A novel method to aging state recognition of viscoelastic sandwich structures

2021

IEEE Access

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

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

2021

IEEE Access

Self Cite

“…Qu et al proposed an aging state recognition method for viscoelastic sandwich structure based on adaptive second generation wavelet packet transform and multiwavelet support vector machine (SVM). 11 Then, Qu et al proposed another aging state recognition method for viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine. 12 In these methods, a great of training samples from different aging states are required, which limits their practical application.…”

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.

“…[1][2][3] Due to the perfect performance of energy dissipation, a viscoelastic sandwich structure is playing a very important role in the vibration damping, noise reduction, and sealing of mechanical equipment. [4][5][6] However, in the long-term transportation, storage, and usage process of mechanical equipment, due to influence by such environmental factors and their variations as temperature, corrosion, and vibration, the viscoelastic layer of viscoelastic sandwich structure would inevitably generate aging with different degrees. The aging of the viscoelastic layer will result in the service performance 1 degradation of the viscoelastic sandwich structure, thus affecting the functional performance and operational safety of mechanical equipment.…”

Section: Introductionmentioning

confidence: 99%

A novel aging state recognition method of a viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine

Structural Health Monitoring

Ding

et al. 2019

Self Cite

A viscoelastic sandwich structure is widely used in mechanical equipment, but therein viscoelastic layers inevitably suffer from aging which changes the dynamic characteristics of the structure and influences the whole performance of the equipment. Hence, accurate and automatic aging state recognition of the viscoelastic sandwich structure is very significant to monitor structural health state and guarantee equipment operating reliably. To fulfill this task, by analyzing the sensor-based vibration response signals, a novel aging state recognition approach of the viscoelastic sandwich structure based on permutation entropy of dual-tree complex wavelet packet transform and generalized Chebyshev support vector machine is proposed in this article. To extract effective aging feature information, the measured nonlinear and non-stationary vibration response signals are processed by dual-tree complex wavelet packet transform, and multiple permutation entropy features are extracted from the frequency-band signals to reflect structural aging states. For accurate and automatic aging state classification, generalized Chebyshev kernel is introduced, and multi-class generalized Chebyshev support vector machine is developed to classify structural aging states. In order to demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed and fabricated, and various structural aging states are created through the hot oxygen–accelerated aging of viscoelastic layers. The testing results show that the proposed method can recognize the different structural aging states accurately and automatically. In addition, the superiority of dual-tree complex wavelet packet transform in processing vibration response signals and the performance of generalized Chebyshev support vector machine in classifying structural aging states are respectively validated by comparing with the commonly used methods.