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Aging state detection of viscoelastic sandwich structure using redundant second generation wavelet packet transform and fuzzy support vector data description

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2022年第5期21卷 2370-2385页

作者： Qu, Jinxiu Shi, Changquan Guo, Jinzhu Shi, Xiaowei Huang, Jiaqi Cao, Wei Sun, Jinjuan Xian Technol Univ Sch Mech & Elect Engn Xian 710021 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.

关键词： viscoelastic sandwich structure aging state detection vibration response signal analysis redundant second generation wavelet packet transform fuzzy support vector data description

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A Multi-Sensor Data Fusion Method for Intelligent Aging Condition Identification of viscoelastic sandwich structure

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IEEE ACCESS 2021年 9卷 63029-63042页

作者： Qu, Jinxiu Shi, Changquan Xian Technol Univ Sch Mech & Elect Engn Xian 710021 Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China

viscoelastic sandwich structure is playing an important role in mechanical equipment. The aging condition identification of viscoelastic sandwich structure is essential for monitoring structural health condition and guaranteeing the safe operation of mechanical equipment. The development of a reliable intelligent aging condition identification method is an ongoing attempt. By analyzing vibration response signals, a multi-sensor data fusion method using multi-scale permutation entropy (MPE) and ensemble-based incremental support vector machine (EISVM) is proposed in this paper. In this method, for obtaining complementary information, multiple sensors are mounted on the different locations of viscoelastic sandwich structure. For extracting effective feature information, MPE is introduced to reflect the structural aging condition change, and multiple feature sets of multiple sensors are obtained. With distance evaluation technique, multiple sensitive feature sets are respectively selected from the multiple original feature sets to discard irrelevant or redundant features. Based on ensemble learning and incremental learning, EISVM is developed to fuse all sensitive feature sets from different sensors for reliable intelligent classification. The proposed method is verified with a well-designed viscoelastic sandwich structure in which the viscoelastic layer subjected to accelerated aging. The testing results show the outstanding performance of the proposed method.

关键词： Feature extraction Aging sandwich structures Vibrations Support vector machines Intelligent sensors Data integration viscoelastic sandwich structure aging condition identification multi-sensor data fusion multi-scale permutation entropy ensemble-based incremental support vector machine

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Looseness condition feature extraction of viscoelastic sandwich structure using dual-tree complex wavelet packet-based deep autoencoder network

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第3期19卷 873-884页

作者： Si, Yue Zhang, Zhousuo Kong, Chuiqing Li, Shujuan Yang, Guigeng Hu, Bingbing Xian Univ Technol Sch Mech & Instrumental Engn Xian 710048 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg & Syst Engn Sch Mech Engn Xian Peoples R China China Natl Heavy Machinery Res Inst Co Ltd Xian Peoples R China

It is significant to perform looseness condition detection of viscoelastic sandwich structures to avoid serious accidents. Due to the multilayer characteristic of the viscoelastic sandwich structure, the vibration response signal of such structures is nonlinear and nonstationary. Furthermore, the looseness condition feature signal contained in the vibration response signal is very puny. Condition feature extraction has become a challenging task in the looseness condition detection of viscoelastic sandwich structures. Therefore, a novel method called dual-tree complex wavelet packet-based deep autoencoder network is proposed for this task. First, the vibration response signal of the viscoelastic sandwich structure is decomposed by dual-tree complex wavelet packet transform and the sub-band signals which contain rich energy are extracted. Then, the energies of the extracted sub-band signals are calculated to form a feature set. Finally, a deep autoencoder network is established to fuse the feature set, and the fused feature is viewed as the detection index to detect the looseness condition of the viscoelastic sandwich structure. The proposed method is applied to the connecting bolt looseness condition detection of the viscoelastic sandwich structure to validate its effectiveness. Compared with the detection method based on dual-tree complex wavelet packet transform and energy and the detection method based on dual-tree complex wavelet packet transform and permutation entropy, the results indicate that the effectiveness of the proposed method in this article is more superior to that of the other two methods.

关键词： Bolt looseness condition detection viscoelastic sandwich structure feature extraction dual-tree complex wavelet packet deep autoencoder network

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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

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2020年第1期19卷 156-172页

作者： Qu, Jinxiu Shi, Changquan Ding, Feng Wang, Wenjuan Xian Technol Univ Sch Mech & Elect Engn Xian 710021 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn State Key Lab Mfg Syst Engn Xian Peoples R China

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.

