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Journal articles on the topic 'Railway point machine'
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1
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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Kumar, Nikesh, and Kong Fah Tee. "Reliability and Inspection Modelling of Railway Signalling Systems." Modelling 2, no. 3 (July 26, 2021): 344–54. http://dx.doi.org/10.3390/modelling2030018.
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The railway is one of the most prominent models of transportation across the globe and it carries a large number of people, thus requiring high reliability, maintainability and safety. The reliability of railways mostly depends on an effective signalling system, making it one of the critical parts of railway operation. A signalling system is part of a large array of systems with interconnected components and subcomponents. Therefore, there is a need to make the signalling system more reliable and optimised with enhanced fault detection. Proper inspection and maintenance are required to make the signalling system reliable and safe. In this study, different inspection modelling techniques are applied to find the reliability of the signalling system. The signalling system has been divided into subsystems (signal unit, track unit, point-and-point machine) considering their importance and their effects on the failure rate of the entire signalling system. Inspection modelling of each subsystem has been conducted to provide the basis for the entire signalling system. A case study has been investigated to validate the model developed in one of the busiest tracks in eastern India. The obtained data thus are used to analyse the inspection pattern of signalling subsystems. Special attention to maintenance for inspection activities and logistics support has been taken into consideration, which is required to improve the reliability and maintainability of signalling subsystems and systems to make the railway signalling system sustainable in the long run.
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Asada, T., C. Roberts, and T. Koseki. "An algorithm for improved performance of railway condition monitoring equipment: Alternating-current point machine case study." Transportation Research Part C: Emerging Technologies 30 (May 2013): 81–92. http://dx.doi.org/10.1016/j.trc.2013.01.008.
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Fan, Lin Pey, and Tzu How Chu. "Optimal Planning Method for Large-Scale Historical Exhibits in the Taiwan Railway Museum." Applied Sciences 11, no. 5 (March 9, 2021): 2424. http://dx.doi.org/10.3390/app11052424.
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The curation design of cultural heritage sites, such as museums, influence the level of visitor satisfaction and the possibility of revisitation; therefore, an efficient exhibit layout is critical. The difficulty of determining the behavior of visitors and the layout of galleries means that exhibition layout is a knowledge-intensive, time-consuming process. The progressive development of machine learning provides a low-cost and highly flexible workflow in the management of museums, compared to traditional curation design. For example, the facility’s optimal layout, floor, and furniture arrangement can be obtained through the repeated adjustment of artificial intelligence algorithms within a relatively short time. In particular, an optimal planning method is indispensable for the immense and heavy trains in the railway museum. In this study, we created an innovative strategy to integrate the domain knowledge of exhibit displaying, spatial planning, and machine learning to establish a customized recommendation scheme. Guided by an interactive experience model and the morphology of point–line–plane–stereo, we obtained three aspects (visitors, objects, and space), 12 dimensions (orientation, visiting time, visual distance, centrality, main path, district, capacity, etc.), 30 physical principles, 24 suggestions, and five main procedures to implement layout patterns and templates to create an exhibit layout guide for the National Railway Museum of Taiwan, which is currently being transferred from the railway workshop for the sake of preserving the rail culture heritage. Our results are suitable and extendible to different museums by adjusting the criteria used to establish a new recommendation scheme.
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Es-Saadi, Mouna, Mohamed Khafallah, and Hamid Chaikhy. "Using the Five-Level NPC Inverter to Improve the FOC Control of the Asynchronous Machine." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 4 (December 1, 2018): 1457. http://dx.doi.org/10.11591/ijpeds.v9.i4.pp1457-1466.
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<span lang="EN-US">Many researches have been dedicated to develop the induction motor drive control strategy used on the railway traction applications. In this paper we propose to investigate and to improve the electric locomotives by using a Field Oriented Control (FOC) strategy of induction motor drive. This induction motor can be powered by a five-stage neutral point inverter controlled by sinusoidal pulse width modulation (SPWM) due to good quality for output voltage and The use of fast switches. Both conventional and improved locomotives are simulated in Matlab/Simulink and compared in open loop conditions and closed loop conditions using IP controller, in term of torque response, current harmonic distortions and rotor speed response.</span>
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Najeh, Taoufik, Jan Lundberg, and Abdelfateh Kerrouche. "Deep-Learning and Vibration-Based System for Wear Size Estimation of Railway Switches and Crossings." Sensors 21, no. 15 (July 31, 2021): 5217. http://dx.doi.org/10.3390/s21155217.
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The switch and crossing (S&C) is one of the most important parts of the railway infrastructure network due to its significant influence on traffic delays and maintenance costs. Two central questions were investigated in this paper: (I) the first question is related to the feasibility of exploring the vibration data for wear size estimation of railway S&C and (II) the second one is how to take advantage of the Artificial Intelligence (AI)-based framework to design an effective early-warning system at early stage of S&C wear development. The aim of the study was to predict the amount of wear in the entire S&C, using medium-range accelerometer sensors. Vibration data were collected, processed, and used for developing accurate data-driven models. Within this study, AI-based methods and signal-processing techniques were applied and tested in a full-scale S&C test rig at Lulea University of Technology to investigate the effectiveness of the proposed method. A real-scale railway wagon bogie was used to study different relevant types of wear on the switchblades, support rail, middle rail, and crossing part. All the sensors were housed inside the point machine as an optimal location for protection of the data acquisition system from harsh weather conditions such as ice and snow and from the ballast. The vibration data resulting from the measurements were used to feed two different deep-learning architectures, to make it possible to achieve an acceptable correlation between the measured vibration data and the actual amount of wear. The first model is based on the ResNet architecture where the input data are converted to spectrograms. The second model was based on a long short-term memory (LSTM) architecture. The proposed model was tested in terms of its accuracy in wear severity classification. The results show that this machine learning method accurately estimates the amount of wear in different locations in the S&C.
