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

Abstract

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.

Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

“…Therefore, as an improved algorithm of PE, dispersion entropy (DE) is proposed, which has the advantages of little influence by burst signals and good stability [10]. These two kinds of entropies and their improved entropies have shown good results in various fields [11][12][13].…”

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

“…In 2002, PE was proposed for the first time; its advantages are simplicity, exceedingly fast calculation, robustness, etc. [ 25 , 26 , 27 ]. With the development of PE, it has gradually become more widely used in the field of SNS feature extraction, and the improved algorithms of PE were proposed and applied successively in the following years, such as reverse permutation entropy (RPE) [ 28 ], weighted-permutation entropy (W-PE) [ 29 , 30 ], and multi-scale permutation entropy (MPE) [ 31 , 32 ].…”

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“…However, these methods demand on the skills of operators, cannot achieve intelligent identification. Qu et al identified the aging condition of viscoelastic sandwich structure based on support vector machine [11]- [12]. These methods can achieve intelligent identification, but they only utilize the information from individual sensors, which restricts the accuracy and reliability of identification.…”