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Relevant bibliographies by topics / Railway point machine

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Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'Railway point machine'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Railway point machine"

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Choi, Yongju, Jonguk Lee, Daihee Park, Jonghyun Lee, Yongwha Chung, Hee-Young Kim, and Sukhan Yoon. "Stress Detection of Railway Point Machine Using Sound Analysis." KIPS Transactions on Software and Data Engineering 5, no. 9 (September 30, 2016): 433–40. http://dx.doi.org/10.3745/ktsde.2016.5.9.433.

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SUN, Yongkui, Yuan CAO, Guo XIE, and Tao WEN. "Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine." Chinese Journal of Electronics 29, no. 4 (July 1, 2020): 786–92. http://dx.doi.org/10.1049/cje.2020.06.007.

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Mistry, Pritesh, Phil Lane, and Paul Allen. "Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data." Sensors 20, no. 9 (May 9, 2020): 2692. http://dx.doi.org/10.3390/s20092692.

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In this study, we propose a methodology for the identification of potential fault occurrences of railway point-operating machines, using unlabeled signal sensor data. Data supplied by Network Rail, UK, is processed using a fast Fourier transform signal processing approach, coupled with the mean and max current levels to identify potential faults in point-operating machines. The method developed can dynamically adapt to the behavioral characteristics of individual point-operating machines, thereby providing bespoke condition monitoring capabilities in situ and in real time. The work described in this paper is not unique to railway point-operating machines, rather the data pre-processing and methodology is readily applicable to any motorized device fitted with current sensing capabilities. The novelty of our approach is that it does not require pre-labelled data with historical fault occurrences and therefore closely resembles problems of the real world, with application for smart city infrastructure. Lastly, we demonstrate the problems faced with handling such data and the capability of our methodology to dynamically adapt to diverse data presentations.

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Panja, Subhash Chandra, and Pradip Kumar Ray. "Reliability analysis of a ‘point-and-point machine’ of the Indian railway signaling system." Quality and Reliability Engineering International 23, no. 7 (2007): 833–48. http://dx.doi.org/10.1002/qre.851.

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Abbasnejad, Sahand, and Ahmad Mirabadi. "Predicting the failure of railway point machines by using Autoregressive Integrated Moving Average and Autoregressive-Kalman methods." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 232, no. 6 (December 28, 2017): 1790–99. http://dx.doi.org/10.1177/0954409717748790.

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In this paper, forercasting methods that use autoregressive integrated moving average (ARIMA) and autoregressive-Kalman (AR-Kalman) are presented for the prediction of the failure state of S700K railway point machines. Using signal processing methods such as wavelet transform and statistical analysis and the stator current signal, the authors have acquired the time series data of the point machine behavior using a near-failure test point machine. Prediction methods are implemented by utilizing the acquired time series data, and the results are compared with the specified failure margin. Furthermore, the prposed ARIMA method used in this study is compared with the AR-Kalman prediction method, and prediction errors are analysed.

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Li, Zhen, Zhuo Yin, Tao Tang, and Chunhai Gao. "Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder." Applied Sciences 9, no. 23 (November 27, 2019): 5139. http://dx.doi.org/10.3390/app9235139.

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Data-driven fault diagnosis is considered a modern technique in Industry 4.0. In the area of urban rail transit, researchers focus on the fault diagnosis of railway point machines as failures of the point machine may cause serious accidents, such as the derailment of a train, leading to significant personnel and property loss. This paper presents a novel data-driven fault diagnosis scheme for railway point machines using current signals. Different from any handcrafted feature extraction approach, the proposed scheme employs a locally connected autoencoder to automatically capture high-order features. To enhance the temporal characteristic, the current signals are segmented and blended into some subsequences. These subsequences are then fed to the proposed autoencoder. With the help of a weighting strategy, the seized features are weight averaged into a final representation. At last, different from the existing classification methods, we employ the local outlier factor algorithm to solve the fault diagnosis problem without any training steps, as the accurate data labels that indicate a healthy or unhealthy state are difficult to acquire. To verify the effectiveness of the proposed fault diagnosis scheme, a fault dataset termed “Cu-3300” is created by collecting 3300 in-field current signals. Using Cu-3300, we perform comprehensive analysis to demonstrate that the proposed scheme outperforms the existing methods. We have made the dataset Cu-3300 and the code file freely accessible as open source files. To the best of our knowledge, the dataset Cu-3300 is the first open source dataset in the area of railway point machines and our conducted research is the first to investigate the use of autoencoders for fault diagnosis of point machines.

