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Vision of Intelligent Control and Tracking Rail System: Global Evident Data
Published in Tanuja Patgar, Devi CS Kavitha, On-Board Design Models and Algorithm for Communication Based Train Control and Tracking System, 2022
Tanuja Patgar, Devi CS Kavitha
The general architecture for CTCS is given in Figure 1.6. The explanation of each control system is summarized below: Track Circuits: It is always located on track line where the train passing information is communicated through wireless communication.On-board Equipment: It monitors the safety operation of trains. It is used to generate dynamic velocity curves, distance graph and speed control modes on the basis of train movement.Balise: It is used to transmit information about train location, temporary speed restriction to the on-board system.GSM-R: It is used as communication channel for bi-directional, continuous, more capacity of information communication between the on-board and wayside system.
Control of an integrated lateral and roll suspension for a high-speed railway vehicle
Published in Vehicle System Dynamics, 2023
Egidio Di Gialleonardo, Alan Facchinetti, Stefano Bruni
Table 7 reports the values of the performance indexes for five cases, i.e. ideal positioning, ±10 m positioning error, compatible with GPS systems maximum error [38] and ±50 m positioning error. This last value corresponds to the limit to fulfil the European Train Control System (ETCS) requirements [39], considering 1 km balise distance. It is observed that the increase of the rms lateral acceleration is limited in all cases, whereas the PCT index is increased significantly for a positioning error of ±50 m, which is due to increased roll rate of the car body in curve transitions caused by the imperfect synchronisation of the tilt command to the curve transitions. The maximum error on roll increases on account of the positioning error. Note that the roll deviation is evaluated with respect to the ideal tilt (i.e. considering ideal positioning) while the controller keeps the ability to correctly follow the reference that is affected by the positioning error. The errors on the lateral carbody-bogie displacement slightly increase since the error on the reference signal is directly affecting the feed-forward contribution.
Intelligent decision support for maintenance: an overview and future trends
Published in International Journal of Computer Integrated Manufacturing, 2019
C. J. Turner, C. Emmanouilidis, T. Tomiyama, A. Tiwari, R. Roy
The scenario depicted in Figure 1 relates to the possibility that sensors have registered faults with a Balise (track-based forming part of an automatic train protection (ATP) system) and trackside signals in a period of time after the section of track has been tamped (where the ballast bed of the track is adjusted). In addition, a bankside sensor has noted some occasional subsidence in the past. All these data streams are recorded at a central control centre. The use of data mining may establish a causal link between these events taking into account the outlier measurement from the bankside sensor leading to the root cause of the fault. The audit trail establishes the order of events via timestamps and the output from data mining/machine learning. Such audit trails once established can help in the decision-making and may also advise trackside workers, undertaking maintenance in future-scheduled activities, to make additional checks based on the history of the track section.