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This is a Experimental Study study.

September 29, 2025The International Journal of Acoustics and VibrationOpen Access

Identification and Prediction of Magnetorheological Damper Force Based on Deep Learning

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Authors

WHWei HuangBaidu (China)JXJian XuUniversity of Southern California

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Implication

This study demonstrates a CNN-LSTM model that accurately predicts magnetorheological damper forces, highlighting its application in vibration control.

Key Points

The CNN-LSTM model achieved superior accuracy in predicting output forces of magnetorheological dampers.
Using continuous wavelet transform, one-dimensional MRD force signals are transformed into two-dimensional time-frequency maps.
This study developed a novel approach that combines CNN for feature extraction and LSTM for sequence modeling.
Findings promote the use of deep learning technologies to enhance performance in engineering vibration control.

Cite This Study

Huang et al. (2025) studied this question.

synapsesocial.com/papers/68da58dcc1728099cfd1133b https://doi.org/10.20855/ijav.2025.30.32160

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Implication

This study demonstrates a CNN-LSTM model that accurately predicts magnetorheological damper forces, highlighting its application in vibration control.

Identification and Prediction of Magnetorheological Damper Force Based on Deep Learning

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Implication

Key Points

Cite This Study

Identification and Prediction of Magnetorheological Damper Force Based on Deep Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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