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Farmakis, Ioannis, David Bonneau, D. Jean Hutchinson, and Nicholas Vlachopoulos. "Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification." Remote Sensing 13, no. 7 (April 1, 2021): 1354. http://dx.doi.org/10.3390/rs13071354.
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Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics.
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Doni, Matteo. "Gli effetti urbanistici di un grande progetto di trasformazione della cittŕ di Milano." TERRITORIO, no. 47 (February 2009): 139–49. http://dx.doi.org/10.3280/tr2008-047016.
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- Consideration of the affair of the former O.M. (Machine Workshop) area starts with an attempt to ‘read the past to understand the present' and therefore the main documents, plans and programmes which affected this part of the city were examined and ‘taken apart' as it were. This process involved analysing some of the main Milanese deregulation instruments (The master document for the Urban Link Line Railway, 1983), the starting point for the redefinition of plans for this area. In subsequent years the entire area was affected by the ‘Nine parks for Milan project' (1995) which laid the foundations for the complete conversion of it, which was to occur with the ‘Urban redevelopment programme' (1999) named the ‘former O.M.'. As we will see, this programme was to take no account of some abandoned or about to be abandoned areas contiguous to the zone which were redeveloped independently using the ‘Dia' instrument. Reflection performed at the same time on the planning assumptions and on actual results highlighted issues for consideration within the framework of large urban transformations. Being There and Away.
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Li, Muxiao, Ziwei Zhu, Tiesong Deng, and Xiaozhen Sheng. "An investigation into high-speed train interior noise with operational transfer path analysis method." Railway Engineering Science 29, no. 1 (February 26, 2021): 1–14. http://dx.doi.org/10.1007/s40534-021-00235-0.
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AbstractPassengers' demands for riding comfort have been getting higher and higher as the high-speed railway develops. Scientific methods to analyze the interior noise of the high-speed train are needed and the operational transfer path analysis (OTPA) method provides a theoretical basis and guidance for the noise control of the train and overcomes the shortcomings of the traditional method, which has high test efficiency and can be carried out during the working state of the targeted machine. The OTPA model is established from the aspects of "path reference point-target point" and "sound source reference point-target point". As for the mechanism of the noise transmission path, an assumption is made that the direct sound propagation is ignored, and the symmetric sound source and the symmetric path are merged. Using the operational test data and the OTPA method, combined with the results of spherical array sound source identification, the path contribution and sound source contribution of the interior noise are analyzed, respectively, from aspects of the total value and spectrum. The results show that the OTPA conforms to the calculation results of the spherical array sound source identification. At low speed, the contribution of the floor path and the contribution of the bogie sources are dominant. When the speed is greater than 300 km/h, the contribution of the roof path is dominant. Moreover, for the carriage with a pantograph, the lifted pantograph is an obvious source. The noise from the exterior sources of the train transfer into the interior mainly through the form of structural excitation, and the contribution of air excitation is non-significant. Certain analyses of train parts provide guides for the interior noise control.
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Wang, Yuxue, Su Li, Xun Zhang, Dong Jiang, Mengmeng Hao, and Rui Zhou. "Site Selection of Digital Signage in Beijing: A Combination of Machine Learning and an Empirical Approach." ISPRS International Journal of Geo-Information 9, no. 4 (April 4, 2020): 217. http://dx.doi.org/10.3390/ijgi9040217.
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With the extensive use of digital signage, precise site selection is an urgent issue for digital signage enterprises and management agencies. This research aims to provide an accurate digital signage site-selection model that integrates the spatial characteristics of geographical location and multisource factor data and combines empirical location models with machine learning methods to recommend locations for digital signage. The outdoor commercial digital signage within the Sixth Ring Road area in Beijing was selected as an example and was combined with population census, average house prices, social network check-in data, the centrality of traffic networks, and point of interest (POI) facilities data as research data. The data were divided into 100–1000 m grids for digital signage site-selection modelling. The empirical approach of the improved Huff model was used to calculate the spatial accessibility of digital signage, and machine learning approaches such as back propagation neural network (BP neural networks) were used to calculate the potential location of digital signage. The site of digital signage to be deployed was obtained by overlay analysis. The result shows that the proposed method has a higher true positive rate and a lower false positive rate than the other three site selection models, which indicates that this method has higher accuracy for site selection. The site results show that areas suitable for digital signage are mainly distributed in Sanlitun, Wangfujing, Financial Street, Beijing West Railway Station, and along the main road network within the Sixth Ring Road. The research provides a reference for integrating geographical features and content data into the site-selection algorithm. It can effectively improve the accuracy and scientific nature of digital signage layouts and the efficiency of digital signage to a certain extent.
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Casagrande, G., F. Cucchi, and L. Zini. "Hazard connected to railway tunnel construction in karstic area: applied geomorphological and hydrogeological surveys." Natural Hazards and Earth System Sciences 5, no. 2 (February 23, 2005): 243–50. http://dx.doi.org/10.5194/nhess-5-243-2005.
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Abstract. In a mature karstic system, the realisation of galleries using the methodology of railway tunnel boring machine (TBM) involves particular problems due to the high risk of interference with groundwater (often subject to remarkable level variations) and with cavities and/or thick fill deposits. In order to define groundwater features it is necessary to investigate both hydrodynamic and karstification. To define and quantify the karst phenomenon in the epikarst of the Trieste Karst (Italy), an applied geomorphological approach has been experimented with surface and cavity surveys. The surface surveys have contributed to determining the potential karst versus the different outcropping lithologies and to define the structural setting of the rocky mass also through the realisation of geostructural stations and the survey of the main lines thanks to photo-interpretation. Moreover, all the dolines and the cavities present in the area interested by the gallery have been studied by analysing the probable extension of caves and/or of the secondary fill deposits and by evaluating the different genetic models. In an area 900m large and 27km long, which has been studied because of the underground karst, there are 41 dolines having diameters superior to 100m and 93 dolines whose diameters range between 100 and 50m; the dolines whose diameters are inferior to 50m are 282. The entrances of known and registered cavities in the cadastre records are 520. The hypogeal surveys have shown 5 typologies in which it has been possible to group all the cavities present in a hypothetical intersection with the excavation. The comparison between surface and hypogeal structural data and the direction of development of cavities has allowed for the definition of highly karstified discontinuity families, thus having a higher risk. The comparison of the collected data has enabled to identify the lithologies and areas having major risk and thus to quantify the probability of intersection with the different cavity typologies for each area. To make an example, out of 27000m of studied gallery 3930 are the metres expected to be at very high "karst risk". Out of these, as a whole 310 are risky because of the probable presence of gallery cavities, 2170 because of the probable presence of pits and sinkholes diffusely present under the dolines, and along 1450m karst is particularly intense. Moreover, 2200 should be the metres in which the rocky mass will be particularly divided because of tectonic causes. From a hydrogeological point of view a monitoring of water level has started to quantify water excursion, due to closeness of the railway tunnel to the mean water level. First results related to galleries intersection are here presented.
