A data-driven approach for seismic damage detection of shear-type building structures using the fractal dimension of time-frequency features

2012 ◽  
Vol 20 (9) ◽  
pp. 1191-1210 ◽  
Author(s):  
Hui Li ◽  
Dongwang Tao ◽  
Yong Huang ◽  
Yuequan Bao
Keyword(s):  
Fractal Dimension ◽  
Damage Detection ◽  
Seismic Damage ◽  
Data Driven ◽  
Building Structures ◽  
Time Frequency ◽  
Data Driven Approach ◽  
Shear Type ◽  
Frequency Features

Empirical Mode Decomposition and its Extensions Applied to EEG Analysis: A Review

2018 ◽  
Vol 10 (02) ◽  
pp. 1840001 ◽  
Author(s):  
Catherine M. Sweeney-Reed ◽  
Slawomir J. Nasuto ◽  
Marcus F. Vieira ◽  
Adriano O. Andrade
Keyword(s):  
Empirical Mode Decomposition ◽  
Data Driven ◽  
Eeg Analysis ◽  
Time Frequency ◽  
Computer Interfaces ◽  
Mode Decomposition ◽  
Eeg Data ◽  
Wide Range ◽  
Challenges And Opportunities ◽  
Data Driven Approach

Empirical mode decomposition (EMD) provides an adaptive, data-driven approach to time–frequency analysis, yielding components from which local amplitude, phase, and frequency content can be derived. Since its initial introduction to electroencephalographic (EEG) data analysis, EMD has been extended to enable phase synchrony analysis and multivariate data processing. EMD has been integrated into a wide range of applications, with emphasis on denoising and classification. We review the methodological developments, providing an overview of the diverse implementations, ranging from artifact removal to seizure detection and brain–computer interfaces. Finally, we discuss limitations, challenges, and opportunities associated with EMD for EEG analysis.

scholarly journals Seismic Damage Detection of Moment Resisting Frame Structures Using Time-Frequency Features

2018 ◽  
Vol 2018 ◽  
pp. 1-18 ◽  
Author(s):  
Dongwang Tao ◽  
Qiang Ma ◽  
Shanyou Li
Keyword(s):  
Damage Detection ◽  
Plastic Hinge ◽  
Superposition Principle ◽  
Seismic Damage ◽  
Transformation Matrix ◽  
Gabor Wavelet ◽  
Plastic Hinges ◽  
Time Frequency ◽  
Moment Resisting Frame ◽  
Moment Resisting

To detect seismic damage of moment resisting frame (MRF) structures, a data-driven method using the fractal dimension (FD) of time-frequency feature (TFF) of structural seismic dynamic responses at measured stories is extended and refined. The TFF is defined as the real part of Gabor wavelet transform of translational interstory displacement, and FD is used to give a quantitative value to describe the calculated TFF. Static condensation method is first used to reduce the degrees-of-freedom (DOFs) of MRF and to express the rotational displacements using translational displacements. For linear MRF, the FDs of TFFs at all stories are the same using the definition of TFF and modal superposition principle. For damaged MRF with plastic hinges at the ends of beams and columns, the force analogy method is implemented to establish transformation matrix from plastic hinge rotations to translational interstory inelastic displacements. Due to the sparseness of the transformation matrix, plastic hinges only generate interstory inelastic displacements, which are low-frequency contents, in the vicinity of plastic hinges. Correspondingly, the FDs of TFFs of interstory displacements with inelastic component are different from the FDs of TFFs of the interstory displacements that do not contain inelastic component. A numerical simulation on a 16-story MRF was conducted. The simulation included 10 cases such as no damage or linear structure, plastic hinges in single-story beams, plastic hinges in single-story columns, plastic hinges in single-story beams and columns, and plastic hinges in multiple story beams and columns. The robustness to measurement noise was also investigated. The seismic damage detection results demonstrated that the proposed method was capable of locating the stories where the plastic hinges occurred.

