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

As the occurrence of earthquakes is increasing in South Korea, the earthquake early warning (EEW) system becomes indispensable for the protection of high-speed railways. Although the importance of EEW system has been increasing, the number of installed seismic accelerometers in South Korea is not sufficient to provide rapid information. This study uses a stochastic signal analysis technique to utilize the smartphone sensors for the rapid EEW system. From the train vibration data from the low fidelity on-board accelerometer, the virtual earthquake detection data in the train by smartphone sensor has been constructed. To analyze the stochastic characteristics of the constructed data, the short time Fourier transform (STFT) approach has been applied. The study’s overall objective is to offer stochastic approaches that provide effective analysis of the low fidelity sensor data, such as smartphone sensor data, for the rapid EEW system.

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Sensor data-based probabilistic monitoring of time-history deflections of railway bridges induced by high-speed trains

Structural Health Monitoring ◽

10.1177/14759217211063424 ◽

2022 ◽

pp. 147592172110634

Author(s):

Jaebeom Lee ◽

Seunghoo Jeong ◽

Junhwa Lee ◽

Sung-Han Sim ◽

Kyoung-Chan Lee ◽

...

Keyword(s):

High Speed ◽

Time History ◽

Training Data ◽

Sensor Data ◽

Data Synchronization ◽

Speed Estimation ◽

Railway Bridges ◽

Monitoring Model ◽

High Speed Trains ◽

Probabilistic Monitoring

Structural condition monitoring of railway bridges has been emphasized for guaranteeing the passenger comfort and safety. Various attempts have been made to monitor structural conditions, but many of them have focused on monitoring dynamic characteristics in frequency domain representation which requires additional data transformation. Occurrence of abnormal structural responses, however, can be intuitively detected by directly monitoring the time-history responses, and it may give information including the time to occur the abnormal responses and the magnitude of the dynamic amplification. Therefore, this study suggests a new Bayesian method for directly monitoring the time-history deflections induced by high-speed trains. To train the monitoring model, the data preprocessing of speed estimation and data synchronization are conducted first for the given training data of the raw time-history deflection; the Bayesian inference is then introduced for the derivation of the probability-based dynamic thresholds for each train type. After constructing the model, the detection of the abnormal deflection data is proceeded. The speed estimation and data synchronization are conducted again for the test data, and the anomaly score and ratio are estimated based on the probabilistic monitoring model. A warning is generated if the anomaly ratio is at an unacceptable level; otherwise, the deflection is considered as a normal condition. A high-speed railway bridge in operation is chosen for the verification of the proposed method, in which a probabilistic monitoring model is constructed from displacement time-histories during train passage. It is shown that the model can specify an anomaly of a train-track-bridge system.

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Automatic Prediction Model of Ground Vibration for High-Speed Trains

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.764-765.644 ◽

2015 ◽

Vol 764-765 ◽

pp. 644-648

Author(s):

Yit Jin Chen ◽

Chi Jim Chen

Keyword(s):

Prediction Model ◽

High Speed ◽

Influence Factors ◽

Ground Vibration ◽

Support Vector ◽

Vibration Level ◽

Vibration Data ◽

High Speed Trains ◽

Train Speed ◽

Ground Condition

This paper presents an automatic prediction model for ground vibration induced by Taiwan high-speed trains on embankment structures. The prediction model is developed using different field-measured ground vibration data. The main characteristics that affect the overall vibration level are established based on the database of measurement results. The influence factors include train speed, ground condition, measurement distance, and supported structure. Support vector machine (SVM) algorithm, a widely used prediction model, is adopted to predict the vibration level induced by high-speed trains on embankments. The measured and predicted vibration levels are compared to verify the reliability of the prediction model. Analysis results show that the developed SVM model can reasonably predict vibration level with an accuracy rate of 72% to 84% for four types of vibration level, including overall, low, middle, and high frequency ranges. The methodology in developing the automatic prediction system for ground vibration level is also presented in this paper.

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A Systematic Approach to Identify Sources of Abnormal Interior Noise for a High-Speed Train

Shock and Vibration ◽

10.1155/2018/5085847 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 2

Author(s):

Jie Zhang ◽

Xinbiao Xiao ◽

Xiaozhen Sheng ◽

Zhihui Li ◽

Xuesong Jin

Keyword(s):

High Speed ◽

Sound Pressure ◽

Systematic Approach ◽

Noise Analysis ◽

Interior Noise ◽

Sound Insulation ◽

High Speed Train ◽

Main Contributor ◽

Vibration Data ◽

High Speed Trains

A systematic approach to identify sources of abnormal interior noise occurring in a high-speed train is presented and applied in this paper to resolve a particular noise issue. This approach is developed based on a number of previous dealings with similar noise problems. The particular noise issue occurs in a Chinese high-speed train. It is measured that there is a difference of 7 dB(A) in overall Sound Pressure Level (SPL) between two nominally identical VIP cabins at 250 km/h. The systematic approach is applied to identify the root cause of the 7 dB(A) difference. Well planned measurements are performed in both the VIP cabins. Sound pressure contributions, either in terms of frequency band or in terms of facing area, are analyzed. Order analysis is also carried out. Based on these analyses, it is found that the problematic frequency is the sleeper passing frequency of the train, and an area on the roof contributes the most. In order to determine what causes that area to be the main contributor without disassembling the structure of the roof, measured noise and vibration data for different train speeds are further analyzed. It is then reasoned that roof is the main contributor caused by sound pressure behind the panel. Up to this point, panels of the roof are removed, revealing that a hole of 300 cm2 for running cables is presented behind the red area without proper sound insulation. This study can provide a basis for abnormal interior noise analysis and control of high-speed trains.

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High Speed Train Bogie Fault Signal Analysis Based on Wavelet Entropy Feature

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.753-755.2286 ◽

2013 ◽

Vol 753-755 ◽

pp. 2286-2289 ◽

Cited By ~ 11

Author(s):

Na Qin ◽

Wei Dong Jin ◽

Jin Huang ◽

Peng Jiang ◽

Zhi Min Li

Keyword(s):

Signal Analysis ◽

High Speed ◽

Performance Monitoring ◽

Vibration Signal ◽

High Speed Train ◽

Wavelet Entropy ◽

Result Show ◽

High Speed Trains ◽

Dimension Space ◽

Train System

Mechanical behavior of high speed trains bogie seriously impact the reliability of the train system. Performance monitoring and fault diagnosis for the critical component on bogie are very important. Simulation data of high speed train bogie fault signal is selected in data experiment. Based on multiresolution analysis, wavelet entropy features are extracted to reflect the uncertainty level of the vibration signal on scales. In the high dimension space composed by several wavelet entropy features, the dates from four fault patterns are classified and the result is satisfactory. Result show that wavelet entropy feature is effective for fault signal analysis of high speed train bogie.

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