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Oyebande, B. O., and A. C. Renfrew. "Condition monitoring of railway electric point machines." IEE Proceedings - Electric Power Applications 149, no. 6 (2002): 465. http://dx.doi.org/10.1049/ip-epa:20020499.
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Tiverovsky, V. I. "European railways on the way to the digital future." Transport Technician: Education and Practice 2, no. 2 (June 21, 2021): 213–20. http://dx.doi.org/10.46684/2687-1033.2021.2.213-220.
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The issues of development and practical implementation of control systems and devices and traffic safety assurance, which lead to the digital future of railways, are considered. The main directions of digitalization of European railways are presented. The role and importance of the European Train Management System (ETCS) and the European Train Management and Safety Management System (ERTMS) are shown. Both systems are being implemented on the railways of European countries in stages (levels). Various systems of centralization of points and signals are gradually being replaced by digital centralization (DSTW). At the same time, much attention is paid to equipping railway crossings with modern automation and traffic safety. In different European countries, research and development work is being carried out and projects for the automation of train control are being implemented. At this stage, the automation process is being carried out more actively in public transport, especially successfully in the metro. The increasing use of distributed diagnostic components in rail transport has been observed along with the growing demand for a centralized diagnostic system. The works in the field of using the capabilities of artificial intelligence, machine learning and other modern technologies are noted. The study of foreign experience in digitalization of railway transport is useful to take into account when solving the problems of development and digitalization of Russian railways.
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Bian, Chong, Shunkun Yang, Tingting Huang, Qingyang Xu, Jie Liu, and Enrico Zio. "Degradation state mining and identification for railway point machines." Reliability Engineering & System Safety 188 (August 2019): 432–43. http://dx.doi.org/10.1016/j.ress.2019.03.044.
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Kim, Hee-Young, Daihee Park, and Yongwha Chun. "Fault Detection of Railway Point Machines Using Electrical Signals." Korean Data Analysis Society 22, no. 6 (December 30, 2020): 2225–35. http://dx.doi.org/10.37727/jkdas.2020.22.6.2225.
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Kozyrev, N. A., R. A. Shevchenko, A. A. Usol’tsev, A. N. Prudnikov, and L. P. Bashchenko. "Welding of differentially heat-strengthened rails. Industrial testing." Izvestiya. Ferrous Metallurgy 63, no. 5 (July 1, 2020): 305–12. http://dx.doi.org/10.17073/0368-0797-2020-5-305-312.
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The article describes the study of welding processes with the subsequent control of cooling of full-profile rail joints, produced by passing alternating electric current pulses after welding. The influence of welding modes on the quality of welded joint was investigated. Welding was carried out on a resistance butt welding machine MCP-6301 in conditions of the rail welding company LLC “RSP-M” (RSP-29). For research, the samples of P65 type full-profile rails of DT350 category 600 mm long were cut out. The isothermal holding conditions after welding were controlled using a personal computer with a change in the program of the SIMATIC S7-300 industrial controller and the software SIMATIC STEP 7, which allows modes of controlled cooling to be set. The control program was written in the LAD graphic language. To search for optimal modes of controlled cooling, a complete factor experiment N = 2k was carried out. Non-heat-treated joints were tested for three-point static bending according to the state standard STO RZD 1.08.002 – 2009 “Railway rails, welded by electric contact method”. Static bending tests were carried out on the press of PMS-320 type. Values of the force arising during bending Pbend and the bend deflection fpr at which the control sample is destroyed, were determined, as well as maximum values of these indicators if the sample was not destroyed during the tests. During the experiments, regression models were obtained for output parameters of the bending force and bend deflection. Macrostructure of the samples and distribution of the metal hardness on rolling surface of the rails welded joint were studied. A new method of resistance butt welding was developed, which makes it possible to obtain a welded connection of P65 type rails of DT350 category with properties that exceed the technical requirements of the mentioned state standard.
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Kim, H., J. Sa, Y. Chung, D. Park, and S. Yoon. "Fault diagnosis of railway point machines using dynamic time warping." Electronics Letters 52, no. 10 (May 2016): 818–19. http://dx.doi.org/10.1049/el.2016.0206.
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Lee, Jonguk, Heesu Choi, Daihee Park, Yongwha Chung, Hee-Young Kim, and Sukhan Yoon. "Fault Detection and Diagnosis of Railway Point Machines by Sound Analysis." Sensors 16, no. 4 (April 16, 2016): 549. http://dx.doi.org/10.3390/s16040549.
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Pan, Nan, Xin Shen, Xiaojue Guo, Min Cao, and Dilin Pan. "Study on intelligent anti–electricity stealing early-warning technology based on convolutional neural networks." Journal of Intelligent & Fuzzy Systems 40, no. 4 (April 12, 2021): 7993–99. http://dx.doi.org/10.3233/jifs-189621.
