An fast Fourier transform–based correlation coefficient approach for structural damage diagnosis

Ngo, Nhi; Nguyen, Thi Hoa; Vu, Thu Van; Nguyen‐Xuan, H.

doi:10.1177/1475921720949561

Cited by 14 publications

(5 citation statements)

References 40 publications

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“…However, the variation in the data’s regression speed is still small in this experimental model without being clearly shown in the statuses of different disabilities under the progressive level. This result is completely consistent with many studies, 5,8,9,44 where cuts on the beam structure were made to indicate the structure’s defect-bearing status as being completely inappropriate. Obviously, many defects still exist in the case of implementing the experiment to evaluate structural damage through the cutting model.…”

Section: Results and Commentssupporting

confidence: 90%

“…Of many different assumptions, the most important is that the defects in a model will cause the beam's force-bearing capacity to be directly affected. The defects will then change either the dimensions or the geometry of the structure, [7][8][9] or the structure will change internally through changes in the material's mechanical properties. [10][11][12][13] Researchers often demonstrate defects using the crack model implemented in the laboratory, 1,2,14,15 with shapes of geometrical cuts, such as cutting, cross-cutting, longitudinal cutting, and depth cutting, or changes of the cutting geometry, 16,17 such as circular cutting, oval cutting, and oblique cutting.…”

Section: Introductionmentioning

confidence: 99%

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Structural health monitoring based on the cumulative function and cumulative circle of a defective beam under a moving load

Nguyen

2022

Journal of Low Frequency Noise, Vibration and Active Control

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A defective structure has been evaluated and identified in beam models’ deflection measurement signals in much research. However, many results have not been optimally exploited and evaluated to find new parameters for data excavation. This manuscript proposes a cumulative welding model and a cumulative circle of deflection signals for a defective beam under the impact of a moving force. Based on this evaluation model, the manuscript recognizes that the speed of the moving force will determine the regression speed of the received datasets, which is considered the basis for showing the working ability of a beam structure with defects. Moreover, the structure’s cumulative regression circle also shows its resilience in that structure after the impact of the load. The development of defects in the structure will depend on both the structure’s level and resilience after each bearing-load cycle. Our research has shown us that the method of evaluation and use of both the cumulative model and cumulative circle from the deflection’s measured value have yielded more results compared to the previous method. This research can evaluate many defects in different situations while simultaneously evaluating many types of structures.

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Section: Results and Commentssupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Structural health monitoring based on the cumulative function and cumulative circle of a defective beam under a moving load

Nguyen

2022

Journal of Low Frequency Noise, Vibration and Active Control

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“…The Fourier series coefficients define the amplitudes of the sine and cosine terms, each with its own frequency. A strategy for decomposing a recorded dynamic signal into its amplitude and frequency components has recently been presented in Nguyen 22 and Ngo et al 23 The dynamic portion of a signal of an arbitrary period T can be described in equation ( 1)…”

Section: Fourier Transform and Frequency Spectrummentioning

confidence: 99%

PSD characteristics for the random vibration signals used in bridge structural health monitoring in Vietnam based on a multi-sensor system

Nguyen

2022

International Journal of Distributed Sensor Networks

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This study proposes two parameters, including the appearance frequency of harmonics (AFH) and the change in shape of the power spectral density (PSD), which are examined to assess the decline in stiffness of a bridge span. PSDs are obtained from the real vibration signals of the randomized traffic load model based on accelerometer multi-sensors that indicate the change in mechanical behavior of the structure over time. In addition, AFHs evaluate the workability of the structure. With these parameters in mind, actual vibrations in real beam structures are studied with the aim of using structural health monitoring to assess the bearing capacity reduction on Saigon Bridge’s spans. The results show that AFHs and the high-frequency regions relate to the decreased stiffness of the bridge’s spans over a given period of time. In the future, this research can be used to monitor structural health for various types of structure materials and many different bridge spans.

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“…However, the signals collected during the machining processes are often very noisy. Many of the traditional techniques such as wavelet packet decomposition, 5,6 Fourier transformation, 7 and others are widely used to analyze system dynamics. 8 The learning effect in machine learning can be improved by removing noise with high recognizability.…”

Section: Introductionmentioning

confidence: 99%

Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets

Kuo

Luan

Tseng

et al. 2023

Structural Health Monitoring

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Chatter has a direct effect on the precision and life of machine tools and its detection is a crucial issue in all metal machining processes. Traditional methods focus on how to extract discriminative features to help identify chatter. In this study, an effective procedure for chatter data preprocessing is proposed that can improve neural network learning results from data of extremely low quantity. Different Computer numerical control (CNC) machines, cutting tools, operation conditions as well as workpiece material, and shapes all generate different dynamic behavior . Therefore, the same cutting conditions are processed in different CNCs, some will produce chatter, and some will not. In order to collect chatter signals of different cutting environments, the cost of materials and time is relatively high. Cutting chatter also leads to tool wear, which also increases the cost of data collection. This makes the use of CNCs for large-scale chatter testing experiments impractical. However, a way of producing accurate chatter test results from rather sparse data is needed. The solution to this practical problem involved an innovative data preprocessing and training strategy combined with a modified convolutional neural network and a deep convolutional generative adversarial net. Through the characteristics of a chaotic attractor, the variability of chatter data can be minimized. Moreover, the characteristics of a chaotic attractor are utilized where the chaotic system, very sensitive to the input, can distinguish data with chatter and without chatter to improve chatter detection and classification. Convolutional neural networks can be effective chatter classifiers, and adversarial networks can act as generators that produce more data. Original training data are collected and preprocessed by the Chen-Lee chaotic error mapping. Experimental results indicated that the generative adversarial network (GAN) model could generate better training data than the traditional data augmentation method. The convolutional neural networks were trained using augmented data produced by the generator network. The adversarial training process used these data to create the generator and the generator could produce enough data to compensate for the lack of training data. The experimental results are compared without a data generator and data augmentation. Using only 60 original data, the proposed method has an accuracy of 95.3% on leave-one-out cross-validation over 10 runs and surpassed other methods and models. The forged data are also compared with original training data as well as data produced by augmentation. The distribution shows that forged data have similar quality and characteristics to the original data. The proposed training strategy provides a high-quality deep learning chatter detection model.

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An fast Fourier transform–based correlation coefficient approach for structural damage diagnosis

Cited by 14 publications

References 40 publications

Structural health monitoring based on the cumulative function and cumulative circle of a defective beam under a moving load

Structural health monitoring based on the cumulative function and cumulative circle of a defective beam under a moving load

PSD characteristics for the random vibration signals used in bridge structural health monitoring in Vietnam based on a multi-sensor system

Machine tool chattering monitoring by Chen-Lee chaotic system-based deep convolutional generative adversarial nets

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