Vibration-Based Damage Detection of Composite Wingbox Structures Using Improved Hilbert-Huang Transform

Chen, Huan Guo; Yan, Yi; Jiang, Jie Sheng

doi:10.4028/0-87849-413-8.539

Cited by 2 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The time domain waveform with wavelet decomposition method of intact and damaged structure can not reflect the damage status of a plate, but their energy spectrum can directly reflect the plate damage status. In order to extract structural damage message from structural dynamic response signals, Signals are decomposed into several suber-signals under various frequencies using wavelet packet analysis method [3][4][5] can be expressed as follows: A signal energy accordingly can be expressed as:…”

Section: Build Up and Extract The Structural Damage Characteristicsmentioning

confidence: 99%

Characteristics of Structure Small Damage Detection under Strong Noise Background of Wavelet of Potter Syndrome

Zhao¹,

Zhang²,

Zhang³

2014

AMM

View full text Add to dashboard Cite

This paper presents a study on the effect of input noise on the detect index of structural damage. The main procedures include that, for some structures with small damage, the different dynamics response are obtained under the given input function and various input noise level, and then, the structural damage index is constructed using the energy spectrum of the sub-signal decomposed by wavelet analysis. Results show that the relationship between the different input noise level and the damage index is nonlinear, and some optimal signal/noise ratio can be found in order to obtain the most sensitive damage index to small structural damage.

show abstract

Section: Build Up and Extract The Structural Damage Characteristicsmentioning

confidence: 99%

Characteristics of Structure Small Damage Detection under Strong Noise Background of Wavelet of Potter Syndrome

Zhao¹,

Zhang²,

Zhang³

2014

AMM

View full text Add to dashboard Cite

show abstract

“…Moreover, this technique does not include the time and frequency resolution concepts while representing the instantaneous frequency. [11][12][13] Therefore, the Hilbert-Huang spectrum shows uniform resolution in the full frequency range. 25 In this regard, it provides an advantage over the Fourier transform (FT)-based methods, as the time-frequency resolution of FT-based methods depends on the window function length.…”

Section: Introductionmentioning

confidence: 99%

“…There are various signal-processing algorithms 10 that have been used in structural damage detection. According to the literature, Hilbert–Huang transform (HHT) 11–17 and discrete wavelet transform (DWT) 18–23 are two types of powerful techniques that were applied in various ways to analyse the structural data. For example, an interesting study predicted the location and severity of damage through the hybrid approach.…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection and localization using a hybrid method and artificial intelligence techniques

Hoshyar

Samali

Liyanapathirana

et al. 2019

Structural Health Monitoring

View full text Add to dashboard Cite

In this article, an intelligent scheme for structural damage detection and localization is introduced by implementing a hybrid method using the Hilbert–Huang transform and the wavelet transform. First, the second derivatives of the Discrete Laplacian are computed on Hilbert spectrum parameters at each frequency coordinate, and then, in order to highlight the influence of damage on signals, the data are rescaled and weighted with respect to the variance to adjust the differences in amplitude at different scales. Afterwards, the anti-symmetric extension is applied to deal with the boundary distortion phenomenon. A two-dimensional map is created using the multi two-dimensional discrete wavelet transform. This generates the coefficient matrices of level 2 approximation and horizontal, vertical and diagonal details. Horizontal detail coefficients are used to localize damages due to its sensitiveness to any perturbation. Finally, the validity of the algorithm corresponding to various damage states, the single state damage and multiple state damage, is examined through experimental analysis. The results indicate that the proposed framework can effectively localize cracks on concrete and reinforced concrete beams and can provide reliable crack localization in the presence of noise up to 5% more than the expected noise. In addition, the detection problem is mapped to machine learning tasks (support vector machine, k-nearest neighbours and ensemble methods) to automate the damage detection process. The quality of the models is evaluated and validated using the features extracted from the horizontal detail coefficients. The numerical results show that the ensemble models outperform the other models with respect to accuracy, prediction speed and training time.

show abstract

Vibration-Based Damage Detection of Composite Wingbox Structures Using Improved Hilbert-Huang Transform

Cited by 2 publications

References 0 publications

Characteristics of Structure Small Damage Detection under Strong Noise Background of Wavelet of Potter Syndrome

Characteristics of Structure Small Damage Detection under Strong Noise Background of Wavelet of Potter Syndrome

Structural damage detection and localization using a hybrid method and artificial intelligence techniques

Contact Info

Product

Resources

About