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

Abstract

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.

Keywords

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

Get full access to this article

View all access options for this article.

References

Moita

Araújo

Mota Soares

, et al. Finite element model for damping optimization of viscoelastic sandwich structures. Adv Eng Softw 2013; 66: 34–39.

Zghal

Bouazizi

Bouhaddi

, et al. Model reduction methods for viscoelastic sandwich structures in frequency and time domains. Finite Elem Anal Des 2015; 93: 12–29.

Ledi

Hamdaoui

Robin

, et al. An identification method for frequency dependent material properties of viscoelastic sandwich structures. J Sound Vib 2018; 428: 13–25.

Zhang

Kong

, et al. Aging condition identification of viscoelastic sandwich structure based on empirical wavelet transform and Hilbert envelope demodulation. Compos Struct 2019; 215: 13–22.

Sun

Zhang

Luo

, et al. Support vector machine-based Grassmann manifold distance for health monitoring of viscoelastic sandwich structure with material ageing. J Sound Vib 2016; 368: 249–263.

Zhang

Wen

, 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. Smart Mater Struct 2014; 23(8): 085004.

Sun

Zhang

Guo

, et al. A novel manifold–manifold distance index applied to looseness state assessment of viscoelastic sandwich structures. Smart Mater Struct 2014; 23(6): 065019.

Wang

Zhang

, et al. Time-frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure. Meas Sci Technol 2016; 27(7): 075001.

Yan

Gao

Chen

Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 2014; 96: 1–15.

10.

Zhang

Wang

, et al. Mono-modal feature extraction for bonding quality detection of explosive clad structure with optimized dual-tree complex wavelet transform. J Sound Vib 2017; 392: 346–366.

11.

Kumar

Singh

Shahani

, et al. Dual-tree complex wavelet transform-based control algorithm for power quality improvement in a distribution system. IEEE T Ind Electron 2017; 64(1): 764–772.

12.

Wang

Xiang

Zhong

, et al. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mech Syst Signal Pr 2018; 112: 154–170.

13.

Wang

Xiang

Zhong

, et al. Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowl-Based Syst 2018; 144: 65–76.

14.

Wang

Xiang

A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps. Soft Comput. Epub ahead of print 23 May 2019. DOI: 10.1007/s00500-019-04076-2.

15.

Jia

Lei

Lin

, et al. Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Pr 2016; 72: 303–315.

16.

Baldi

Autoencoders, unsupervised learning and deep architectures. In: Proceedings of the 2011 ICML workshop on unsupervised and transfer learning, Bellevue, WA, 28 June–2 July 2011, pp. 37–49. Washington, DC: JMLR.

17.

Serbes

Aydin

Modified dual tree complex wavelet transform for processing quadrature signals. Biomed Signal Proces 2011; 6(3): 301–306.

18.

Selesnick

Baraniuk

Kingsbury

NG.

The dual-tree complex wavelet transform. IEEE Signal Proc Mag 2005; 22(6): 123–151.

19.

Bayram

Selesnick

IW.

On the dual-tree complex wavelet packet and $M$-band transforms. IEEE T Signal Proces 2008; 56(6): 2298–2310.

20.

Wang

Enhancement of signal denoising and multiple fault signatures detecting in rotating machinery using dual-tree complex wavelet transform. Mech Syst Signal Pr 2010; 24(1): 119–137.

21.

Hinton

Salakhutdinov

RR.

Reducing the dimensionality of data with neural networks. Science 2006; 313(5786): 504–507.

22.

LeCun

Bengio

Hinton

Deep learning. Nature 2015; 521(7553): 436–444.

23.

Singhal

Gogna

Majumdar

Deep dictionary learning vs deep belief network vs stacked autoencoder: an empirical analysis. In: Proceedings of the international conference on neural information processing, Kyoto, Japan, 16–21 October 2016, pp. 337–344. Cham: Springer.

24.

Hinton

Osindero

Teh

Y-W.

A fast learning algorithm for deep belief nets. Neural Comput 2006; 18(7): 1527–1554.