关键词： viscoelastic sandwich structure aging state recognition dual-tree complex wavelet packet transform permutation entropy generalized Chebyshev support vector machine

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Aging condition identification of viscoelastic sandwich structure based on empirical wavelet transform and Hilbert envelope demodulation

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COMPOSITE structureS 2019年 215卷 13-22页

作者： Si, Yue Zhang, Zhousuo Kong, Lingfei Li, Shujuan Wang, Quandai Kong, Chuiqing Li, Yan Xian Univ Technol Sch Mech & Instrumental Engn Xian 710048 Shaanxi Peoples R China Xi An Jiao Tong Univ Sch Mech Engn State Key Lab Mfg & Syst Engn Xian 710049 Shaanxi Peoples R China China Natl Heavy Machinery Res Inst Co Ltd Xian 710032 Shaanxi Peoples R China

viscoelastic sandwich structures (VSSs) have been widely used in mechanical equipment. The aging condition monitoring of VSSs is essential for fault prevention of mechanical equipment. However, the ageing condition identification of VSS is still a challenging task since the change of structural response caused by the ageing of viscoelastic sandwich structure (VSS) is very puny. Therefore, a novel identification method based on empirical wavelet transform (EWT) and Hilbert envelope demodulation is proposed for the ageing condition identification of VSS. First, EWT is constructed to decompose the vibration response signals of VSS into a set of sub-band signals, so that the aging condition feature components are allocated in a certain frequency band. Second, the sub-band signals that contain rich aging condition information are selected and used to reconstruct a new signal. Thirdly, the reconstructed signal is analyzed with Hilbert envelope demodulation to obtain an envelope spectrum. Finally, the low band energy of envelope spectrum is calculated as detection index to identify the aging condition of VSS. The proposed method is verified with a well-designed VSS in which the viscoelastic core subjected to accelerated ageing in a thermal-oxygen ambient. The results show the outstanding performance of the proposed method.

关键词： Aging condition identification viscoelastic sandwich structure Empirical wavelet transform Hilbert envelope demodulation

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Aging State Detection of viscoelastic sandwich structure Based on ELMD and Sensitive IA Spectrum Entropy

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IEEE ACCESS 2019年 7卷 140690-140702页

作者： Qu, Jinxiu Shi, Changquan Xian Technol Univ Sch Mech & Elect Engn Xian 710021 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China

viscoelastic sandwich structure is playing an important role in mechanical equipment, but therein viscoelastic material inevitably suffers from aging which affects structural service performance and the whole performance of equipment. Therefore, the aging state detection of viscoelastic sandwich structure based on vibration response signal is essential for monitoring the health state of structure and guaranteeing the operation safety of equipment. However, the weakness of structural vibration response variation caused by material aging make this task challenging. In this paper, a novel method based on ensemble local mean decomposition (ELMD) and sensitive IA spectrum entropy is proposed for this task. As an adaptive nonlinear and non-stationary signal processing method, ELMD is introduced to decompose the structural vibration response signal, and a series of instantaneous amplitudes (IAs) are obtained. Then, the spectrum entropies of these IAs are developed to quantitatively assess the aging state of viscoelastic sandwich structure. However, the IA spectrum entropies have different sensitivities to the aging state. Therefore, the most sensitive IA spectrum entropy is selected with a distance evaluation technique to detect the aging state of viscoelastic sandwich structure. In order to demonstrate the effectiveness of the proposed method, the experimental device of a viscoelastic sandwich structure is designed, and different structural aging states are created through the accelerated aging of viscoelastic material. The results show the outstanding performance of the proposed method.

关键词： Aging sandwich structures Entropy Vibrations Monitoring Feature extraction Frequency modulation Ensemble local mean decomposition IA spectrum entropy feature selection aging state detection viscoelastic sandwich structure

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Time-frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure

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MEASUREMENT SCIENCE AND TECHNOLOGY 2016年第7期27卷 075001-075001页

作者： Yan, Wang Zhang, Zhousou Qu, Jinxiu Sun, Chuang Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Shaanxi Peoples R China Case Western Reserve Univ Dept Mech & Aerosp Engn Cleveland OH 44106 USA

In general, a vibration signal consists of several frequency modulation (FM) components. Every component contains different information, and can be characterized by its instantaneous amplitude (IA) and instantaneous phase (IP). In engineering applications, conventional time-frequency analysis methods and signal decomposition methods have shown their power in investigating features of the vibration signal. However, they are limited in resolution and it is hard to analyze these FM components individually. To overcome these deficiencies, a novel signal decomposition algorithm, named time-frequency distribution decomposition (TFDD), is proposed in this paper, which reconstructs one FM component of the signal at a time by estimating its IP and IA. The IA and IP are approximated by two polynomial functions respectively. One important advantage of TFDD is that it can directly extract the component we are interested in. Therefore, we can analyze the key component of the signal with little influence from other components. This will help us to characterize the vibration signal more deeply. Furthermore, it is very stable to noise. This is conductive to protecting the information of the vibration signal. The effectiveness of the TFDD is validated by a numerical simulation and the study of the vibration response signal collected from a viscoelastic sandwich structure (VSS). From the value of permutation entropy of the component extracted by TFDD, the looseness state of the VSS is recognized.

