Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition

Saito, Akira; Tanaka, Masato

doi:10.1007/s11071-023-08958-x

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2024

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Cited by 3 publications

References 31 publications

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Dynamic mode decomposition with optimal amplitude and time-delay embedding for reconstruction and prediction of local measurements

Cao,

Zhang,

Zhang

et al. 2024

Nonlinear Dyn

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Dynamic mode decomposition with optimal amplitude and time-delay embedding for reconstruction and prediction of local measurements

Cao,

Zhang,

Zhang

et al. 2024

Nonlinear Dyn

View full text Add to dashboard Cite

Multilayer Extended Dynamic Mode Decomposition for Coupled Van der Pol Oscillators

Hasebe,

Uchiyama,

Masuda

2024

2024 28th International Conference on Methods and Models in Automation and Robotics (MMAR)

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Data-Driven Initial Gap Identification of Piecewise-Linear Systems Using Sparse Regression and Universal Approximation Theorem

Kanki,

Saito

2024

Journal of Computational and Nonlinear Dynamics

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This paper proposes a method for identifying an initial gap in piecewise-linear systems from data. Piecewise-linear systems appear in many engineered systems such as degraded mechanical systems and infrastructures, and are known to show strong nonlinearities. To analyze the behavior of such piecewise-linear systems, it is necessary to identify the initial gap, at which the system behavior switches. The proposed method identifies the initial gap by discovering the governing equations using sparse regression and calculating the gap based on the universal approximation theorem. A key step to achieve this is to approximate a piecewise-linear function by a finite sum of piecewise-linear functions in sparse regression. Equivalent gap is then calculated from the coefficients of the multiple piecewise-linear functions and their respective switching points in the obtained equation. The proposed method was first applied to a numerical model to confirm its applicability to piecewise-linear systems. Experimental validation of the proposed method has then been conducted with a simple mass-spring-hopping system, where the method successfully identified the initial gap in the system with high accuracy.

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Data-driven model order reduction for structures with piecewise linear nonlinearity using dynamic mode decomposition

Cited by 3 publications

References 31 publications

Dynamic mode decomposition with optimal amplitude and time-delay embedding for reconstruction and prediction of local measurements

Dynamic mode decomposition with optimal amplitude and time-delay embedding for reconstruction and prediction of local measurements

Multilayer Extended Dynamic Mode Decomposition for Coupled Van der Pol Oscillators

Data-Driven Initial Gap Identification of Piecewise-Linear Systems Using Sparse Regression and Universal Approximation Theorem

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