A novel method to aging state recognition of viscoelastic sandwich structures

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.

[1]  Qiao Hu,et al.  Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble , 2007 .

[2]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[3]  Paulo A.F. Martins,et al.  A finite element model for the analysis of viscoelastic sandwich structures , 2011 .

[4]  Baoping Tang,et al.  A novel fault diagnosis model for gearbox based on wavelet support vector machine with immune genetic algorithm , 2013 .

[5]  Robert X. Gao,et al.  Multivariable wavelet finite element-based vibration model for quantitative crack identification by using particle swarm optimization , 2016 .

[6]  El Mostafa Daya,et al.  Review: Complex modes based numerical analysis of viscoelastic sandwich plates vibrations , 2011 .

[7]  Li Cheng,et al.  Development in vibration-based structural damage detection technique , 2007 .

[8]  Robert X. Gao,et al.  Wavelets for fault diagnosis of rotary machines: A review with applications , 2014, Signal Process..

[9]  Jiawei Xiang,et al.  Fault diagnosis of rolling bearing based on second generation wavelet denoising and morphological filter , 2015, Journal of Mechanical Science and Technology.

[10]  Lei Guo,et al.  Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description , 2009 .

[11]  Richard G. Baraniuk,et al.  Nonlinear wavelet transforms for image coding via lifting , 2003, IEEE Trans. Image Process..

[12]  Wei Cheng,et al.  State detection of explosive welding structure by dual-tree complex wavelet transform based permutation entropy , 2015 .

[13]  D. Hardin,et al.  Fractal Functions and Wavelet Expansions Based on Several Scaling Functions , 1994 .

[14]  Zhiwen Liu,et al.  Multi-fault classification based on wavelet SVM with PSO algorithm to analyze vibration signals from rolling element bearings , 2013, Neurocomputing.

[15]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[16]  Jing Yuan,et al.  Multiwavelet transform and its applications in mechanical fault diagnosis – A review , 2014 .

[17]  Yanyang Zi,et al.  Rotating machinery fault diagnosis using signal-adapted lifting scheme , 2008 .

[18]  Jiawei Xiang,et al.  Rolling element bearing fault detection using PPCA and spectral kurtosis , 2015 .

[19]  Li Zhang,et al.  Wavelet support vector machine , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[20]  Xiaoyan Zhang,et al.  Planetary gearbox condition monitoring of ship-based satellite communication antennas using ensemble multiwavelet analysis method , 2015 .

[21]  Haifeng Gao,et al.  A new method to detect cracks in plate-like structures with though-thickness cracks , 2014 .

[22]  Haifeng Gao,et al.  A hybrid fault diagnosis method using morphological filter–translation invariant wavelet and improved ensemble empirical mode decomposition , 2015 .

[23]  H. Xia,et al.  Dynamic analysis and shear connector damage identification of steel-concrete composite beams , 2012 .

[24]  Z. Lachiri,et al.  Broken rotor bar diagnosis in induction machines through stationary wavelet packet transform and multiclass wavelet SVM , 2013 .

[25]  E. Peter Carden,et al.  Vibration Based Condition Monitoring: A Review , 2004 .

[26]  Seong Beom Lee,et al.  Useful Lifetime Prediction of Rubber Components Using Accelerated Testing , 2010, IEEE Transactions on Reliability.

[27]  Wei Cheng,et al.  A novel manifold-manifold distance index applied to looseness state assessment of viscoelastic sandwich structures , 2014 .

[28]  Pizhong Qiao,et al.  Vibration-based Damage Identification Methods: A Review and Comparative Study , 2011 .

[29]  Bing Li,et al.  State recognition of the viscoelastic sandwich structure based on the adaptive redundant second generation wavelet packet transform, permutation entropy and the wavelet support vector machine , 2014 .

[30]  Jiawei Xiang,et al.  A simple method to detect cracks in beam-like structures , 2012 .

[31]  Michel Barlaud,et al.  Design of signal-adapted multidimensional lifting scheme for lossy coding , 2004, IEEE Transactions on Image Processing.

[32]  Gregory Dudek,et al.  Auto-correlation wavelet support vector machine , 2009, Image Vis. Comput..

[33]  Fulei Chu,et al.  Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography , 2004 .