Structural Health Monitoring with Sensor Data and Cosine Similarity for Multi-Damages

This work deals with structural health monitoring for jacket-type foundations of offshore wind turbines. In particular, a vibration-response-only methodology is proposed based on accelerometer data and deep convolutional neural networks. The main contribution of this article is twofold: (i) a signal-to-image conversion of the accelerometer data into gray scale multichannel images with as many channels as the number of sensors in the condition monitoring system, and (ii) a data augmentation strategy to diminish the test set error of the deep convolutional neural network used to classify the images. The performance of the proposed method is analyzed using real measurements from a steel jacket-type offshore wind turbine laboratory experiment undergoing different damage scenarios. The results, with a classification accuracy over 99%, demonstrate that the stated methodology is promising to be utilized for damage detection and identification in jacket-type support structures.

Section: Introductionmentioning

confidence: 99%

Vibration-Response-Only Structural Health Monitoring for Offshore Wind Turbine Jacket Foundations via Convolutional Neural Networks

Puruncajas

Vidal

Tutivén

2020

“…The latter has large fluctuations in the data series, which are due to the term-by-term division operation with a small denominator for obtaining the transformation matrix Q i . The equivalent excitation force matrices ˆe F in Equation (26) and ' e F in Equation 28 The diagonal elements of matrix Q i obtained from the vertical IRFs at nodes 5 and 12 are plotted in Figure 6. There are large values in the diagonal elements numbered 198, 204, 248, 280, and 284 as shown in Figure 6a, which correspond to the large fluctuations in the nonzero equivalent excitation force in the IRF estimation at node 5 as shown in Figure 5a.…”

Section: Comparison Of Transformation Matrixes Q I and Q Imentioning

confidence: 99%

“…More recently, Lin and Xu [15][16][17][18][19] proposed a covariance-based multi-sensing damage detection method with optimal sensor placement in which the damage index was sensitive to local damage but insensitive to measurement noise. Other researchers also developed data-driven damage detection methods incorporating artificial intelligence algorithms [20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Structural Damage Diagnosis-Oriented Impulse Response Function Estimation under Seismic Excitations

Lin

Wang

et al. 2019

Impulse response function (IRF) is an ideal structural damage index for the identification of structural damage associated with changes in modal properties. However, IRFs from multiple excitations applied at different degrees-of-freedoms jointly contribute to the dynamic response, and their estimation is often underdetermined. Although some efforts have been devoted to the estimation of IRF for a structure under single excitation, the case under multiple excitations has not been fully investigated yet. The estimation of IRF under multiple excitations is generally an ill-conditioned inverse problem such that an incorrect or non-feasible solution is common, preventing its application to damage detection. This work explores this problem by introducing dimensionality reduction transformation matrices relating two sets of IRFs of a structure with discussions on the performance of the non-unique transformation matrices. Then, the extraction of IRF via wavelet-based and Tikhonov regularization-based methods are compared. Finally, a numerical study with a truss structure is conducted to validate the estimation of the IRFs and to demonstrate their applicability for damage detection under seismic excitations. Both the damage locations and severity are accurately identified, indicating the proposed methodology can enable the IRFs estimation under multiple excitations for successful damage detection.

“…Using the advanced sensing technique, structural health monitoring (SHM) technique can diagnose the structural damage and assess the structural safety of bridges by using the different types of structural response [ 1 , 2 , 3 , 4 ]. The core mission of SHM is to detect potential damage of bridges; thus, some methods for damage detection have been proposed [ 5 , 6 , 7 , 8 ], and among them, vibration-based approaches have shown excellent potential. Bridges inevitably suffer from the actions caused by varying environmental temperatures; furthermore, the abovementioned actions may mask the changes in damage features—e.g., the natural frequencies of bridges—caused by structural damage.…”

Section: Introductionmentioning

confidence: 99%

Damage Detection of Bridges under Environmental Temperature Changes Using a Hybrid Method

Wang¹,

Gao

Liu

2020

Principal component analysis (PCA)-based method is popular for detecting the damage of bridges under varying environmental temperatures. However, this method deletes some information when the damage features are projected in the direction of nonprincipal components; thus, the effectiveness of PCA-based methods will decrease if the deleted information is related to bridge damage. To address this issue, a hybrid method is proposed to detect the damage of bridges under environmental temperature changes. On one side, the PCA-based method is applied to deal with the nonprincipal components; on the other side, the Gaussian mixture method (GMM) is used to classify all the principal components into different clusters, and then the novel detection method is implemented to detect bridge damage for each cluster. In this way, all the damage feature information is saved and used to detect bridge damage. The numerical example and example of an actual bridge show that the proposed hybrid method is effective in detecting bridge damage under environmental temperature changes. The GMM is effective for classifying the natural monitoring frequency data of actual bridges, and the relationship between the natural frequencies of actual bridges and the environmental temperature is not always linear.