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A frequency-domain sequential Bayesian filter for sparse and broadband force estimation problems
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DOI：10.1016/j.ymssp.2025.112729.png)
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Abstract
En 中文
This paper presents a novel method for estimating the external sources acting on a mechanical structure in the frequency domain. Under the assumption of spatially sparse and broadband sources, a sequential Bayesian filter is derived. Its general structure follows that of a sequential Kalman-like filter, which is commonly used for input-state estimation problems in the time domain. This paper also includes an original Bayesian method for computing the noise variances of each measurement channel, which is a key element for the proper tuning of the proposed filtering algorithm. The proposed method is validated by a numerical experiment and an experimental application. The numerical experiment considers a simply supported beam subjected to a broadband point force under different operating conditions, while the experimental application deals with the identification of a point force acting on a simply supported plate. The comparison made with approaches available in the literature shows that the proposed strategy is able to estimate the external forces acting on a mechanical structure with the best trade-off between computational time/resources and accuracy.
Keywords:
Linear inverse problem
Force identification
Space-frequency approach
Bayesian filter
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