Sensor selection is one of the key factors that dictate the performance of estimating vertical wheel forces in vehicle durability design. To select K most relevant sensors among S candidate ones that best fit the response of one vertical wheel force, it has S!/(K!(S-K)!) possible choices to evaluate, which is not practical unless K or S is small. In order to tackle this issue, this paper proposes a data-driven method based on maximizing the marginal likelihood of the data of the vertical wheel force without knowing the dynamics of vehicle systems. Although the resulting optimization problem is a mixed-integer programming problem, it is relaxed to a convex problem with continuous variables and linear constraints. The proposed sensor selection method is flexible and easy to implement, and no additional hyper-parameters needed to be tuned using cross-validation. The feasibility and effectiveness of the proposed method are verified using experimental data in vehicle durability design. The results show that the proposed method has good performance with different data sizes and model orders, in providing sub-optimal sensor configurations for estimating vertical wheel forces in vehicles.

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