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In recent years, electricity stealing has been repeatedly prohibited, and as the methods of stealing electricity have become more intelligent and concealed, it is growing increasingly difficult to extract high-dimensional data features of power consumption. In order to solve this problem, a correlation model of power-consumption data based on convolutional neural networks (CNN) is established. First, the original user signal is preprocessed to remove the noise. The user signal with a fixed signal length is then intercepted and the parallel class labelled. The segmented user signals and corresponding labels are input into the convolutional neural network for training, and the trained convolutional neural network is then used to detect and classify the test user signals. Finally, the actual steal leak dataset is used to verify the effectiveness of this algorithm, which proves that the algorithm can effectively carry out anti–-electricity stealing by warning of abnormal power consumption behavior. There are lots of line traces on the surface of the broken ends which left in the cable cutting case crime scene along the high-speed railway in China. The line traces usually present nonlinear morphological features and has strong randomness. It is not very effective when using existing image-processing and three-dimensional scanning methods to do the trace comparison, therefore, a fast algorithm based on wavelet domain feature aiming at the nonlinear line traces is put forward to make fast trace analysis and infer the criminal tools. The proposed algorithm first applies wavelet decomposition to the 1-D signals which picked up by single point laser displacement sensor to partially reduce noises. After that, the dynamic time warping is employed to do trace feature similarity matching. Finally, using linear regression machine learning algorithm based on gradient descent method to do constant iteration. The experiment results of cutting line traces sample data comparison demonstrate the accuracy and reliability of the proposed algorithm.
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Sa, Jaewon, Younchang Choi, Yongwha Chung, Hee-Young Kim, Daihee Park, and Sukhan Yoon. "Replacement Condition Detection of Railway Point Machines Using an Electric Current Sensor." Sensors 17, no. 2 (January 29, 2017): 263. http://dx.doi.org/10.3390/s17020263.
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Yang, Chunsheng, Yanmin Sun, Chris Ladubec, and Yan Liu. "Developing Machine Learning-Based Models for Railway Inspection." Applied Sciences 11, no. 1 (December 22, 2020): 13. http://dx.doi.org/10.3390/app11010013.
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Smart railway maintenance is crucial to the safety and efficiency of railway operations. Successful deployment of technologies such as condition-based monitoring and predictive maintenance will enable railway companies to conduct proactive maintenance before defects and failures take place to improve operation safety and efficiency. In this paper, we first propose to develop a classification-based method to detect rail defects such as localized surface collapse, rail end batter, or rail components—such as joints, turning points, crossings, etc.—by using acceleration data. In order to improve the performance of the classification-based models and enhance their applicability in practice, we further propose a deep learning-based approach for the detection of rail joints or defects by deploying convolutional neural networks (CNN). CNN-based models can work directly with raw data to reduce the heavy preprocessing of feature engineering and directly detect joints located on either the left or the right rail. Two convolutional networks, ResNet and fully convolutional networks (FCN), are investigated and evaluated with the collected acceleration data. The experimental results show both deep neural networks obtain good performance, which demonstrate that the deep learning-based methods are effective for detecting rail joints or defects with the expected performance.
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Li, Hu, Qianen Xu, and Yang Liu. "Method for Diagnosing the Uneven Settlement of a Rail Transit Tunnel Based on the Spatial Correlation of High-Density Strain Measurement Points." Sustainability 13, no. 16 (August 18, 2021): 9245. http://dx.doi.org/10.3390/su13169245.
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Rail transit tunnels span long distances, are large-scale structures and pass through complicated geological conditions; thus, the risk of uneven settlement cannot be ignored. To address this issue, a method for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed in this study. First, with the distributed optical fiber sensing technology, a method for determining the intervals of strain measurement points with strong spatial correlations is proposed based on a support vector machine. Second, combined with the statistical analysis of the influence range of the uneven settlement of a tunnel, an algorithm for diagnosing the uneven settlement of regional railway tunnels based on the spatial correlation of high-density strain measurement points is proposed. Finally, the effectiveness of the proposed method is verified by numerical simulation and actual tunnel data.
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Wróbel, Andrzej, Marek Płaczek, and Andrzej Buchacz. "An Endurance Test of Composite Panels." Solid State Phenomena 260 (July 2017): 241–48. http://dx.doi.org/10.4028/www.scientific.net/ssp.260.241.
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In this paper possibilities of freight wagons modernization using new composite materials was presented. This work was carried out under the project number PBS2/A6/17/2013 agreement implemented under the Applied Research Program, founded by the National Centre for Research and Development. All of this activities are done as consortium consists of the scientific entity – the Institute of Engineering Processes Automation and Integrated Manufacturing Systems of the Silesian University of Technology and DB Schenker Rail Poland SA and Germaz. The purpose of this article is analyse the work strength of the connection sheet metal and composite glass-epoxy with multi-point pressure. To estimate the strength of strain-gauge methods were used. The study was conducted on a stand equipped with 4 pneumatic actuators and the PLC. During the tests the same steal plate, then on a plate with mounted rivets and connection steal and composite panel were checked. Some aspects have already been presented in previous publications of the authors. This article is an extension of the previous publications with new knowledge. To determine the changes in the samples strain gauges was used. In the article original program written to the PLC in order to automatically generate pressure on the tested composites was presented. A composite material is a material having a heterogeneous structure composed of two or more components with different properties. structural components most commonly used are fibers such as glass fibers, carbon fibers and Kevlar. Currently, composites are widely used, among others, in construction, aviation, in transprcie, automotive and machinery manufacturing. In this paper research methods for detection of laminate panels damage were presented. Aspect of continuous monitoring of the technical state of the laminate is very important. As part of the work method of endurance test panels made of composite by strain gauge will be presented. In paper machine designed to periodically generate extortion will be also presented. At the Faculty of Mechanical Engineering Technical University of Silesia are conducted research on the analysis and synthesis of mechanical systems and mechatronics concern both theoretical considerations [10,11] how practical industrial tasks Recent works undertaken relate to research the properties of composite materials [1-4, 12-17] as components and assemblies railway wagons [5,8,11]. Some of the work is conducted in collaboration with other research centers [5-9].
