Application of support vector machine for pattern classification of active thermometry-based pipeline scour monitoring

Zhao, Xuefeng; Li, Weijie; Zhou, Lei; Song, Gangbing; Ba, Qin; Ho, Siu Chun Michael; Ou, Jinping

doi:10.1002/stc.1724

Cited by 31 publications

(21 citation statements)

References 18 publications

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“…Recently, many sparse optimization algorithms have been proposed in the fields of signal and image processing [16], compressive sensing [17], machine learning [25], and data mining [26] for consideration that the optimization variables have some sparse structures. Sparse optimization makes it possible to reconstruct high-dimensional signals and extract potential information from a small amount of data.…”

Section: Sparse Optimized Dmd Via Non-convex Regularizationmentioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components. A novel denoising and feature extraction algorithm for multi-component coupled noisy mechanical signals is proposed based on the standard DMD algorithm, which provides a new method solving the two intractable problems above. Firstly, a sparse optimization method of non-convex penalty function is adopted to determine the optimal dimensionality reduction space in the process of DMD, obtaining a series of optimal DMD modes. Then, multiscale permutation entropy calculation is performed to calculate the complexity of each DMD mode. Modes corresponding to the noise components are discarded by threshold technology, and we reconstruct the modes whose entropies are smaller than a threshold to recover the signal. By applying the algorithm to rolling bearing simulation signals and comparing with the result of wavelet transform, the effectiveness of the proposed method can be verified. Finally, the proposed method is applied to the experimental rolling bearing signals. Results demonstrated that the proposed approach has a good application prospect in noise reduction and fault feature extraction.

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Section: Sparse Optimized Dmd Via Non-convex Regularizationmentioning

confidence: 99%

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Dang

et al. 2018

Entropy

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“…This method was an outstanding technique for handling the description of multimodal data, making it robust with high computational efficiency [26]. Additionally, scientists employed support vector machines (SVM) to build individual classifiers per sample cluster [33,34]. Such a SVM was a linear classifier trained by only one single positive sample and multiple negative samples; it was denoted as Exemplar-SVM.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

et al. 2018

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Acoustic emission (AE) technique is a common approach to identify the damage of the refractories; however, there is a complex problem since there are as many as fifteen involved parameters, which calls for effective data processing and classification algorithms to reduce the level of complexity. In this paper, experiments involving three-point bending tests of refractories were conducted and AE signals were collected. A new data processing method of merging the similar parameters in the description of the damage and reducing the dimension was developed. By means of the principle component analysis (PCA) for dimension reduction, the fifteen related parameters can be reduced to two parameters. The parameters were the linear combinations of the fifteen original parameters and taken as the indexes for damage classification. Based on the proposed approach, the Gaussian mixture model was integrated with the Bayesian information criterion to group the AE signals into two damage categories, which accounted for 99% of all damage. Electronic microscope scanning of the refractories verified the two types of damage.

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“…By using SHM technologies, the real-time damage detection of various structures, including concrete structures [2][3][4][5][6], pipeline structures [7][8][9][10][11][12], and steel structures [13][14][15][16][17][18][19], can be investigated in order to provide early warning and hopefully to avoid accidents. In the aerospace industry, several application areas have garnered significant interests.…”

Section: Introductionmentioning

confidence: 99%

Experimental Damage Identification of a Model Reticulated Shell

Hao

et al. 2017

Applied Sciences

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Abstract:The damage identification of a reticulated shell is a challenging task, facing various difficulties, such as the large number of degrees of freedom (DOFs), the phenomenon of modal localization and transition, and low modeling accuracy. Based on structural vibration responses, the damage identification of a reticulated shell was studied. At first, the auto-regressive (AR) time series model was established based on the acceleration responses of the reticulated shell. According to the changes in the coefficients of the AR model between the damaged conditions and the undamaged condition, the damage of the reticulated shell can be detected. In addition, the damage sensitive factors were determined based on the coefficients of the AR model. With the damage sensitive factors as the inputs and the damage positions as the outputs, back-propagation neural networks (BPNNs) were then established and were trained using the Levenberg-Marquardt algorithm (L-M algorithm). The locations of the damages can be predicted by the back-propagation neural networks. At last, according to the experimental scheme of single-point excitation and multi-point responses, the impact experiments on a K6 shell model with a scale of 1/10 were conducted. The experimental results verified the efficiency of the proposed damage identification method based on the AR time series model and back-propagation neural networks. The proposed damage identification method can ensure the safety of the practical engineering to some extent.

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Application of support vector machine for pattern classification of active thermometry-based pipeline scour monitoring

Cited by 31 publications

References 18 publications

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy

Damage Detection of Refractory Based on Principle Component Analysis and Gaussian Mixture Model

Experimental Damage Identification of a Model Reticulated Shell

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