Improved Bayesian Model Updating Method for Frequency Response Function with Metrics Utilizing NHBFT-PCA

Jinhui Li, Zhenhong Deng, Yong Tang (), Siqi Wang, Zhe Yang, Huageng Luo, Wujun Feng and Baoqiang Zhang ()
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Jinhui Li: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Zhenhong Deng: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Yong Tang: AECC Hunan Aviation Powerplant Research Institute, Zhuzhou 412002, China
Siqi Wang: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Zhe Yang: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Huageng Luo: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Wujun Feng: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China
Baoqiang Zhang: School of Aerospace Engineering, Xiamen University, Xiamen 361000, China

Mathematics, 2024, vol. 12, issue 13, 1-20

Abstract: To establish a high-fidelity model of engineering structures, this paper introduces an improved Bayesian model updating method for stochastic dynamic models based on frequency response functions (FRFs). A novel validation metric is proposed first within the Bayesian theory by using the normalized half-power bandwidth frequency transformation (NHBFT) and the principal component analysis (PCA) method to process the analytical and experimental frequency response functions. Subsequently, traditional Bayesian and approximate Bayesian computation (ABC) are improved by integrating NHBFT-PCA metrics for different application scenarios. The efficacy of the improved Bayesian model updating method is demonstrated through a numerical case involving a three-degrees-of-freedom system and the experimental case of a bolted joint lap plate structure. Comparative analysis shows that the improved method outperforms conventional methods. The efforts of this study provide an effective and efficient updating method for dynamic model updating based on the FRFs, addressing some of the existing challenges associated with FRF-based model updating.

Keywords: uncertainty quantification; Bayesian model updating; principal component analysis (PCA); normalized half-power bandwidth frequency transformation (NHBFT); frequency response function (FRF) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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