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Ou, Dongxiu, Rui Xue, and Ke Cui. "A Data-Driven Fault Diagnosis Method for Railway Turnouts." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (March 27, 2019): 448–57. http://dx.doi.org/10.1177/0361198119837222.

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Turnout systems on railways are crucial for safety protection and improvements in efficiency. The statistics show that the most common faults in railway system are turnout system faults. Therefore, many railway systems have adopted the microcomputer monitoring system (MMS) to monitor their health and performance in real time. However, in practice, existing turnout fault diagnosis methods depend largely on human experience. In this paper, we propose a data-driven fault diagnosis method that monitors data from point machines collected using MMS. First, based on a derivative method, data features are extracted by segmenting the original sample. Then, we apply two methods for feature reduction: principal component analysis (PCA) and linear discriminant analysis (LDA). The results show that LDA gave a better performance in the cases studied. A problem that cannot be overlooked is that the imbalanced quantity of rare fault samples and abundant normal samples will reduce the accuracy of classic fault diagnosis models. To deal with this problem of imbalanced data, we propose a modified support vector machine (SVM) method. Finally, an experiment using real data collected from the Guangzhou Railway Line is presented, which demonstrates that our method is reliable and feasible in fault diagnosis. It can further assist engineers to perform timely repairs and maintenance work in the future.

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Atamuradov, Vepa, Kamal Medjaher, Fatih Camci, Pierre Dersin, and Noureddine Zerhouni. "Railway Point Machine Prognostics Based on Feature Fusion and Health State Assessment." IEEE Transactions on Instrumentation and Measurement 68, no. 8 (August 2019): 2691–704. http://dx.doi.org/10.1109/tim.2018.2869193.

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Oyebande, B. O., and A. C. Renfrew. "The design and implementation of a simulated trackside environment test system for an electric point machine." Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit 221, no. 4 (July 1, 2007): 477–85. http://dx.doi.org/10.1243/09544097f02605.

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The background and constraints involved in condition monitoring of railway point operation are reviewed and discussed. An approximate method to replicate maladjustment, based on industry experience, is proposed and a computer simulation of the point machine and load is introduced. In view of uncertainty in results, a mechanical solution was preferred and the design and implementation of a simulated trackside environment test system and the instrumentation and measurement techniques used to gather practical data are described. In view of the severe railway environment, only electrical quantities were monitored using non-invasive sensors. A comparison is made with site data. Conclusions include the estimated payback available from implementing the condition monitoring system.

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Xu, Jing Mang, Ping Wang, and Hao Xu. "Improvement and Lock Calculation of Electric Switch Machine of Railway Turnout." Applied Mechanics and Materials 409-410 (September 2013): 1496–501. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1496.

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Electric switch machine locks the crossing rail in the working direction and checks the working status of the crossing. With the increase of train speed, the ZD(J)9 electric switch machine cant satisfy the equipment of high speed railway, This paper studied the optimization; in order to study the lock calculation of nose rail after conversion, a dynamic model is established to research the influence of working status of the crossing. It indicates that for the first traction point, the stress and deformation are mainly affected by scant displacement between nose rail and wing rail; for the second traction point, they are affected by the gap between nose rail and spacer; fastener lateral stiffness doesnt influence the stress status, but the lateral fastener stiffness should not be too small.

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Dissertations / Theses on the topic "Railway point machine"

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Shi, Zhe. "A Comparative Study of Performance Assessment and Fault Diagnosis Approaches for Reciprocating Electromechanical Mechanism." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1468512813.

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Asada, Tomotsugu. "Novel condition monitoring techniques applied to improve the dependability of railway point machines." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/4155/.

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Point machines are the key actuator used in railways to provide a means of moving a switch blade from one position to the other. Failure in the point actuator has a significant effect on train operations. Condition monitoring systems for point machines have been therefore implemented in some railways, but these condition monitoring systems have limitations for detecting incipient faults. Furthermore, the majority of condition monitoring systems which are currently in use cannot diagnose faults. The ability to diagnose faults is useful to maintenance staff who need to fix problems immediately. This thesis proposes a methodology to detect and diagnose incipient faults using an advanced algorithm. In the main body of this thesis the author considers a new approach using Wavelet Transforms and Support vector machines for fault detection and diagnosis for railway electrical AC point machines operated in Japan. The approach is further enhanced with more data sets collected from railway electrical DC point machines operated in Great Britain. Furthermore, a method to express the qualitative features of healthy and faulty waveforms was proposed to test the transferability of the specific algorithm parameters from one instance of a point machine to another, which is tested on railway electrical DC point machines used in Great Britain. Finally, an approach based on Wavelet Transforms and Neural networks is used to predict the drive force when the point machine is operating. The approach was tested using electrical DC point machines operated in Great Britain. It is shown through the use of laboratory experimentation that the proposed methods have potential to be used in a real railway system.

