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

Guo

et al. 2023

J. Phys.: Conf. Ser.

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The viscoelastic sandwich structure exhibits highly nonlinear dynamic characteristics, and minimal changes in signal caused by aging state alterations, thus posing a challenge for extracting its aging characteristics. A method is proposed for extracting aging features from viscoelastic sandwich structures using variational mode decomposition (VMD) and permutation entropy (PE). First, the original dynamic response signal is decomposed by VMD to acquire several intrinsic mode functions (IMF). Then the permutation entropy characteristics of each IMF are calculated. The research results show that there are obvious differences in the distribution of permutation entropy corresponding to each IMF component, which indicates that the permutation entropy feature extracted based on the variational modal decomposition is more susceptive to the change of the aging state of the structure, thus verifying the availability of this method for feature extraction.

Section: Introductionmentioning

confidence: 99%

A Novel Method to the Aging Feature Extraction of Viscoelastic Sandwich Structures

Guo

et al. 2023

J. Phys.: Conf. Ser.

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“…That is, the vibration response signals of viscoelastic sandwich structure carry plenty of feature information reflecting the structural aging condition change, vital feature information can be extracted with certain processing techniques, and structural aging condition identification can be achieved with the extracted feature information. On this basis, some scholars carried out the aging condition identification of viscoelastic sandwich structure by constructing sensitive index [8]- [10]. However, these methods demand on the skills of operators, cannot achieve intelligent identification.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Sensor Data Fusion Method for Intelligent Aging Condition Identification of Viscoelastic Sandwich Structure

2021

IEEE Access

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Viscoelastic sandwich structure is playing an important role in mechanical equipment. The aging condition identification of viscoelastic sandwich structure is essential for monitoring structural health condition and guaranteeing the safe operation of mechanical equipment. The development of a reliable intelligent aging condition identification method is an ongoing attempt. By analyzing vibration response signals, a multi-sensor data fusion method using multi-scale permutation entropy (MPE) and ensemblebased incremental support vector machine (EISVM) is proposed in this paper. In this method, for obtaining complementary information, multiple sensors are mounted on the different locations of viscoelastic sandwich structure. For extracting effective feature information, MPE is introduced to reflect the structural aging condition change, and multiple feature sets of multiple sensors are obtained. With distance evaluation technique, multiple sensitive feature sets are respectively selected from the multiple original feature sets to discard irrelevant or redundant features. Based on ensemble learning and incremental learning, EISVM is developed to fuse all sensitive feature sets from different sensors for reliable intelligent classification. The proposed method is verified with a well-designed viscoelastic sandwich structure in which the viscoelastic layer subjected to accelerated aging. The testing results show the outstanding performance of the proposed method.

“…14 Qu et al detected the aging state of viscoelastic sandwich structure based on ensemble local mean decomposition and sensitive instantaneous amplitude spectrum entropy. 15 However, without intelligent techniques, these methods demand the skills of operators, which reduces the real-time and accuracy in practical application. Therefore, an easy-to-implement and intelligent method is particularly significant to aging state detection of viscoelastic sandwich structure in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Aging state detection of viscoelastic sandwich structure using redundant second generation wavelet packet transform and fuzzy support vector data description

Structural Health Monitoring

Guo

et al. 2021

Self Cite

Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.