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Lumban-Gaol, Y. A., Z. Chen, M. Smit, X. Li, M. A. Erbaşu, E. Verbree, J. Balado, M. Meijers, and N. van der Vaart. "A COMPARATIVE STUDY OF POINT CLOUDS SEMANTIC SEGMENTATION USING THREE DIFFERENT NEURAL NETWORKS ON THE RAILWAY STATION DATASET." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 28, 2021): 223–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-223-2021.
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Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.
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Sysyn, Mykola, Vitalii Kovalchuk, Ulf Gerber, Olga Nabochenko, and Bogdan Parneta. "Laboratory Evaluation of Railway Ballast Consolidation by the Non-Destructive Testing." Communications - Scientific letters of the University of Zilina 21, no. 2 (May 24, 2019): 81–88. http://dx.doi.org/10.26552/com.c.2019.2.81-88.
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The property of railway track to resist the irreversible deformation for a long-term operational loading considerably depends on the ballast layer. The ballast layer is the element of a railway track whose mechanical properties and state are formed in-situ with the help of the ballast cleaning, tamping and distributing machines. The varied properties of the ballast material to obtain the form during the maintenance and retain it during the operation are equally important for the ballast layer. The control over the processes of the ballast consolidation and deconsolidation by means of measurement methods could potentially provide great possibilities for improvement of its properties both during the maintenance and operation. The paper deals with an experimental study of the railway ballast consolidation and deconsolidation processes under the vibration loading of the sleeper. A non-destructive measurement method is proposed to investigate the distribution of the ballast consolidation along the sleeper. The method is based on the measurement of time of mechanical wave propagation. Modern low-cost sensors and powerful microcontroller techniques enable creating smart measurement systems for automatic multi-point data acquisition, online processing and statistical estimation of the ballast consolidation distribution.
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Babishin, Vladimir, and Sharareh Taghipour. "An algorithm for estimating the effect of maintenance on aggregated covariates with application to railway switch point machines." Eksploatacja i Niezawodnosc - Maintenance and Reliability 21, no. 4 (September 14, 2019): 619–30. http://dx.doi.org/10.17531/ein.2019.4.11.
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Čepin, Marko. "An algorithm for estimating the effect of maintenance on aggregated covariates with application to railway switch point machines." Eksploatacja i Niezawodnosc - Maintenance and Reliability 21, no. 4 (September 14, 2019): 631–37. http://dx.doi.org/10.17531/ein.2019.4.12.
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Kerrouche, Abdelfateh, Taoufik Najeh, and Pablo Jaen-Sola. "Experimental Strain Measurement Approach Using Fiber Bragg Grating Sensors for Monitoring of Railway Switches and Crossings." Sensors 21, no. 11 (May 24, 2021): 3639. http://dx.doi.org/10.3390/s21113639.
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Railway infrastructure plays a major role in providing the most cost-effective way to transport freight and passengers. The increase in train speed, traffic growth, heavier axles, and harsh environments make railway assets susceptible to degradation and failure. Railway switches and crossings (S&C) are a key element in any railway network, providing flexible traffic for trains to switch between tracks (through or turnout direction). S&C systems have complex structures, with many components, such as crossing parts, frogs, switchblades, and point machines. Many technologies (e.g., electrical, mechanical, and electronic devices) are used to operate and control S&C. These S&C systems are subject to failures and malfunctions that can cause delays, traffic disruptions, and even deadly accidents. Suitable field-based monitoring techniques to deal with fault detection in railway S&C systems are sought after. Wear is the major cause of S&C system failures. A novel measuring method to monitor excessive wear on the frog, as part of S&C, based on fiber Bragg grating (FBG) optical fiber sensors, is discussed in this paper. The developed solution is based on FBG sensors measuring the strain profile of the frog of S&C to determine wear size. A numerical model of a 3D prototype was developed through the finite element method, to define loading testing conditions, as well as for comparison with experimental tests. The sensors were examined under periodic and controlled loading tests. Results of this pilot study, based on simulation and laboratory tests, have shown a correlation for the static load. It was shown that the results of the experimental and the numerical studies were in good agreement.
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Lu, Bi Hong, and H. P. Zheng. "Imageware and Pro/E Based Reverse Engineering Techniques for Drawbar in Heavy-Duty Railway Freight Cars." Key Engineering Materials 392-394 (October 2008): 320–24. http://dx.doi.org/10.4028/www.scientific.net/kem.392-394.320.
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As a new for heavy-railway vehicle connecting device, drawbar can eliminate connective gap and improve dynamic performance of the train obviously as it is well interchangeable, simple structure, easy manufacture, convenient maintenance, mature technology, high reliability, and widely applied abroad. Through the research on reverse engineering of foreign drawbar, authors present data collecting method based on the coordinates measuring machine (CMM) and data preprocessing by Imageware software, which including noise points removal, data alignment, simplification, sorting and benchmark transformation. Converting points cloud data by Pro/E independent geometric module, 3D models based on features and constraints were reconstructed, which support the redesign of parts and their manufacturing analysis. The outcome of the paper provides effective ways in terms of drawbar techniques digesting and absorption, and also supports innovative design of new products.
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Antoš, Jan. "Energy saving in rail transport." Acta Polytechnica CTU Proceedings 31 (July 14, 2021): 1–4. http://dx.doi.org/10.14311/app.2021.31.0001.