关键词： viscoelastic sandwich structure structure health monitoring ensemble empirical mode decomposition time-frequency distribution decomposition permutation entropy

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Uncertainty propagation in modal analysis of viscoelastic sandwich structures using a stochastic collocation method

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JOURNAL OF sandwich structureS & MATERIALS 2021年第4期23卷 1141-1165页

作者： Wang, Tianyu Xu, Chao Hamdaoui, Mohamed Guo, Ning Gu, Liangxian Northwestern Polytech Univ Sch Astronaut Xian Peoples R China Univ Lorraine LEM3 UMR 7239 Metz France

This paper presents an uncertainty propagation analysis of viscoelastic sandwich laminated structures using a stochastic collocation method based on Smolyak's sparse grids approach. Shear deformation in the viscoelastic layers is accurately captured by means of layer-wise plate finite elements. Modal properties are determined by solving nonlinear eigenvalue problems due to the frequency-dependent nature of the considered viscoelastic materials. Variations in material properties and layer thicknesses are both treated in a probabilistic way. The variabilities of natural frequencies and modal loss factors of two different viscoelastic laminated plate structures (plane and cylindrical) are studied. Results show that the present method converges quickly with small sample sizes when compared to the classical Monte-Carlo method. The influence of the variability of several parameters on viscoelastic structure modal properties is commented.

关键词： Uncertainty propagation viscoelastic sandwich structure nonlinear eigenvalue problem stochastic collocation method Smolyak’ s sparse grids

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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

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SMART MATERIALS AND structureS 2014年第8期23卷

作者： Qu, Jinxiu Zhang, Zhousuo Wen, Jinpeng Guo, Ting Luo, Xue Sun, Chuang Li, Bing Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China China Acad Engn Phys Inst Syst Engn Mianyang 621999 Peoples R China

The viscoelastic sandwich structure is widely used in mechanical equipment, yet the structure always suffers from damage during long-term service. Therefore, state recognition of the viscoelastic sandwich structure is very necessary for monitoring structural health states and keeping the equipment running with high reliability. Through the analysis of vibration response signals, this paper presents a novel method for this task based on the adaptive redundant second generation wavelet packet transform (ARSGWPT), permutation entropy (PE) and the wavelet support vector machine (WSVM). In order to tackle the non-linearity existing in the structure vibration response, the PE is introduced to reveal the state changes of the structure. In the case of complex non-stationary vibration response signals, in order to obtain more effective information regarding the structural health states, the ARSGWPT, which can adaptively match the characteristics of a given signal, is proposed to process the vibration response signals, and then multiple PE features are extracted from the resultant wavelet packet coefficients. The WSVM, which can benefit from the conventional SVM as well as wavelet theory, is applied to classify the various structural states automatically. In this study, to achieve accurate and automated state recognition, the ARSGWPT, PE and WSVM are combined for signal processing, feature extraction and state classification, respectively. To demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed, and the different degrees of preload on the structure are used to characterize the various looseness states. The test results show that the proposed method can reliably recognize the different looseness states of the viscoelastic sandwich structure, and the WSVM can achieve a better classification performance than the conventional SVM. Moreover, the superiority of the proposed ARSGWPT in processing the complex vibration response s

关键词： adaptive redundant second generation wavelet packet transform permutation entropy wavelet support vector machine viscoelastic sandwich structure state recognition

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Dynamic and instability analyses of FG graphene-reinforced sandwich deep curved nanobeams with viscoelastic core under magnetic field effect

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COMPOSITES PART B-ENGINEERING 2019年 174卷 106966-000页

作者： Sobhy, Mohammed Abazid, Mohammad Alakel King Faisal Univ Fac Sci Dept Math & Stat POB 400 Al Hufuf 31982 Saudi Arabia Kafrelsheikh Univ Fac Sci Dept Math Kafrelsheikh 33516 Egypt

Based on the nonlocal strain gradient theory, the effect of an axial magnetic field on the free vibration and mechanical buckling responses of an FG graphene-reinforced sandwich deep curved nanobeam with viscoelastic core embedded in a viscoelastic medium is elucidated in this paper. The curved beam is also assumed to be exposed to axial external compressions. The volume fraction of the constituents of the composite face layers are presumed to be functionally graded through the thickness according to a layer-wise law. The material properties are calculated in the framework of Halpin-Tsai micromechanical scheme. Lorentz magnetic force is deduced employing electro-dynamic Maxwell's relations for a conducting body. According to a refined shear and normal deformations curved beam theory, the motion equations are introduced in the polar coordinates. Analytical solutions are obtained for the natural frequencies and critical buckling load of the viscoelastic sandwich curved nanobeams. By comparing the present results with the available data in the literature, the developed formulations are validated. Furthermore, other several numerical examples are performed to show the effects of different parameters including viscoelastic core thickness, magnetic field, structural and foundation damping factor, opening angle and graphene concentration on the frequency and buckling load of the viscoelastic sandwich curved beams.

关键词： viscoelastic sandwich structure Magnetic field Graphene-reinforced curved beam Dynamic analysis Buckling

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