Observer-based Robust Train Speed Estimation Subject to Wheel-Rail Adhesion Faults

2021 ◽  
Author(s):  
Hiba Fawzia Bouchama ◽  
Denis Berdjag ◽  
Michael Defoort ◽  
Jimmy Lauber
Keyword(s):  
Speed Estimation ◽  
Train Speed

Train speed estimation using low-cost GPS receivers

2019 ◽  
Author(s):  
Leonard Chia ◽  
Bhavana Bhardwaj ◽  
Raj Bridgelall ◽  
Pan Lu ◽  
Denver D. Tolliver
Keyword(s):  
Low Cost ◽  
Speed Estimation ◽  
Gps Receivers ◽  
Train Speed

scholarly journals Train Speed Estimation from Track Structure Vibration Measurements

2020 ◽  
Vol 10 (14) ◽  
pp. 4742
Author(s):  
Xinyue Wang ◽  
Xianfeng Shi ◽  
Jialiang Wang ◽  
Xun Yu ◽  
Baoguo Han
Keyword(s):  
Structural Health Monitoring ◽  
Health Monitoring ◽  
Geometrical Parameters ◽  
Track Structure ◽  
Speed Estimation ◽  
Vibration Measurements ◽  
Structural Health ◽  
Treatment Techniques ◽  
Train Speed ◽  
Structure Vibration

This paper proposes a new method for train speed estimation from track structure vibration measurements for field track structural health monitoring. This method employed image treatment techniques, wavelet transform, and short-time Fourier transform into the signal processing. Afterward, the train speed was estimated by the combination of the extracted features and the geometrical parameters of the passing trains. A total of 240 measurements, gotten from 20 trains measured by 12 sensors, were implemented to verify the effectiveness and practicability of the proposed method. The results showed that the average differences of the train speed calculating by phase differences and the proposed method were 0.61% for slab displacement measurements, 1.39% for rail acceleration measurements, and 2.97% for slab acceleration measurements, respectively. Furthermore, the proposed method was proved to be effective in different test locations and track structure state. Therefore, it is concluded that the proposed method can estimate train speed from the vibration measurements automatically, reliably, and in real time with no need for additional speed measurement modules, which meets the requirement of speed estimation in the short-term, multi-location, and tough environment of structural health monitoring.

High-speed train speed estimation via one geophone near an high-speed railway

2021 ◽  
Author(s):  
Xiaokai Wang ◽  
Baoli Wang ◽  
Chun Li ◽  
Wenchao Chen ◽  
Chen Zhao
Keyword(s):  
High Speed ◽  
Speed Estimation ◽  
High Speed Train ◽  
High Speed Railway ◽  
Train Speed

Position and Speed Estimation Methods of SPMSMs using a Hall Effect Sensor

2008 ◽  
Vol 128 (2) ◽  
pp. 125-130
Author(s):  
Kan Akatsu ◽  
Nobuhiro Mitomo ◽  
Shinji Wakui
Keyword(s):  
Hall Effect ◽  
Estimation Methods ◽  
Speed Estimation ◽  
Hall Effect Sensor

scholarly journals Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 798
Author(s):  
Hamed Darbandi ◽  
Filipe Serra Bragança ◽  
Berend Jan van der Zwaag ◽  
John Voskamp ◽  
Annik Imogen Gmel ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Body ◽  
Biomechanical Analysis ◽  
Estimation Accuracy ◽  
Speed Estimation ◽  
Motion Patterns ◽  
Positioning Systems ◽  
Machine Learning Approach ◽  
Global Positioning ◽  
Measurement Units

Speed is an essential parameter in biomechanical analysis and general locomotion research. It is possible to estimate the speed using global positioning systems (GPS) or inertial measurement units (IMUs). However, GPS requires a consistent signal connection to satellites, and errors accumulate during IMU signals integration. In an attempt to overcome these issues, we have investigated the possibility of estimating the horse speed by developing machine learning (ML) models using the signals from seven body-mounted IMUs. Since motion patterns extracted from IMU signals are different between breeds and gaits, we trained the models based on data from 40 Icelandic and Franches-Montagnes horses during walk, trot, tölt, pace, and canter. In addition, we studied the estimation accuracy between IMU locations on the body (sacrum, withers, head, and limbs). The models were evaluated per gait and were compared between ML algorithms and IMU location. The model yielded the highest estimation accuracy of speed (RMSE = 0.25 m/s) within equine and most of human speed estimation literature. In conclusion, highly accurate horse speed estimation models, independent of IMU(s) location on-body and gait, were developed using ML.

scholarly journals A Rail-Temperature-Prediction Model Based on Machine Learning: Warning of Train-Speed Restrictions Using Weather Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4606
Author(s):  
Sunguk Hong ◽  
Cheoljeong Park ◽  
Seongjin Cho
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Weather Forecasting ◽  
Thermal Environment ◽  
Management Plan ◽  
Temperature Prediction ◽  
Railway Systems ◽  
Train Speed ◽  
Speed Restrictions ◽  
Entire Network

Predicting the rail temperature of a railway system is important for establishing a rail management plan against railway derailment caused by orbital buckling. The rail temperature, which is directly responsible for track buckling, is closely related to air temperature, which continuously increases due to global warming effects. Moreover, railway systems are increasingly installed with continuous welded rails (CWRs) to reduce train vibration and noise. Unfortunately, CWRs are prone to buckling. This study develops a reliable and highly accurate novel model that can predict rail temperature using a machine learning method. To predict rail temperature over the entire network with high-prediction performance, the weather effect and solar effect features are used. These features originate from the analysis of the thermal environment around the rail. Precisely, the presented model has a higher performance for predicting high rail temperature than other models. As a convenient structural health-monitoring application, the train-speed-limit alarm-map (TSLAM) was also proposed, which visually maps the predicted rail-temperature deviations over the entire network for railway safety officers. Combined with TSLAM, our rail-temperature prediction model is expected to improve track safety and train timeliness.

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