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

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.

[1]  Ruixiang Sun,et al.  Support vector machine with orthogonal Chebyshev kernel , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[2]  Souhir Zghal,et al.  Model reduction methods for viscoelastic sandwich structures in frequency and time domains , 2015 .

[3]  Raúl Alcaraz,et al.  A review on sample entropy applications for the non-invasive analysis of atrial fibrillation electrocardiograms , 2010, Biomed. Signal Process. Control..

[4]  Noureddine Zerhouni,et al.  Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression , 2015, IEEE Transactions on Instrumentation and Measurement.

[5]  Gabriele Comanducci,et al.  Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis , 2016 .

[6]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[7]  Robert X. Gao,et al.  Mechanical Systems and Signal Processing Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring , 2006 .

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

[9]  Bogdan Dumitrescu,et al.  Optimization of Symmetric Self-Hilbertian Filters for the Dual-Tree Complex Wavelet Transform , 2008, IEEE Signal Processing Letters.

[10]  Buddhi Wimarshana,et al.  Application of entropy in identification of breathing cracks in a beam structure: Simulations and experimental studies , 2018 .

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

[12]  Qingsong Xu,et al.  Impact detection and location for a plate structure using least squares support vector machines , 2014 .

[13]  Jinxiu Qu,et al.  Time-frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure , 2016 .

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

[15]  Jianwei Zhang,et al.  Operation conditions monitoring of flood discharge structure based on variance dedication rate and permutation entropy , 2018 .

[16]  Ivan W. Selesnick,et al.  The design of approximate Hilbert transform pairs of wavelet bases , 2002, IEEE Trans. Signal Process..

[17]  P. Torkzadeh,et al.  A machine-learning approach for structural damage detection using least square support vector machine based on a new combinational kernel function , 2016 .

[18]  Wei Liang,et al.  Dual-tree complex wavelet transform and SVD based acoustic noise reduction and its application in leak detection for natural gas pipeline , 2016 .

[19]  Zhousuo Zhang,et al.  Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing , 2016 .

[20]  Dongyuan Shi,et al.  Divisional fault diagnosis of large-scale power systems based on radial basis function neural network and fuzzy integral , 2013 .

[21]  Saied Taheri,et al.  Improved LMD, Permutation Entropy and Optimized K-Means to Fault Diagnosis for Roller Bearings , 2016, Entropy.

[22]  Ruqiang Yan,et al.  Permutation entropy: A nonlinear statistical measure for status characterization of rotary machines , 2012 .

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

[24]  Charles R. Farrar,et al.  Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures , 2018 .

[25]  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 .

[26]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[27]  Hakan A. Çirpan,et al.  A set of new Chebyshev kernel functions for support vector machine pattern classification , 2011, Pattern Recognit..

[28]  Yanyang Zi,et al.  Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform , 2010 .

[29]  Bing Li,et al.  A novel method to aging state recognition of viscoelastic sandwich structures , 2016 .

[30]  Wei Guo,et al.  A hybrid fault diagnosis method based on second generation wavelet de-noising and local mean decomposition for rotating machinery. , 2016, ISA transactions.

[31]  C. Bandt.  Ordinal time series analysis , 2005 .

[32]  Aboelmagd Noureldin,et al.  Wavelet Transform for Structural Health Monitoring: A Compendium of Uses and Features , 2006 .

[33]  Yi Hong,et al.  The Application of Dual-Tree Complex Wavelet Transform (DTCWT) Energy Entropy in Misalignment Fault Diagnosis of Doubly-Fed Wind Turbine (DFWT) , 2017, Entropy.

[34]  H. Ozkaramanli,et al.  Hilbert transform pairs of orthogonal wavelet bases: necessary and sufficient conditions , 2005, IEEE Transactions on Signal Processing.

[35]  Aryaz Baradarani,et al.  Sampled-Data Design of FIR Dual Filter Banks for Dual-Tree Complex Wavelet Transforms via LMI Optimization , 2008, IEEE Transactions on Signal Processing.

[36]  B. Pompe,et al.  Permutation entropy: a natural complexity measure for time series. , 2002, Physical review letters.

[37]  C. M. Mota Soares,et al.  Finite element model for damping optimization of viscoelastic sandwich structures , 2013, Adv. Eng. Softw..

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

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

[40]  Vikas Sharma,et al.  Gearbox fault diagnosis using RMS based probability density function and entropy measures for fluctuating speed conditions , 2017 .

[41]  Jianwei Zhang,et al.  Operating characteristic information extraction of flood discharge structure based on complete ensemble empirical mode decomposition with adaptive noise and permutation entropy , 2018 .

[42]  Jin Chen,et al.  Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means , 2010 .