Aging State Detection of Viscoelastic Sandwich Structure Based on ELMD and Sensitive IA Spectrum Entropy

• Main Authors: Jinxiu Qu, Changquan Shi
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Ensemble local mean decomposition; IA spectrum entropy; feature selection; aging state detection; viscoelastic sandwich structure
• Online Access: https://ieeexplore.ieee.org/document/8850043/

Viscoelastic sandwich structure is playing an important role in mechanical equipment, but therein viscoelastic material inevitably suffers from aging which affects structural service performance and the whole performance of equipment. Therefore, the aging state detection of viscoelastic sandwich structure based on vibration response signal is essential for monitoring the health state of structure and guaranteeing the operation safety of equipment. However, the weakness of structural vibration response variation caused by material aging make this task challenging. In this paper, a novel method based on ensemble local mean decomposition (ELMD) and sensitive IA spectrum entropy is proposed for this task. As an adaptive nonlinear and non-stationary signal processing method, ELMD is introduced to decompose the structural vibration response signal, and a series of instantaneous amplitudes (IAs) are obtained. Then, the spectrum entropies of these IAs are developed to quantitatively assess the aging state of viscoelastic sandwich structure. However, the IA spectrum entropies have different sensitivities to the aging state. Therefore, the most sensitive IA spectrum entropy is selected with a distance evaluation technique to detect the aging state of viscoelastic sandwich structure. In order to demonstrate the effectiveness of the proposed method, the experimental device of a viscoelastic sandwich structure is designed, and different structural aging states are created through the accelerated aging of viscoelastic material. The results show the outstanding performance of the proposed method.