Keywords
High-speed railway; Vehicle-bridge system; Adaptive extended Kalman filter; Frequency identification; Track irregularity identification; On-board monitoring
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Funding
- National Natural Science Foundation of China [51838006]
- Natural Science Foundation of Hubei Province, China [2018CFB429]
- Fundamental Research Funds for the Central Universities [HUST_2018KFYYX JJ007]
- Technology Innovation Special Projects of Hubei Province [2017ACA183]
- Natural Science Fund for Distinguished Young of Hubei Province [2017CFA074]
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This paper proposes a novel algorithm for simultaneously identifying the frequencies and track irregularities of high-speed railway bridges using vehicle dynamic responses. The algorithm accelerates the convergence of estimation with an adaptive procedure, providing a low-cost and high-efficient approach.
Bridge frequencies and track irregularities are both the focuses of railway bridge condition assessment, which are coupled with each other in a vehicle-bridge system. Available algorithms face a great challenge when applied to simultaneously identify the natural frequencies and track irregularities of railway bridges using on-board measurement data. This paper proposes a novel algorithm for simultaneously identifying the frequencies and track irregularities of high-speed railway bridges using vehicle dynamic responses for the first time. An extended state-space model with unknown input condensation is established for time-dependent vehicle-bridge systems. We subsequently propose a new extended Kalman filter algorithm with an adaptive procedure for accelerating the convergence of estimation, which can simultaneously identify the frequencies and track irregularities of a railway bridge when a vehicle is running on it. The effectiveness of the proposed algorithm has been illustrated via numerical simulations of two real high-speed railway bridges. The proposed algorithm provides a low-cost and high-efficient approach for identifying the natural frequencies and track irregularities of high-speed railway bridges. (c) 2020 Elsevier Ltd. All rights reserved.
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