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Aim of this article is to introduce and present advantages and possibilities of using simulation tools to calculate energy flows of tractive vehicle in all phases of its ride. The focus is on the braking phase, especially on the braking energy recuperation that offers the highest energy saving potential.Energetic simulation was carried out as a part of a current railway reconstruction study, which is a good example of possible energetic calculation use. Energetic calculation in the simulation are performed using so called "exact method" developed at the Faculty of Transportation. The advantage of the method is that it provides a high degree of accuracy. Due to the simulation calculation being performed in real-time, the user can enter the calculation even during the simulation. Crucial part ofevery simulation are quality input data, such as vehicle braking characteristic (including all working points below the envelope line together with the efficiency characteristics of the power train parts (engine, transmission, convertor and others)). Also, sufficiently precise (with a precision of 1 meter) railway track details (detailed geometrical and other information on the current and reconstructed railway track or on the railway track proposal.To present savings during the ride, the railway track section on the track Montreál-Sherbrooke was chosen. On this track, (especially electrification and partial reconstruction of the superstructure) is planned. Reconstruction level will be dependant on the local economical and social conditions. However, in the most optimistic case, total electrification of the track will be undertaken, or lower level of the reconstruction when the vehicles that have very similar characteristics. The railway track section is located between the cities of Montreál and Sherbrooke, that are 160 km apart. To compare the current, and possible future, state, comparison of energy consumption of the vehicle was conducted on the same track with the same geometrical characteristics.We can assume that the overall energy intensity of the ride on the newly reconstructed track (eg. with greater radius of the arches) will be lower than the overall energy intensity of the ride on the track before reconstruction. Current data on energetic consumption are not available, therefore they were obtained by simulation and verified by calculations and considered sufficiently accurate.Presumably, the input simulation parameters required for the simulation will not always be delivered in the sufficient quality or will be partly missing completely. In these cases they will be substituted by various algorithms, that can be machine-processed and that are part of the simulation calculations. One of the crucial algorithms is so called "moment efficiency". Its machine processingis not entirely developed and therefore, this simulation will be conducted without its usage. For financial reasons, simulation calculation was verified only by the theoretical physical calculation method. Based on several partial results, emergent computing tool is functional and sufficiently accurate. The simulation program is supposed to be experimentally verified after its finishing.The outcomes presented in this article are calculated by simulated computation and were not confronted with the real situation in any way. However, relevant calculation algorithms, processes and theoretical background of the simulation were already verified on the older software versions. Therefore, the simulated outcomes are supposed to be at least sufficiently accurate to provide preliminary study for the railway reconstruction. Presented calculations were executed by using author’s simulation software.Part of the methodology was taken from the previous, already verified, versions of the software for vehicle ride simulation.
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I. Tchernyshev, N., O. E. Sysoev, D. B. Solovev, and E. P. Kiselyov. "Basic Robotecnical Platform for Implementation of Accurate Farming Technologies." Bulletin of Electrical Engineering and Informatics 7, no. 4 (December 1, 2018): 522–28. http://dx.doi.org/10.11591/eei.v7i4.920.
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Improvement of modern technical systems and technologies. Increasing the productivity of modern agricultural machines with increasing their weight, which leads, in the course of their work, to a significant compaction of the soil. The heterogeneity of the soil, as a bearing surface, causes not adjustable fluctuations in the workplace, which makes automation of the application of robotics more difficult. Modern solutions to the problems of reducing the negative impact on the soil, increasing the permeability of aggregates due to the reconstruction of the propulsors do not give the proper effect. More cardinally solve these problems, as well as the ability to implement automation and robotics bridge systems such as ABAC, moving along rail tracks, AASP on vertical piles and point gravel-halide supports with concrete platforms. The most promising of these is the AAS platform, which is a 30x10 m bridge structure that moves by step-by-step extension, the beams onto 3 subsequent pads located 10 m away. After entering the new position of the bridge platform, along the long 30-meter span beams Moves the work-technological module with a set of working elements, performing the programmed operations. Thus, in the AASP bridge system under consideration, the soil area subjected to compaction at reference points is just over 1% of the 70% protraction of modern machines. Compared to the ABAC system, moving along railways, the equipment of point supports is much less expensive and requires insignificant operating costs. At the same time, the rigidity of AASP design ensures stable operation of technological mechanisms in a programmed robotic mode with a minimum of unproductive energy costs associated with movement.
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Pinceti, Paolo, Micaela Caserza Magro, Enrico De Paola, Pierluigi Firpo, Enrico Marino, and Marco Antonelli. "CBM for a fleet of railway vehicles: infrastructure and algorithms." ACTA IMEKO 5, no. 4 (December 30, 2016): 56. http://dx.doi.org/10.21014/acta_imeko.v5i4.412.
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<p>Data acquisition and communication technologies give the possibility of receiving and storing a huge amount of data from machinery and plants in operation. From these data it is possible to create a set of Key Maintenance Indicators (KMI) useful for optimizing the maintenance policy. Raw data from the field are to be processed and filtered for obtaining effective KMIs to use in algorithms aimed at discovering anomalies or abnormal operation of one or more machineries or plants.</p><p>This paper presents a roadmap towards the Condition Based Maintenance of a fleet of railway vehicles. The paper associates to each maintenance policy its benefits and its requirements in terms of technological infrastructure and operating costs. Bombardier Transportation Italy started this roadmap a few years ago, for moving from a reactive maintenance policy to a proactive policy.</p><p>Increasing the effectiveness of maintenance implies the <em>sensorization</em> of the machines and the creation of a network for funneling information from the machineries to the central maintenance room. A Company must find an equilibrium point between complexity and expected benefits.</p><p>Results achieved by means of a specifically developed tool for data analysis applied to some sub-systems of the vehicles are presented.</p>
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Li, Jianbo, and Hongmei Shi. "Rail Corrugation Detection of High-Speed Railway Using Wheel Dynamic Responses." Shock and Vibration 2019 (February 25, 2019): 1–12. http://dx.doi.org/10.1155/2019/2695647.
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Rail corrugation often occurs on the high-speed railway, which will affect ride comfort and even the train operation safety in severe condition. Detection of rail corrugation wavelength and depth is absolutely essential for maintenance and safety. A novel method using wheel vibration acceleration is proposed in this paper, in which ensemble empirical mode decomposition (EEMD) is employed to estimate the wavelength, and bispectrum features are extracted to recognize the depth with support vector machine (SVM). Firstly, a vehicle-track coupling model considering the rail corrugation of high-speed railway is established to calculate the wheel vibration acceleration. Secondly, the estimation algorithm of wavelength is studied by analyzing the main frequency with EEMD. The optimal parameters of EEMD are selected according to the orthogonal coefficient of decomposition results and the distribution of the extreme points of signal. The depth detection is transformed to a classification problem with SVM. Bispectrum features, which are extracted from the reconstructed signal using the high-frequency components of wheel vibration acceleration, combining with train speed and corrugation wavelength are input into SVM to recognize the rail corrugation depth. Finally, numerical simulation is carried out to verify the accuracy of the proposed estimation method. The simulation results show that the proposed detection algorithm can accurately identify rail corrugation, the estimation error of rail corrugation wavelength is less than 0.25%, and the classification accuracy of rail corrugation depth is more than 99%.
