Damage detection of a flywheel shaft

2010 ◽  
Vol 7 ◽  
pp. 211-218 ◽  
Author(s):  
A.G. Khakimov
Keyword(s):  
Damage Detection ◽  
Natural Frequencies ◽  
Torsional Vibrations

Using three natural frequencies of torsional vibrations, it is possible to define the location and size of a transverse notch on the flywheel shaft.

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 Structural Damage Detection Using Supervised Nonlinear Support Vector Machine

2021 ◽  
Vol 5 (11) ◽  
pp. 303
Author(s):  
Kian K. Sepahvand
Keyword(s):  
Support Vector Machine ◽  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Training Data ◽  
Support Vector ◽  
Lightweight Structures ◽  
Straightforward Method ◽  
Classification Boundary ◽  
Nonlinear Support

Damage detection, using vibrational properties, such as eigenfrequencies, is an efficient and straightforward method for detecting damage in structures, components, and machines. The method, however, is very inefficient when the values of the natural frequencies of damaged and undamaged specimens exhibit slight differences. This is particularly the case with lightweight structures, such as fiber-reinforced composites. The nonlinear support vector machine (SVM) provides enhanced results under such conditions by transforming the original features into a new space or applying a kernel trick. In this work, the natural frequencies of damaged and undamaged components are used for classification, employing the nonlinear SVM. The proposed methodology assumes that the frequencies are identified sequentially from an experimental modal analysis; for the study propose, however, the training data are generated from the FEM simulations for damaged and undamaged samples. It is shown that nonlinear SVM using kernel function yields in a clear classification boundary between damaged and undamaged specimens, even for minor variations in natural frequencies.

VIBRATION-BASED STRUCTURAL DAMAGE DETECTION UNDER VARYING TEMPERATURE CONDITIONS

2013 ◽  
Vol 13 (05) ◽  
pp. 1250082 ◽  
Author(s):  
XIAO-QING ZHOU ◽  
WEN HUANG
Keyword(s):  
Damage Detection ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Environmental Changes ◽  
Model Updating ◽  
Modal Testing ◽  
Structural Damage Detection ◽  
Vibration Data ◽  
Temperature Conditions ◽  
Vibration Properties

In vibration-based structural damage detection, it is necessary to discriminate the variation of structural properties due to environmental changes from those caused by structural damages. The present paper aims to investigate the temperature effect on vibration-based structural damage detection in which the vibration data are measured under varying temperature conditions. A simply-supported slab was tested in laboratory to extract the vibration properties with modal testing. The slab was then damaged and the modal testing was conducted again, in which the temperature varied. The modal data measured under different temperature conditions were used to detect the damage with a two-stage model updating technique. Some damage was falsely detected if the temperature variation was not considered. Natural frequencies were then corrected to those under the same temperature conditions according to the relation between the temperature and material modulus. It is shown that all of the damaged elements can be accurately identified.

Structural Health Monitoring Using Synchrosqueezed Wavelet Transform on IASC-ASCE Benchmark Phase I

2020 ◽  
Vol 20 (12) ◽  
pp. 2050138
Author(s):  
Wilson D. Sanchez ◽  
Jose V. de Brito ◽  
Suzana M. Avila
Keyword(s):  
Wavelet Transform ◽  
Dynamic Response ◽  
Phase I ◽  
Damage Detection ◽  
Health Monitoring ◽  
Natural Frequencies ◽  
Detection Methods ◽  
State Of Health ◽  
Civil Structures ◽  
Synchrosqueezed Wavelet Transform

Civil structures suffer deterioration either for years of service, deficiency due to environmental factors or damages caused by factors such as earthquakes, winds, impact loads, and cyclical loads. When a structure ages, it is necessary to know its state of health and make a decision of maintenance or replacement. When a structure such as a bridge or building is subjected to destructive environmental forces, determining its state of health becomes a priority since its recovery is urgently required to function normally. Structural Health Monitoring (SHM) is a technology that aims to prevent the collapse of structures and loss of human life through early diagnosis of the health status of a structure. There are a large number of damage detection methods that can be classified into (1) non-destructive testing methods, (2) dynamic characteristics-based damage detection methods, (3) dynamic response-based, (4) multi-scale damage detection method and (5) damage detection methods with consideration of uncertainties. In this work, it is implemented synchrosqueezed wavelet transform (SWT), which can be classified as a methods based on the dynamic response. To validate the robustness of the method it is identified first, the natural frequencies of the Benchmark Phase I without damage, which consists of a steel structure of 4-story [Formula: see text] bay 3D steel frame structure subjected to ambient vibrations. Subsequently, some damage patterns are validated according to IASC-ASCE SHM Task Group. The results obtained in the identification of natural frequencies are compared with those reported in literature. SWT was efficient, presenting a minimum error of 0.12[Formula: see text] and a maximum of 3.06[Formula: see text] in the identification of natural frequencies about the AISCE-ASCE group model. SWT overcomes some other damage detection methods, which are deficient in the identification of closely spaced frequencies, commonly present in many civil structures due to symmetric geometry or similar physical properties in different directions.

