Research on the sensing properties and vibration reduction performance of self-sensing self-tuning magnetic fluid damper

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper (SSMFD) is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper’s natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit’s output induced electromotive force model is created, followed by an expression of the damper’s natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage. Finally, an experimental platform was developed to assess the damper’s self-sensing and self-tuning vibration reduction performance. The experimental results show that the proposed SSMFD performs well, making it a feasible solution for achieving self-sensing and self-tuning vibration reduction.

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Publication Details

Journal
Measurement Science and Technology
Published
2024-11-08
DOI
https://doi.org/10.1088/1361-6501/ad903c
Citations
1
Primary Topic
Advanced Algorithms and Applications
Type
article
Field-Weighted Citation Impact
0.33

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article

Research on the sensing properties and vibration reduction performance of self-sensing self-tuning magnetic fluid damper

Wenrong Yang, M Zhang
1 citations
Measurement Science and Technology
Advanced Algorithms and Applications
0.33
article

Research on the sensing properties and vibration reduction performance of self-sensing self-tuning magnetic fluid damper

Wenrong Yang, M Zhang
article en
1 citations

Abstract

Abstract The semi-active control damping system has gained popularity due to its quick response time and versatility. However, external sensors are susceptible to environmental interference, affecting system reliability and increasing complexity and maintenance costs, restricting their use. To address this, a self-sensing self-tuning magnetic fluid damper (SSMFD) is proposed. The vibration-measuring induction coil is wound on the damper to sense the magnetic fluid vibration information in real time, and the vibration signal is communicated to the self-tuning control circuit. The control circuit calculates and determines the dominant frequency of structural vibration, then outputs the relevant current signal to set the damper’s natural frequency to track the excitation frequency, resulting in self-tuning vibration reduction. First, the self-sensing unit’s output induced electromotive force model is created, followed by an expression of the damper’s natural frequency, indicating that the self-sensing unit can achieve self-tuning vibration reduction by tracking the excitation frequency. The multi-field coupling simulation model of the magnetic fluid damper is generated, and the induction coil coupling mode and damper excitation angle are defined to obtain the maximum induced voltage. Finally, an experimental platform was developed to assess the damper’s self-sensing and self-tuning vibration reduction performance. The experimental results show that the proposed SSMFD performs well, making it a feasible solution for achieving self-sensing and self-tuning vibration reduction.

Measurement Science and TechnologyVol. 36(1)
Hebei University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 40%
Advanced Algorithms and Applications
0.33
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