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Steel and Composite Structures   Volume 21, Number 6, August30 2016, pages 1183-1210
DOI: http://dx.doi.org/10.12989/scs.2016.21.6.1183
 
A novel method to aging state recognition of viscoelastic sandwich structures
Jinxiu Qu, Zhousuo Zhang, Xue Luo, Bing Li and Jinpeng Wen

 
Abstract     [Buy Article]
    Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, nonstationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.
 
Key Words
    viscoelastic sandwich structures; aging state recognition; vibration response signals; adaptive second generation wavelet packet transform; multiwavelet support vector machine
 
Address
(1) Jinxiu Qu, Zhousuo Zhang, Xue Luo, Bing Li:
State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049, People's Republic of China;
(2) Jinpeng Wen:
Institute of Systems Engineering, China Academy of Engineering Physics, Mianyang 621999, Peopl's Republic of China.
 

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