Structural damage detection in pipeline using Lamb waves and wavelet transform

Lamb waves have been widely used for structural damage detection. However, practical applications of this technique are still limited. One of the main reasons is due to the complexity of Lamb wave propagation modes. Therefore, instead of directly analysing and interpreting Lamb wave propagation modes for information about health conditions of the structure, this study has proposed another approach that is based on statistical analyses of the stationarity of Lamb waves. The method is validated by using Lamb wave data from intact and damaged aluminium plates exposed to temperature variations. Four popular unit root testing methods, including Augmented Dickey–Fuller (ADF) test, Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, Phillips–Perron (PP) test, and Leybourne–McCabe (LM) test, have been investigated and compared in order to understand and make statistical inference about the stationarity of Lamb wave data before and after hole damages are introduced to the aluminium plate. The separation between t-statistic features, obtained from the unit root tests on Lamb wave data, is used for damage detection. The results show that both ADF test and KPSS test can detect damage, while both PP and LM tests were not significant for identifying damage. Moreover, the ADF test was more stable with respect to temperature changes than the KPSS test. However, the KPSS test can detect damage better than the ADF test. Moreover, both KPSS and ADF tests can consistently detect damages in conditions where temperatures vary below 60 °C. However, their t-statistics fluctuate more (or less homogeneous) for temperatures higher than 65 °C. This suggests that both ADF and KPSS tests should be used together for Lamb wave based structural damage detection. The proposed stationarity-based approach is motivated by its simplicity and efficiency. Since the method is based on the concept of stationarity of a time series, it can find applications not only in Lamb wave based SHM but also in condition monitoring and fault diagnosis of industrial systems.

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Structural damage detection using artificial neural networks and wavelet transform

2012 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings ◽

10.1109/cimsa.2012.6269593 ◽

2012 ◽

Cited By ~ 7

Author(s):

Arthur Shi ◽

Xiao-Hua Yu

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Wavelet Transform ◽

Damage Detection ◽

Structural Damage ◽

Structural Damage Detection ◽

Artificial Neural

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Structural damage detection by wavelet transform and probabilistic neural network

10.1117/12.602012 ◽

2005 ◽

Cited By ~ 1

Author(s):

Guirong Yan ◽

Zhongdong Duan ◽

Jinping Ou

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Damage Detection ◽

Structural Damage ◽

Probabilistic Neural Network ◽

Structural Damage Detection

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

Structural Health Monitoring ◽

10.1177/1475921719887768 ◽

2019 ◽

Vol 19 (5) ◽

pp. 1507-1523 ◽

Cited By ~ 1

Author(s):

Azadeh Noori Hoshyar ◽

Bijan Samali ◽

Ranjith Liyanapathirana ◽

Afsaneh Nouri Houshyar ◽

Yang Yu

Keyword(s):

Wavelet Transform ◽

Damage Detection ◽

Hybrid Method ◽

Structural Damage ◽

Reinforced Concrete Beams ◽

Support Vector ◽

Discrete Wavelet ◽

Two Dimensional ◽

Structural Damage Detection ◽

Detection And Localization

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.

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