Application of machine learning in vibration energy harvesting from rotating machinery using jeffcott rotor model
學年 114
學期 1
出版（發表）日期 2025-08-29
作品名稱 Application of machine learning in vibration energy harvesting from rotating machinery using jeffcott rotor model
作品名稱（其他語言）
著者 Yi-Ren Wang; Chien-Yu Chen
單位
出版者
著錄名稱、卷期、頁數 Energies 18(17) ,p. 4591
摘要 This study presents a machine learning-based framework for predicting the electrical output of a vibration energy harvesting system (VEHS) integrated with a Jeffcott rotor model. Vibration induced by rotor imbalance is converted into electrical energy via piezoelectric elements, and the system’s dynamic response is simulated using the fourth-order Runge–Kutta method across varying mass ratios, rotational speeds, and eccentricities. The resulting dataset is validated experimentally with a root-mean-square error below 5%. Three predictive models—Deep Neural Network (DNN), Long Short-Term Memory (LSTM), and eXtreme Gradient Boosting (XGBoost)—are trained and evaluated. While DNN and LSTM yield a high predictive accuracy (R2 > 0.9999), XGBoost achieves comparable accuracy (R2 = 0.9994) with significantly lower computational overhead. The results demonstrate that among the tested models, XGBoost provides the best trade-off between speed and accuracy, achieving R2 > 0.999 while requiring the least training time. These results demonstrate that XGBoost might be particularly suitable for real-time evaluation and edge deployment in rotor-based VEHS, offering a practical balance between speed and precision.
關鍵字 vibration energy harvesting (VEH);rotating machinery;Jeffcott rotor model;machine learning (ML);deep neural network (DNN);long short-term memory (LSTM);eXtreme Gradient Boosting (XGBoost)
語言 en_US
ISSN
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Yi-Ren Wang
審稿制度 否
國別 CHE
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/128296 )

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