A time series based damage detection method for obtaining separate mass and stiffness damage features of shear-type structures

2020 ◽  
Vol 208 ◽  
pp. 109914 ◽  
Author(s):  
Ngoan T. Do ◽  
Mustafa Gül
Keyword(s):  
Time Series ◽  
Damage Detection ◽  
Detection Method ◽  
Shear Type

Substructure Damage Detection Method for Shear Structure Using Sub-Time Series and ARMAX

2013 ◽  
Vol 558 ◽  
pp. 149-159 ◽  
Author(s):  
Liu Mei ◽  
Akira Mita ◽  
Zhen Hua Xing
Keyword(s):  
Time Series ◽  
Damage Detection ◽  
Degrees Of Freedom ◽  
Detection Method ◽  
Processing System ◽  
Computing Power ◽  
Detection Systems ◽  
Iterative Computation ◽  
Substructure Approach ◽  
Shear Structure

A local damage detection method for shear structure based on substructure approach and sub-time series superposition is proposed, which is promising for application in parallel and distributed damage detection systems. This method requires only three sensors to identify localized damage in any story of a building and it doesnt require constructing the structure models. A substructure method was used in this method to divide a complete structure into several substructures. Each substructure has a considerably smaller number of degrees of freedom (DOFs) which makes fewer requirements on the computing power of the data processing system. The algorithm is simple, which involves only algebra calculation and does not need any iterative computation. Moreover the damage detection process can be independently conducted on each substructure. Thus, this method is easy, efficient and suitable for being embedded into parallel and distributed damage detection systems. To better assess the performance of the method proposed above, experimental verification of the proposed approach has been conducted, which shows the proposed method works quite well and stably.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

scholarly journals Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation

2021 ◽  
Vol 11 (10) ◽  
pp. 4589
Author(s):  
Ivan Duvnjak ◽  
Domagoj Damjanović ◽  
Marko Bartolac ◽  
Ana Skender
Keyword(s):  
Boundary Conditions ◽  
Damage Detection ◽  
Mode Shape ◽  
Experimental Validation ◽  
Detection Method ◽  
Dynamic Properties ◽  
Damage Index ◽  
Main Principle ◽  
The Difference ◽  
Different Levels

The main principle of vibration-based damage detection in structures is to interpret the changes in dynamic properties of the structure as indicators of damage. In this study, the mode shape damage index (MSDI) method was used to identify discrete damages in plate-like structures. This damage index is based on the difference between modified modal displacements in the undamaged and damaged state of the structure. In order to assess the advantages and limitations of the proposed algorithm, we performed experimental modal analysis on a reinforced concrete (RC) plate under 10 different damage cases. The MSDI values were calculated through considering single and/or multiple damage locations, different levels of damage, and boundary conditions. The experimental results confirmed that the MSDI method can be used to detect the existence of damage, identify single and/or multiple damage locations, and estimate damage severity in the case of single discrete damage.

Elimination of outlier measurements for damage detection of structures under changing environmental conditions

2021 ◽  
pp. 147592172199847
Author(s):  
William Soo Lon Wah ◽  
Yining Xia
Keyword(s):  
Damage Detection ◽  
Multiple Regression ◽  
Natural Frequencies ◽  
Detection Method ◽  
Detection Methods ◽  
Structure Model ◽  
Bridge Structure ◽  
Beam Structure ◽  
Dependent Variables ◽  
The Difference

Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.

scholarly journals A Deep Learning-Based Framework for Damage Detection with Time Series

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Qun Yang ◽  
Dejian Shen ◽  
Wencai Du ◽  
Weijun Li
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Damage Detection

Study on Damage Detection of Bridge Based on Wavelet Multi-Scale Analysis

2013 ◽  
Vol 639-640 ◽  
pp. 1010-1014 ◽  
Author(s):  
Ke Ding ◽  
Ting Peng Chen
Keyword(s):  
Wavelet Transform ◽  
Damage Detection ◽  
Detection Method ◽  
Mode Shapes ◽  
Scale Analysis ◽  
Multi Scale ◽  
Damage Location ◽  
Transform Coefficients ◽  
Beam Bridge ◽  
Multi Scale Analysis

The damage detection method based on wavelet multi-scale analysis is presented in the paper. The damage location can be identified by analyzing the multi-scale wavelet transform coefficients of curvatures of mode shapes. The extreme value of wavelet transform coefficients indicates the damage location. But it is difficult to detect the location of defect if the defect is near to the equilibrium position of vibration. In order to solve this problem, we put forward a method which is to add the wavelet transform coefficients of multi modals together. The method can effectively overcome the above problem. Three damage situations of simply supported beam bridge are discussed in the paper. The results show that the peaks of wavelet transform coefficients indicate the damage location of structural. It is possible to pinpoint the damage location based on wavelet multi-scale analysis on curvatures of mode shapes.

Gear Damage Detection Using Chaotic Dynamics Techniques: A Preliminary Study

1995 ◽  
Author(s):  
M. Farid Golnaraghi ◽  
DerChyan Lin ◽  
Paul Fromme
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Damage Detection ◽  
Correlation Dimension ◽  
Chaotic Dynamics ◽  
Crack Detection ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Nonlinear Time Series Analysis ◽  
Preliminary Study

Abstract This paper is a preliminary study applying nonlinear time series analysis to crack detection in gearboxes. Our investigations show that the vibration signal emerging from a gearbox is chaotic. Appearance of a crack in a gear tooth alters this response and hence the chaotic signature. We used correlation dimension and Lyapunov exponents to quantify this change. The main goal of this study is to point out the great potential of these methods in detection of cracks and faults in machinery.

A full-process damage detection method using small MOSFET and protection diode

1996 ◽  
Vol 17 (12) ◽  
pp. 563-565 ◽  
Author(s):  
Donggun Park ◽  
Chenming Hu ◽  
Scott Zheng ◽  
Nguyen Bui
Keyword(s):  
Damage Detection ◽  
Detection Method ◽  
Process Damage
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