Detecting Loosening of Bolted Connections in a Pipeline Using Changes in Natural Frequencies

2011 ◽  
Author(s):  
K. He ◽  
W. D. Zhu
Keyword(s):  
Damage Detection ◽  
Natural Frequencies ◽  
Detection Method ◽  
Complex Geometry ◽  
Early Stage ◽  
Measurement Noise ◽  
Modeling Error ◽  
Bolted Connections ◽  
Bolted Connection ◽  
Stiffness Reduction

Loosening of bolted connections in a structure can significantly reduce the load-bearing capacities of the structure. Detecting loosening of bolted connections at an early stage can avoid failure of the structure. Due to the complex geometry of a bolted connection and the material discontinuity between the clamped components, it is difficult to detect loosening of a bolted connection using conventional non-destructive test methods. A vibration-based method that uses changes in natural frequencies of a structure to detect the locations and extent of damage can be used to detect loosening of bolted connections, since the method focuses on detecting a stiffness reduction, which can result from loosening of the bolted connections. Experimental and numerical damage detection using the vibration-based method was conducted to detect the loosening of the bolted connections in a fullsize steel pipeline with bolted flanges. With the recent development of a predictive modeling technique for bolted connections in thin-walled structures, an accurate physics-based finite element model of the pipeline that is required by the vibration-based damage detection method is developed. A trust-region search strategy is employed to improve the damage detection method so that convergence of the damage detection algorithm can be ensured for under-determined systems, and the robustness of the algorithm can be enhanced when relatively large modeling error and measurement noise are present. The location and extent of the loosened bolted connections were successfully detected in experimental damage detection using changes in the natural frequencies of the first several modes; the exact location and extent of the loosened bolted connections can be detected in the numerical simulation where there are no modeling error and measurement noise.

Response-Only Frequency-Domain Method for Structural Damage Detection

2009 ◽  
Author(s):  
Shuncong Zhong ◽  
S. Olutunde Oyadiji
Keyword(s):  
Damage Detection ◽  
Frequency Domain ◽  
Natural Frequency ◽  
Structural Damage ◽  
Natural Frequencies ◽  
Frequency Domain Method ◽  
Structural Damage Detection ◽  
Frequency Curve ◽  
Domain Method ◽  
Auxiliary Mass

This paper proposes a response-only method in frequency domain for structural damage detection by using the derivative of natural frequency curve of beam-like structures with a traversing auxiliary mass. The approach just uses the response time history of beam-like structures and does not need the external source of force excitation. The natural frequencies of a damaged beam with a traversing auxiliary mass change due to change in flexibility and inertia of the beam as the auxiliary mass is traversed along the beam. Therefore the auxiliary mass can enhance the effects of the crack on the dynamics of the beam and, therefore, facilitating locating the damage in the beam. That is, the auxiliary mass can be used to probe the dynamic characteristic of the beam by traversing the mass from one end of the beam to the other. However, it is impossible to obtain accurate modal frequencies by the direct operation of the Fast Fourier Transform of the response data of the structure because the frequency spectrum can be only calculated from limited sampled time data which results in the well-known leakage effect. A spectrum correction method is employed to estimate high accurate frequencies of structures with a traversing auxiliary mass. In the present work, the modal responses of damaged simply supported beams with auxiliary mass are computed using the Finite Element Analysis. The graphical plots of the natural frequencies versus axial location of auxiliary mass are obtained. The derivatives of natural frequency curve can provide crack information for damage detection of beam-like structures. However, it is suggested that the derivative do not go beyond the third derivative of natural frequency curves to avoid the difference approximation error which will be magnified at higher derivative. The sensitivity of crack index for different noise, crack depth, auxiliary mass and damping ratio are also investigated. The simulated result demonstrated the efficiency and precision of the response-only frequency-domain method which can be recommended for the real application in structural damage detection.

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