An IPNN-Based Parameter Identification Method for a Vibration Sensor Sensitivity Model

Honglong Li, Zhihua Liu, Chenguang Cai, Kemin Yao (), Jun Pan and Ming Yang ()
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Honglong Li: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Zhihua Liu: Institute of Mechanics and Acoustic Metrology, National Institute of Metrology, Beijing 100029, China
Chenguang Cai: Institute of Mechanics and Acoustic Metrology, National Institute of Metrology, Beijing 100029, China
Kemin Yao: School of Digital Economy and Finance, Guizhou University of Commerce, Guiyang 550014, China
Jun Pan: School of Digital Economy and Finance, Guizhou University of Commerce, Guiyang 550014, China
Ming Yang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China

Mathematics, 2025, vol. 13, issue 22, 1-15

Abstract: Vibration sensors, as critical components in motion control and measurement systems, have dynamic characteristics that directly affect measurement accuracy. However, existing sensitivity models, due to structural simplifications and parameter uncertainties, hinder conventional vibration and shock calibration methods from fully characterizing their dynamic performance. In addition, traditional parameter identification approaches are often noise-sensitive and lack interpretability, making them inadequate for high-precision applications. To address these challenges, this study proposes an Algorithm-Unrolled Interpretable Physics-Informed Neural Network (IPNN) for parameter identification of a vibration sensor sensitivity model. By integrating the physical characteristics of the sensors with vibration calibration data, the method enables high-precision parameter identification and interpretable dynamic modeling. Comparative experimental results show that the proposed IPNN reduces the RMSE of sensor voltage predictions by over 60% compared with GRU and LSTM and decreases the average full-frequency relative deviation from laser interferometry calibration results by approximately 65% relative to LSM.

Keywords: parameter identification; vibration sensor; IPNN; LSTM; GRU (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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