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Wang, Judith, and Xiangwu Zeng. "Numerical Simulations of Vibration Attenuation of High-Speed Train Foundations With Varied Trackbed Underlayment Materials." Journal of Vibration and Control 10, no. 8 (August 2004): 1123–36. http://dx.doi.org/10.1177/1077546304043268.
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Interest in high-speed railway as an alternative means of transportation is steadily increasing around the world. However, high-speed trains come with the concern of track vibration and induced noise and ground vibration. Excessive track vibration can cause damage to trains and tracks and reduce riding comfort for passengers. Ground vibration induced by passing trains can also damage and disturb surrounding infrastructure (especially structures housing precision machines or instruments) and residents. One potential solution toward minimizing these vibrations is the use of rubber-modified asphalt concrete (RMAC) as a material for high-speed train trackbed underlayments. In this paper we present the results of a finite element simulation of a high-speed train foundation. The simulated foundation was subjected to dynamic loading in several test scenarios, with RMAC and other traditional paving materials used as trackbed underlayment materials. The ground accelerations at designated points in these simulations were then monitored and compared with one another to determine the relative effectiveness in vibration attenuation. From these parametric studies, RMAC proves to be more effective than currently used paving materials in damping out vibrations from dynamic loading. Implications for field applications are also discussed.
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Inoue, Ryo, Rihoko Ishiyama, and Ayako Sugiura. "Identifying local differences with fused-MCP: an apartment rental market case study on geographical segmentation detection." Japanese Journal of Statistics and Data Science 3, no. 1 (January 17, 2020): 183–214. http://dx.doi.org/10.1007/s42081-019-00070-y.
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AbstractAs the variety and quality of spatial data increase in recent times, the potential to analyze local characteristics based on spatial data is getting stronger. Previous spatial analysis methods structuralize the spatial autocorrelation of data by the distances between data observation points and the contiguity of the data-observed regions. It is significant for the estimation of global characteristics of spatial data. However, these approaches are not suitable for identifying local differences from the data since they assume a smooth spatial autocorrelation structure. Generalized fused lasso, which can detect local differences in spatial data, has been proposed in machine learning studies. Its limitation is that the estimated parameters are biased toward zero; however, methods that overcome the limitation have also been proposed. Fused-MCP is one of those methods and is expected to be useful in spatial analyses. This study applies fused-MCP to spatial analyses. As an example of spatial analyses based on fused-MCP, this study analyzes the structure of geographical segmentation of the real estate market in central Tokyo. Fused-MCP is utilized to extract areas where the valuation standard is the same. The results reveal that the geographical segmentation displays hierarchal patterns. Specifically, the market is divided by municipalities, railway lines and stations, and neighborhoods. The case study confirmed the applicability of fused-MCP to spatial analyses.
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Loktev, Daniil, and Olga Lokteva. "Image processing of transport objects using neural networks." E3S Web of Conferences 164 (2020): 03036. http://dx.doi.org/10.1051/e3sconf/202016403036.
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The paper is devoted to the development of an automated system model for monitoring and control of transport objects, based on the processing of images obtained using photo or video detectors, which can be installed on a fixed base near the transport highway for monitoring traffic flows and individual vehicles, and on rolling stock for monitoring transport infrastructure facilities. Image processing occurs by determining the function of blurring the image of an object, algorithms for extracting an image of an object using cascading classifiers and characteristic points, depending on the behavior of the object itself, as well as using a convolutional neural network. Machine learning of the convolutional neural network occurs when using the back propagation method of error. A neural network allows detecting objects of certain classes in the image, determining the parameters of their state and behavior. The proposed model with a movable hardware, which is responsible for obtaining the primary image, was tested on a section of the railway track to identify deviations of the state of the superstructure from the content standards, and a system with stationary photodetectors was tested to determine the parameters of moving vehicles. The obtained results of processing experimental data allowed drawing qualitative conclusions about the possibility of using the proposed algorithms and schemes for monitoring and control of various transport objects.
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Han, Lu, Chongchong Yu, Cuiling Liu, Yong Qin, and Shijie Cui. "Fault Diagnosis of Rolling Bearings in Rail Train Based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm." Applied Sciences 9, no. 15 (August 2, 2019): 3143. http://dx.doi.org/10.3390/app9153143.
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The rolling bearing is a key component of the bogie of the rail train. The working environment is complex, and it is easy to cause cracks and other faults. Effective rolling bearing fault diagnosis can provide an important guarantee for the safe operation of the track while improving the resource utilization of the rolling bearing and greatly reducing the cost of operation. Aiming at the problem that the characteristics of the vibration data of the rolling bearing components of the railway train and the vibration mechanism model are difficult to establish, a method for long-term faults diagnosis of the rolling bearing of rail trains based on Exponential Smoothing Predictive Segmentation and Improved Ensemble Learning Algorithm is proposed. Firstly, the sliding time window segmentation algorithm of exponential smoothing is used to segment the rolling bearing vibration data, and then the segmentation points are used to construct the localized features of the data. Finally, an Improved AdaBoost Algorithm (IAA) is proposed to enhance the anti-noise ability. IAA, Back Propagation (BP) neural network, Support Vector Machine (SVM), and AdaBoost are used to classify the same dataset, and the evaluation indexes show that the IAA has the best classification effect. The article selects the raw data of the bearing experiment platform provided by the State Key Laboratory of Rail Traffic Control and Safety of Beijing Jiaotong University and the standard dataset of the American Case Western Reserve University for the experiment. Theoretical analysis and experimental results show the effectiveness and practicability of the proposed method.
