Over the last twenty to thirty years, railway vehicle dynamics has changed from being an essentially mechanical engineering discipline to one that is increasingly starting to include sensors, electronics and computer processing. This paper surveys the application of these technologies to suspensions and running gear, focused upon the complementary issues of control (which has been reviewed within Vehicle System Dynamics previously) and monitoring (which has not previously been reviewed). The theory, concepts and implementation status are assessed in each case, from which the paper identifies the key trends and concludes with a forward look at what is likely to develop over the next years
The aim of this study is to propose a method for analyzing measured signal obtained
from functional Near-Infrared Spectroscopy (fNIRS), which is applicable for
neuroimaging studies for car drivers. We developed a signal processing method by
multiresolution analysis (MRA) based on discrete wavelet transform. Statistical group
analysis using Z-score is conducted after the extraction of task-related signal using
MRA. Brain activities of subjects with different level of mental calculation are
measured by fNIRS and fMRI. Results of mental calculation with nine subjects by using
fNIRS and fMRI showed that the proposed methods were effective for the evaluation of
brain activities due to the task. Finally, the proposed method is applied for evaluating
brain function of car driver with and without adaptive cruise control (ACC) system for
demonstrating the effectiveness of the proposed method. The results showed that frontal
lobe was less active when the subject drove with ACC.
A track condition monitoring system that uses a compact on-board sensing device has been developed and applied for track condition monitoring of regional railway lines in Japan. Monitoring examples show that the system is effective for regional railway operators. A classifier for track faults has been developed to detect track fault automatically. Simulation studies using SIMPACK and field tests were carried out to detect and isolate the track faults from car-body vibration. The results show that the feature of track faults is extracted from car-body vibration and classified from proposed feature space using machine learning techniques.
Track maintenance works based on track geometry recordings are essential to enhance the safety and comfort of railway transportation. The track condition monitoring system is mainly used for the choice of area needing track tamping works for the purpose of the good riding comfort. An advantage of car-body acceleration measurement devices is their simple structures, which make it easier to carry out maintenance. However, the car-body acceleration waveform is considerably different from track geometry. This paper demonstrates the possibility to estimate the track geometry of Shinkansen tracks using car-body motions only. In an inverse problem to estimate track irregularity from car-body motions, a Kalman Filter (KF) was applied to solve the problem. Estimation results showed that track irregularity estimation in vertical direction is possible with acceptable accuracy for real use.
This paper summarizes the track-condition-monitoring system for conventional and high speed railway used in Japanese railway. For conventional railway, rail irregularities are estimated from the vertical and lateral acceleration of the car body. The roll angle of the car body, calculated using a rate gyroscope, is used to distinguish line irregularities from level irregularities. Rail corrugation is detected from cabin noise with spectral peak calculation. A GPS system and a mapmatching algorithm are used to pinpoint the location of faults on tracks. Field test using in-service vehicle was carried out to evaluate the developed system. The results show that the condition of rail irregularity and rail corrugation can be estimated effectively. For high speed railway, shinkansen, the track condition monitoring system called RAIDARSS 3 is introduced.
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