Development of a Seismic Detection Technology for High-Speed Trains Using Signal Analysis Techniques

Moon, Jae Sang; Yoo, Mintaek

doi:10.3390/s20133708

Cited by 5 publications

(5 citation statements)

References 25 publications

(22 reference statements)

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“…Figure 19 shows the lateral acceleration measurement data of the train and the analysis results of STFT, WT, and WVD. STFT and WT results are the same as the results from Moon and Yoo [19]. First, high energy appeared at approximately 100 Hz for all time intervals.…”

Section: Train Datamentioning

confidence: 54%

“…Following discussion and analysis are provided based on the comparison. Some of the results presented here are the same as in the paper by Moon and Yoo [19]. These are used in this study as a comparison.…”

Section: Resultsmentioning

confidence: 95%

“…Therefore, the signal processing system which detects earthquakes in real-time from the lateral movement of the train is a must. Moon and Yoo analyzed vibration data taken from onboard acceleration sensors during train operations and evaluated their earthquake detection capability potential [19]. The limit of this study is that the study considers earthquake response from the general roadbed is measured to the sensor.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Signal-Analysis Techniques for Seismic Detection System for High-Speed Train Data: Effect of Bridge Structures

Yoo

Moon²

2020

Sensors

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This study evaluated the earthquake warning system for high-speed trains using onboard accelerometers instead of expensive seismometers. Onboard accelerometers measure the train data additional to the earthquake acceleration. The measured earthquake acceleration could also be modified by railroad-supporting bridges. To develop the data analysis system, the virtual onboard data sets are synthesized using the train acceleration data and earthquake data. Not only the earthquake acceleration data but also the earthquake responses of bridges are used for the virtual onboard data synthesis. For the analysis of synthesized data, the short-time Fourier Transform (STFT), the wavelet transform (WT), and Wigner–Ville Distribution (WVD) methods have been compared. Results show that WVD provides the best detection performance while the computational costs are large.

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Section: Train Datamentioning

confidence: 54%

Section: Resultsmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

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Comparison of Signal-Analysis Techniques for Seismic Detection System for High-Speed Train Data: Effect of Bridge Structures

Yoo

Moon²

2020

Sensors

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“…Conventional nondestructive testing methods are widely used but they have some limitations [5][6][7]. For example, visual testing can easily miss detection [8][9][10]; radiographic testing has radiation risk and its equipment cost is high [11]; magnetic particle testing and penetration testing can locate the defect position but cannot specifically display its shape; and eddy current detection and other signal detection technologies can not directly reflect the defect shape [12].…”

Section: Introductionmentioning

confidence: 99%

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Fan

Peng

Zhou

et al. 2021

Sensors

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Most of the existing laser welding process monitoring technologies focus on the detection of post-engineering defects, but in the mass production of electronic equipment, such as laser welding metal plates, the real-time identification of defect detection has more important practical significance. The data set of laser welding process is often difficult to build and there is not enough experimental data, which hinder the applications of the data-driven laser welding defect detection method. In this paper, an intelligent welding defect diagnosis method based on auxiliary classifier generative adversarial networks (ACGAN) has been proposed. Firstly, a ten-class dataset consisting of 6467 samples, was constructed, which originate from the optical and thermal sensory parameters in the welding process. A new structured ACGAN network model is proposed to generate fake data similar to the true defect feature distributions. In addition, in order to make the difference between different defects categories more obvious after data expansion, a data filtering and data purification scheme was proposed based on ensemble learning and an SVM (support vector machine), which is used to filter the bad generated data. In the experiments, the classification accuracy can reach 96.83% and 85.13%, for the CNN (convolutional neural network) algorithm model and ACGAN model, respectively. However, the accuracy can further improve to 97.86% and 98.37% for the fusion models of ACGAN-CNN and ACGAN-SVM-CNN models, respectively. The results show that ACGAN can not only be used as an algorithm model for classification, but also be used to achieve superior real-time classification and recognition through data enhancement and multi-model fusion.

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“…In this paper, we focus on identifying the dynamic load in real time which we refer to as the online load identification. This challenge is mainly motivated by the automotive industry and is highly relevant for applications in autonomous vehicles [8][9][10]. For example in autonomous vehicles it is important to evaluate the external dynamic load on a vehicle to take decisions related to acceleration/deceleration or trajectory optimization [9].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Identification of Dynamic Loads Using Inverse Solution and Kalman Filter

et al. 2020

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Evaluating dynamic loads in real time is crucial for health monitoring, fault diagnosis and fatigue analysis in aerospace, automotive and earthquake engineering among other vibration related applications. Developing such algorithms can be vital for several safety and performance functionalities. Therefore, over the past few years the identification of dynamic loads has attracted a lot of attention; however, little literature on the online identification can be found. In this paper, we propose an online-identification method of structural dynamic loads so that the dynamic load is evaluated in real time and while the system response is still being measured. This is achieved by significantly improving the identification efficiency while retaining a high accuracy. The proposed method which is based on Kalman filter, is introduced in detail for a finite as well as an infinite number of degrees of freedom. Starting from an initial guess of the state vector we evaluate the error covariance, which then helps to identify the value of the excitation force using a weighted least square method and minimizing the covariance unbiased estimation. This is repeated at certain time intervals i.e., time steps where the state vector is updated in real time as acceleration measurements are updated. The feasibility of the method is validated using numerical simulations and an experimental verification where a detailed LabVIEW (National Instruments Ltd.) implementation is provided.

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Development of a Seismic Detection Technology for High-Speed Trains Using Signal Analysis Techniques

Cited by 5 publications

References 25 publications

Comparison of Signal-Analysis Techniques for Seismic Detection System for High-Speed Train Data: Effect of Bridge Structures

Comparison of Signal-Analysis Techniques for Seismic Detection System for High-Speed Train Data: Effect of Bridge Structures

Real-Time High-Performance Laser Welding Defect Detection by Combining ACGAN-Based Data Enhancement and Multi-Model Fusion

Real-Time Identification of Dynamic Loads Using Inverse Solution and Kalman Filter

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