Structural Seismic Damage Detection Using Fractal Dimension of Time-Frequency Feature

2013 ◽  
Vol 558 ◽  
pp. 554-560 ◽  
Author(s):  
Dong Wang Tao ◽  
Dong Yu Zhang ◽  
Hui Li
Keyword(s):  
Fractal Dimension ◽  
Structural Damage ◽  
Nonlinear Behavior ◽  
Fractal Dimensions ◽  
Seismic Damage ◽  
Shaking Table ◽  
Dynamic Responses ◽  
Structural Dynamic ◽  
Time Frequency ◽  
Frequency Feature

In this paper, a data-driven approach to localizing structural damage subjected to ground motion is proposed by using the fractal dimension of the time-frequency features of structural dynamic responses. The time-frequency feature is defined as the real part of wavelet coefficient and the fractal dimension adopts the box-counting method. It is shown that the proposed fractal dimensions at each story of linear system are identical, while the fractal dimension at the stories with nonlinearity is different from those at the stories with linearity. Therefore, the nonlinear behavior of structural damage caused by strong ground motions can be detected and localized through comparing the fractal dimensions of structural responses at different stories. Shaking table test on a uniform 16-story 3-bay steel frame with added friction dampers modelling interstory nonlinear behavior was conducted. The experiment results validate the effectiveness of the proposed method to localize single and multi seismic damage of structures.

scholarly journals Health Monitoring of Large-Scale Civil Structures: An Approach Based on Data Partitioning and Classical Multidimensional Scaling

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1646
Author(s):  
Alireza Entezami ◽  
Hassan Sarmadi ◽  
Behshid Behkamal ◽  
Stefano Mariani
Keyword(s):  
Multidimensional Scaling ◽  
Damage Detection ◽  
Health Monitoring ◽  
Large Scale ◽  
Environmental Variability ◽  
Data Driven ◽  
High Dimensional ◽  
Data Driven Approach ◽  
Low Dimensional ◽  
High Dimensional Datasets

A major challenge in structural health monitoring (SHM) is the efficient handling of big data, namely of high-dimensional datasets, when damage detection under environmental variability is being assessed. To address this issue, a novel data-driven approach to early damage detection is proposed here. The approach is based on an efficient partitioning of the dataset, gathering the sensor recordings, and on classical multidimensional scaling (CMDS). The partitioning procedure aims at moving towards a low-dimensional feature space; the CMDS algorithm is instead exploited to set the coordinates in the mentioned low-dimensional space, and define damage indices through norms of the said coordinates. The proposed approach is shown to efficiently and robustly address the challenges linked to high-dimensional datasets and environmental variability. Results related to two large-scale test cases are reported: the ASCE structure, and the Z24 bridge. A high sensitivity to damage and a limited (if any) number of false alarms and false detections are reported, testifying the efficacy of the proposed data-driven approach.

scholarly journals A Data-Driven Approach for the Diagnosis of Mechanical Systems Using Trained Subtracted Signal Spectrograms

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1055 ◽  
Author(s):  
Jiung Huh ◽  
Huan Pham Van ◽  
Soonyoung Han ◽  
Hae-Jin Choi ◽  
Seung-Kyum Choi
Keyword(s):  
Frequency Conversion ◽  
Time Synchronization ◽  
Expert Knowledge ◽  
Health Management ◽  
Industrial Robot ◽  
Mechanical Systems ◽  
Data Driven ◽  
Time Frequency ◽  
Data Driven Approach ◽  
The Difference

Toward the prognostic and health management of mechanical systems, we propose and validate a novel effective, data-driven fault diagnosis method. In this method, we develop a trained subtracted spectrogram, the so called critical information map (CIM), identifying the difference between the signal spectrograms of normal and abnormal status. We believe this diagnosis process may be implemented in an autonomous manner so that an engineer employs it without expert knowledge in signal processing or mechanical analyses. Firstly, the CIM method applies sequential and autonomous procedures of time-synchronization, time frequency conversion, and spectral subtraction on raw signal. Secondly, the subtracted spectrogram is then trained to be a CIM for a specific mechanical system failure by finding out the optimal parameters and abstracted information of the spectrogram. Finally, the status of a system health can be monitored accurately by comparing the CIM with an acquired signal map in an automated and timely manner. The effectiveness of the proposed method is successfully validated by employing a diagnosis problem of six-degree-of-freedom industrial robot, which is the diagnosis of a non-stationary system with a small amount of training datasets.

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