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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

机译:基于双树复小波包变换的置换熵和广义Chebyshev支持向量机的粘弹性三明治结构老化状态识别新方法

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摘要

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.
机译:粘弹性夹层结构广泛用于机械设备中,但是其中的粘弹性层不可避免地会老化,这会改变结构的动态特性并影响设备的整体性能。因此,粘弹性夹层结构的准确,自动的老化状态识别对于监测结构的健康状态并保证设备可靠地运行非常重要。为此,通过分析基于传感器的振动响应信号,提出了一种基于双树复小波包变换的置换熵和广义切比雪夫支持向量机的粘弹性夹层结构的老化状态识别方法。为了提取有效的老化特征信息,通过双树复小波包变换对测得的非线性和非平稳振动响应信号进行处理,并从频带信号中提取多个置换熵特征,以反映结构的老化状态。为了进行准确,自动的老化状态分类,引入了广义的Chebyshev核,并开发了多类广义的Chebyshev支持向量机对结构的老化状态进行分类。为了证明所提出方法的有效性,设计和制造了典型的粘弹性夹层结构,并通过热氧加速粘弹性层的老化产生了各种结构老化状态。测试结果表明,该方法能够准确,自动地识别出不同的结构老化状态。另外,通过与常用方法的比较,分别验证了双树复小波包变换在处理振动响应信号方面的优越性以及广义Chebyshev支持向量机在结构老化状态分类中的性能。

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