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Book chapters on the topic "Railway point machine"

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Fidali, Marek, Paweł Wojciechowski, and Aleksander Pełka. "Fault Detection of Railway Point Machine Using Diagnostic Models." In Applied Condition Monitoring, 275–85. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62042-8_25.

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Guzman, Daniela Narezo, Edin Hadzic, Benjamin Baasch, Judith Heusel, Thorsten Neumann, Gerrit Schrijver, Douwe Buursma, and Jörn C. Groos. "Anomaly Detection and Forecasting Methods Applied to Point Machine Monitoring Data for Prevention of Railway Switch Failures." In Advances in Asset Management and Condition Monitoring, 307–18. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57745-2_26.

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Saunders, Nicholas J. "Forts, Stations, and Ancestors." In Desert Insurgency, 151–83. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198722007.003.0008.

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This chapter focuses on Wadi Rutm Station and Tel Shahm Station. Tel Shahm Station and its landscape is characterized by several types of militarization—tent-ring campsites, a fortified hilltop and blockhouse, shadowy remains of defensive trenches around the station, and a karakoll strong-point further south. It is possible that the anomalous northernmost tent-ring campsite is a mix of prehistoric, railway construction-era, and Arab Revolt-period occupation, and that the southern construction camp saw later re-occupation between 1917 and 1918. The hilltop blockhouse, its perimeter wall, and the observation posts are clear examples of Turkish militarization, reinforced by the munitions found at those posts facing the railway, indicative of outgoing Turkish rifle or machine-gun fire. Together with the shattered railway tracks down on the desert, it is likely that much of the archaeology of this multi-component site belongs to the Arab Revolt.

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Armas Vergel, Octavio, Dolores Cárdenas, Reyes García-Contreras, and Carmen Mata. "Bioethanol-Diesel Blends Used in Diesel Engines and Vehicles under Transient Operation." In Bioethanol [Working Title]. IntechOpen, 2020. http://dx.doi.org/10.5772/intechopen.94359.

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This book chapter describes, the experiences of different experimental works related to the study of the effect of bioethanol-diesel fuel blends directly used on a study about the integrity of some parts of a diesel injection system, and on performance and regulated emissions of engines and/or vehicles under different transient conditions. The experiences described are carried out in an engine test bench, two public busses under urban transportation and a construction machine in an actual railway construction. A bioethanol-diesel fuel blend with 10% v/v of bioethanol was used for engine testing to study the potential effect on performance and emissions. Later, a blend with around 8% v/v of alcohol concentration was selected to carry out the experimental work with an injection system and with busses and construction machinery. This work points out the most important advantages and disadvantages of the use of bioethanol blended with diesel fuel. The most important strength is the potential of these fuel blends for reducing particle matter without penalty in nitrogen oxides emissions. As main weakness can be cited the need for adding a stabilizer additive which restricts the desirable increase of bioethanol content in the blend.

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Conference papers on the topic "Railway point machine"

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Zhou, F. B., M. D. Duta, M. P. Henry, S. Baker, and C. Burton. "Remote Condition Monitoring for Railway Point Machine." In ASME/IEEE 2002 Joint Rail Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/rtd2002-1646.

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This paper presents the research work carried out at Oxford University on condition monitoring of railway point machines. The developed condition monitoring system includes a variety of sensors for acquiring trackside data related to different parameters. Key events to be logged include time stamping of points operation, opening and closing of case cover associated with a points machine, insertion and removal of a hand-crank, loss of supply current and the passage of a train. The system also has built-in Web functions. This allows a remote operator using Internet Explorer to observe the condition of the point machine at any time, while the acquired data can be downloaded automatically for offline analysis, providing more detailed information on the health condition of the monitored point machine. A short daily condition report message can also be sent to relevant staff via email. At last the experience are reported on the four trackside installed systems.

