Abstract
In this paper, we developed a lightweight, self-sensing electret-based dynamic vibration absorber (ESDVA) for micro-vibration suppressions. We modeled the electromechanical coupling procedure of the ESDVA based on the first principles and proposed a sensing model based on support vector regression machine (SVR). The SVR algorithm helps to linearize the original voltage generated by the electret for precise vibration sensing. A prototype of the ESDVA is fabricated, and the theoretical model and SVR algorithms are verified by experiments. According to experimental results, the ESDVA successfully reduced primary structure vibration amplitudes by up to 50% with a mass burden of 1.4% of the primary structure. The proposed sensing model achieve an accuracy rate of over 93.5% for vibration sensing and the robustness of the model was also assessed. Moreover, the advantages of the proposed electret-based sensing method over classical methods are discussed.
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Abbreviations
- m 1, m 2 :
-
Mass of the primary and secondary structures
- x 1, x 2 :
-
Displacement of the primary and secondary structures
- k 1, k 2 :
-
Stiffness of the primary and secondary structures
- c 2 :
-
Damping of the secondary structure
- F :
-
Excitation force
- B 1, B 2 :
-
Amplitudes of the primary and secondary structures
- ϕ 1, ϕ 2 :
-
Phase angles of the primary and secondary structures
- ω n 2 :
-
Resonant frequency of the primary structure
- C :
-
Capacitance between the electrode plates
- x :
-
Distance between the primary and secondary structures
- A :
-
Area of the electrode plate
- h 0 :
-
Initial air gap
- h 1 :
-
Electret thickness
- ε 0, ε r :
-
Relative dielectric coefficients of air and electret
- R :
-
Load resistance
- V surf, V sen :
-
Surface voltage of electret and sensing voltage
- C p :
-
Parasitic capacitance
- Q, Q 0 :
-
Charge on the electrodes and its initial value
- y (q):
-
A hyperplane in high-dimensional space
- w :
-
Weight vector
- q :
-
Input feature vector
- b :
-
Bias term
- ε :
-
Distance of support vectors to hyperplane
- ξ i :
-
Slack variable
- D :
-
Penalty coefficient
- α :
-
Lagrange multipliers
- K (q i , q) :
-
Kernel function
- γ :
-
Hyperparameter determines the width of the Gaussian curve
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Acknowledgements
This Research is supported by the National Natural Science Foundation of China (No. 52205137).
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The work is funded by the National Natural Science Foundation of China (No. 52205137).
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Guoping Liu: Conceptualization, Writing-Original Draft, Methodology, Formal Analysis, Visualization. Zhaoshu Yang: Conceptualization, Writing-Review & Editing, Project administration, Funding acquisition, Formal analysis. Zhongbo He: Resources, Supervision, Formal analysis. Kai Tao: Resources, Formal analysis, Methodology. Jingtao Zhou: Data Curation, Software. Sen Li: Validation, Software. Wei Hu: Software, Investigation. Minzheng Sun: Investigation, Supervision.
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Liu, G., Yang, Z., He, Z. et al. An Electret-Based Self-Sensing Micro-Vibration Absorber and the Modeling Based on Support Vector Regression Algorithm. Microgravity Sci. Technol. 35, 44 (2023). https://doi.org/10.1007/s12217-023-10069-6
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DOI: https://doi.org/10.1007/s12217-023-10069-6