Papers by Keyword: Damage Detection

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Authors: Bong Suk Kim, Soo Hun Lee, Jun Ni, Jun Yeob Song
Abstract: The goal of system monitoring and diagnostics is to minimize economic losses, to increase stability, to maximize productivity, and to maintain product quality in manufacturing. The feature extraction from the signals acquired in rotating machine is required for performance evaluation, condition monitoring, and fault diagnostics. In this paper, we extracted the distinctive features from vibration signals gathered in rotor-bearing system during acceleration in order to monitor an abnormal condition by using various kinds of signal processing methods such as the Fast Fourier Transform, Short-Time Fourier Transform, Wigner-Ville Distribution, and Discrete Wavelet Transform.
Authors: Goh Lyn Dee, Norhisham Bakhary, Azlan Abdul Rahman, Baderul Hisham Ahmad
Abstract: This paper investigates the performance of Artificial Neural Network (ANN) learning algorithms for vibration-based damage detection. The capabilities of six different learning algorithms in detecting damage are studied and their performances are compared. The algorithms are Levenberg-Marquardt (LM), Resilient Backpropagation (RP), Scaled Conjugate Gradient (SCG), Conjugate Gradient with Powell-Beale Restarts (CGB), Polak-Ribiere Conjugate Gradient (CGP) and Fletcher-Reeves Conjugate Gradient (CGF) algorithms. The performances of these algorithms are assessed based on their generalisation capability in relating the vibration parameters (frequencies and mode shapes) with damage locations and severities under various numbers of input and output variables. The results show that Levenberg-Marquardt algorithm provides the best generalisation performance.
Authors: Hoon Sohn, Charles R. Farrar, Francois M. Hemez, Gyuhae Park, Amy N. Robertson, Todd O. Williams
Authors: Xu Ge, Yun Ju Yan, Huan Guo Chen
Abstract: The paper presents an effective damage detection method of complex composite structures. It can be carried out through the experimental modal analysis of the damaged structure. The method using the improved Cross Modal Strain Energy (CMSE) technique and Niche GA has many advantages compared with other damage detection methods. The CMSE method can use any modes of the structure and the modes don’t need to be normalized or consistent in scale. The Niche GA improves the efficiency of the calculation and enhances the capacity of identifying structural damage localization. The model is the composite material airfoil case. The numerical results show that the method proposed in this paper is successful for damage detection of complex structures.
Authors: Yan Hui Zhang, Wen Yu Yang
Abstract: This paper researches a robust damage identification system considering the effects of the environmental and operational conditions based on distributed fiber Bragg grating system. Initially a well-verified method is used to identify damage, but the failure result is obtained. The environmental and operational variations causing the false-positive indication are analyzed, such as the temperature, the change of the excitation and the relaxation phenomenon of the sensors. The Hilbert-Huang Transform method is used to decompose the dynamic strain signal into several intrinsic mode function components, the physical meanings of which are discussed. Then the second level component is used as the damage-induced signal component. Lastly the damage index based on the autoregressive model and Mahalanobis distance is constructed to detect and locate damage.
Authors: Jun Wang
Abstract: This paper puts forward a method for components' damage detection based on optoelectronics imaging technology. According to the collection principle of optoelectronics imaging system, using pixel gray scale difference approach, the method extracts pixel characteristics of the optoelectronics image, which is taken as data foundation of following damage detection. Edge pixel positioning method is adopted to calculate spatial location of optoelectronics data of the components' damaged area edge, thus enabling components damage detection. Experimental results show that the method can improve the accuracy of components optoelectronics damage detection and meets the needs of quality inspection in component production.
Authors: Wei Ming Yan, Da Peng Gu, Yan Jiang Chen, Wei Ning Wang
Abstract: A damage detection method using BP neural network based on a novel damage index, the correlation characteristic of the acceleration response, is proposed, and is evaluated through the FEM simulation and experiment verification. On the basis of achievements in existence, the feasibility of using the correlation characteristic as damage index is validated theoretically. The damage detection for a simple-supported beam using the proposed method was FEM simulated. The results showed that the trained BP neural network can correctly detect the location and extent of damages in both single damage case and multi-damage case. A model test of a reinforced concrete simple-supported beam was performed to verify the validity and efficiency of the damage detection method. From the results of the model test, it is shown that the trained BP neural network can correctly locate the damage mostly detect the extent of damage. A conclusion is given that the novel damage detection method using the correlation characteristic of the acceleration response as damage index is feasible and efficient.
Authors: Francesca Lanata, Daniele Posenato
Abstract: In recent years, several structures have been equipped with permanent monitoring systems, able to record the response both in terms of displacements and strains over very long periods of time and, theoretically, for the entire life of the structure. Despite of the number of applications, very few studies have been presented focused on the interpretation of the data without the study of a numerical model of the structure. Since an optimal and unique algorithm cannot be proposed depending on the variety of applications, the aim of the work is to propose a multi-algorithm methodology as a tool for detecting and localizing the insurgence of damage or material degradation from the measurements taken during a continuous static monitoring of civil structures. A method based on Principal Component Analysis will be proposed in order to compare the responses and detect the insurgence of anomalous behaviors. The algorithm will be first tested on simulated data deriving from a numerical benchmark with sensors and different damage scenarios, then the proposed methodology will be validated on a real structure. In this second application, due to the great number of installed sensors, the algorithm will be integrated with a preliminary analysis in order to cluster and gather together the sensors with a comparable behavior and a similar sensitivity to damage.
Authors: Yan Sheng Song, Zong Guang Sun, Nai Zhi Zhao
Abstract: This paper demonstrates a new abnormal index based on frequency change for structural health monitoring (SHM) which utilizes probability and statistics method. And it was introduced to analyze a steel frame. The results show it could indicate the abnormity of corresponding test cases clearly.
Authors: Nai Zhi Zhao, Shi Yan
Abstract: Damage detection in composite materials can be divided into active and passive approaches. The active approach is usually based on various non-destructive techniques utilizing actuators and/or receivers. Simple laser scans, revealing the change in Lamb wave response amplitudes, have been used to locate delamination and estimate its severity in a composite plate. The validity of the proposed method is demonstrated through experimental studies in which input signals exerted at piezoelectric (PZT) patches on a composite plate are successfully reconstructed by using the time reversal method. The ultimate goal of this study is to develop a reference-free damage diagnosis technique based on the time reversal process so that defects can be identified without relying on any past baseline data.
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