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Zhang, Shiyao, Hairong Dong, Ulrich Maschek, and Haifeng Song. "A digital-twin-assisted fault diagnosis of railway point machine." In 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI). IEEE, 2021. http://dx.doi.org/10.1109/dtpi52967.2021.9540118.

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Wang, Zhipeng, Limin Jia, and Yong Qin. "An integrated feature extraction algorithm for condition monitoring of railway point machine." In 2016 Prognostics and System Health Management Conference (PHM-Chengdu). IEEE, 2016. http://dx.doi.org/10.1109/phm.2016.7819893.

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Guo, Zijian, Hao Ye, Wei Dong, Xiang Yan, and Yindong Ji. "A Fault Detection Method for Railway Point Machine Operations Based On Stacked Autoencoders." In 2018 24th International Conference on Automation and Computing (ICAC). IEEE, 2018. http://dx.doi.org/10.23919/iconac.2018.8749098.

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Vithanage, Randika K. W., Colin S. Harrison, and Anjali K. M. M. DeSilva. "Enhance 3D Point Cloud Accuracy Through Supervised Machine Learning for Automated Rolling Stock Maintenance: A Railway Sector Case Study." In 2018 International Conference on Computing, Electronics & Communications Engineering (iCCECE). IEEE, 2018. http://dx.doi.org/10.1109/iccecome.2018.8658788.

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Rose, Jerry G., Macy L. Purcell, and Qingjie Liu. "Suitability of Earth and Granular Materials Pressure Cells for Measuring Railway Trackbed Tie/Ballast Interfacial Pressures." In 2016 Joint Rail Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/jrc2016-5735.

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The magnitudes and relative distributions of pressures at the tie/ballast interface are important trackbed engineering design and analysis aspects. The pressures produced by millions of load applications ultimately affect the long-term performance of the track and the service lives of the component materials and layers. Ideally the interstitial pressure intensities can be reduced by distributing pressures uniformly over large areas thereby reducing abrasion of the bottom of the tie and crushing of the surface layer of the ballast. Multiple earth pressure cells and granular materials pressure cells were used in a series of laboratory tests to ascertain the applicability of using these types of sensors for accurate measurement of the vertical pressures at the tie/ballast interface. Test loads were applied through a section of a tie that was positioned on the ballast. In addition, the effects of several variables were evaluated; — primary variables included the type of granular support — ranging from new and worn ballast size to fine sand size aggregate and the type of cushioning — including resilient pads and thin rubber membranes. These tests were conducted to validate or negate the relative effects of variables on test results. Pressures calculated from the controlled testing, using a 50,000-lbf (222 kN) MTS hydraulic servo system test machine, were compared to simultaneously measured pressures indicated by the outputs from the pressure cells for machine test loads typical of in-track train loadings. Near perfect correlations were obtained. Furthermore, test repeatability was consistent with little variability of replicated tests. The results indicated that the cells were capable of accurately recording known pressure inputs and therefore applicable for in-track measurement of tie/ballast interfacial pressures. In-track tests were conducted under typical locomotive and freight car loadings with cells positioned at the tie/ballast interfaces directly under the rail/tie intersection — the point of maximum pressure intensities on the ballast layer. These values were very consistent over periods of elapsed time as the track experienced typical loadings from normal train operations. These test results are described in detail as related to measured pressure intensities and distributions from typical locomotive and freight car wheel loadings of 33,000 and 36,000 lbs. (15,000 and 16,300 kg).

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Doboszewski, Iwo, Simon Fossier, and Christophe Marsala. "Data Driven Detection of Railway Point Machines Failures." In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, 2019. http://dx.doi.org/10.1109/ssci44817.2019.9002844.

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Zhou, F. "Condition monitoring and validation of railway point machines." In IEE Seminar Intelligent and Self-Validating Instruments (Sensors and Actuators). IEE, 2001. http://dx.doi.org/10.1049/ic:20010221.

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Atamuradov, V., F. Camci, S. Baskan, and M. Sevkli. "Failure diagnostics for railway point machines using expert systems." In 2009 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives - SDEMPED. IEEE, 2009. http://dx.doi.org/10.1109/demped.2009.5292755.

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Jin, Wenjing, Zhe Shi, David Siegel, Pierre Dersin, Cyril Douziech, Michele Pugnaloni, Piero La Cascia, and Jay Lee. "Development and evaluation of health monitoring techniques for railway point machines." In 2015 IEEE Conference on Prognostics and Health Management (PHM). IEEE, 2015. http://dx.doi.org/10.1109/icphm.2015.7245016.

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