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Ke, Ting, Min Li, Lidong Zhang, Hui Lv, and Xuechun Ge. "Construct a biased SVM classifier based on Chebyshev distance for PU learning." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 3749–67. http://dx.doi.org/10.3233/jifs-192064.
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In some real applications, only limited labeled positive examples and many unlabeled examples are available, but there are no negative examples. Such learning is termed as positive and unlabeled (PU) learning. PU learning algorithm has been studied extensively in recent years. However, the classical ones based on the Support Vector Machines (SVMs) are assumed that labeled positive data is independent and identically distributed (i.i.d) and the sample size is large enough. It leads to two obvious shortcomings. On the one hand, the performance is not satisfactory, especially when the number of the labeled positive examples is small. On the other hand, classification results are not optimistic when datasets are Non-i.i.d. For this reason, this paper proposes a novel SVM classifier using Chebyshev distance to measure the empirical risk and designs an efficient iterative algorithm, named L∞ - BSVM in short. L∞ - BSVM includes the following merits: (1) it allows all sample points to participate in learning to prompt classification performance, especially in the case where the size of labeled data is small; (2) it minimizes the distance of the sample points that are (outliers in Non-i.i.d) farthest from the hyper-plane, where outliers are sufficiently taken into consideration (3) our iterative algorithm can solve large scale optimization problem with low time complexity and ensure the convergence of the optimum solution. Finally, extensive experiments on three types of datasets: artificial Non-i.i.d datasets, fault diagnosis of railway turnout with few labeled data (abnormal turnout) and six benchmark real-world datasets verify above opinions again and demonstrate that our classifier is much better than state-of-the-art competitors, such as B-SVM, LUHC, Pulce, B-LSSVM, NB and so on.
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"Indigenization of Track Tamping Machines for Indian Railway." International Journal of Innovative Technology and Exploring Engineering 9, no. 4 (February 10, 2020): 2105–9. http://dx.doi.org/10.35940/ijitee.d1487.029420.
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This paper gives a diagram of significance and advancement of automation of Indian railroad track support and different kinds of packing machines with their particular capacities which are utilized by Indian rail route for track upkeep. Railroad, being more secure and conservative, has been a favored mode for transportation of travelers and products. For long, the support of tracks were done physically, which was man power riched and tedious as well as had other specialist impediments of trouble in dealing with heavier track structure, constrained work window because of higher recurrence of trains, wellbeing of track upkeep faculty, and unpredictable nature of yield among others. Toward the beginning of automation Indian railroad imported the rail packing machine from plasser and theurer which is an Austrian organization. It is the main organization on the planet that gives a scope of packing machines for support of railroad tracks. At first the attention was solely on machines for track laying, track upkeep and estimating work. During the 1980s the range was reached out to incorporate machines for the establishment and support of overhead lines. From most recent couple of years Indian railroad is chipping away at indigenization of packing machines. For making indigenous machines railroad utilize figuring out system .based on sort of the rail line track (straight track, Curved track and point and intersection track) the packing machine is fabricated.
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Atamuradov, Vepa, Kamal Medjaher, Pierre Dersin, Noureddine Zerhouni, and Fatih Camci. "New Adaptive Prognostics Approach Based on Hybrid Feature Selection with Application to Point Machine Monitoring." Annual Conference of the PHM Society 10, no. 1 (September 24, 2018). http://dx.doi.org/10.36001/phmconf.2018.v10i1.500.
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This paper proposes a new adaptive prognostics approach consisting of hybrid feature selection and remaining-useful-life (RUL) estimation steps for railway point machines. In step-1, different time-domain based features are extracted and the best ones are selected by the hybrid feature selection method. Then, a degradation model is fitted to each of the selected features and the parameters are estimated. In step-2, the RUL of the component is predicted by using the proposed adaptive prognostics approach. The adaptive prognostics is based on the weighted likelihood combination of the estimated model parameters. The model parameters each of which estimated by curve fitting are used in the calculation of the likelihood probability weights. Then, an adaptive degradation model is built by using the weighted combination of the model parameter estimates and the component RUL is estimated. The proposed approach is validated on in-field point machine sliding-chair degradation and the results are discussed.
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Padovani, Damiano, and Monika Ivantysynova. "Investigation of an Energy Efficient Hydraulic Propulsion System for a Railway Machine." Journal of Dynamic Systems, Measurement, and Control 138, no. 3 (January 18, 2016). http://dx.doi.org/10.1115/1.4032223.
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Many railway construction and maintenance machines have large masses and perform repetitive working cycles with frequent stops that require precise positioning. For these reasons, a hydraulic propulsion system is a convenient choice. Common existing solutions make use of valve-controlled hydraulic circuits where inefficient fluid throttling takes place. Most of the time, dissipative braking is realized resulting in a remarkable quantity of available energy being wasted (up to 36 kJ of kinetic energy is available in the reference application). Additionally, the machine's automated positioning is a critical aspect. On some commercialized solutions, an overshoot of the desired final position is commonplace requiring a reverse motion in order to match the location of the desired working point. This is a negative characteristic as it introduces unnecessary fuel consumption and slows down productivity. Moreover, consideration of the limited adhesion in the wheel/rail interface is of critical importance. The propulsion system needs to be capable of differentiating the tractive or the braking torques between the driven axles. To this end, the paper proposes and analyzes a displacement-controlled (DC) propulsion system for a railway maintenance machine. The target is the removal of the fluid throttling mentioned above by defining an efficient hydraulic system. The ability to recover energy via regenerative breaking becomes a key process in improving the global machine efficiency. Simultaneously, an implementable control strategy is required for the proposed architecture to prevent overshoot of the desired position while stopping. To that end, this paper presents the mathematical model of the proposed layout used to simulate the system's behavior in order to confirm proper functioning. This work concludes with a discussion and definition of future improvements.