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1 – 10 of 332Ali Zamani, Ahmad Mirabadi and Felix Schmid
In writing this paper, the authors investigated the use of electromagnetic sensors in axle counter applications by means of train wheel detection. The purpose of this paper is to…
Abstract
Purpose
In writing this paper, the authors investigated the use of electromagnetic sensors in axle counter applications by means of train wheel detection. The purpose of this paper is to improve the detection capability of train wheel detectors, by installing them in the optimal orientation and position, using finite element modeling (FEM) in combination with metamodeling techniques. The authors compare three common metamodeling techniques for the special case of wheel detector orientation: response surface methodology; multivariate adaptive regression splines; and kriging.
Design/methodology/approach
After analyzing the effective parameters of a train wheel detector, an appropriate method for decreasing the system susceptibility to electromagnetic noises is presented.
Findings
The results were validated using a laboratory‐based system and also the results of field tests carried out on the Iranian railway network. The results of the study suggest that the FEM method and a metamodeling technique can reduce the computational efforts and processing time.
Originality/value
In this paper, combination of FEM and metamodeling approaches are used to optimize the railway axle counter coils orientation, which is more insusceptible to electromagnetic noise than initial arrangement used by some signallers.
Details
Keywords
Weiming Tong, Yanlong Liu, Xianji Jin, Zhongwei Li and Jian Guan
The unilateral axle counting sensor is an important railway signal device that detects a train. For efficient and stable detection, the amplitude of induced electromotive force…
Abstract
Purpose
The unilateral axle counting sensor is an important railway signal device that detects a train. For efficient and stable detection, the amplitude of induced electromotive force and its changes must be big enough. Therefore, the purpose of this study is to find a way to design and optimize the sensor structure quickly and accurately.
Design/methodology/approach
With the help of extensive electromagnetic field calculations, the study puts forward a modified model based on the finite element method, establishes an independent air domain around the sensor, wheel and the railway and adopts a unique grid division method. It offers a design optimization method of the induction coil angles and its spatial location with respect to the excitation coil by using the combination weighting algorithm.
Findings
The modified modeling method can greatly reduce the number of finite element mesh and the operation time, and the method can also be applied to other areas. The combination weighting algorithm can optimize the structure of the sensor quickly and accurately.
Originality/value
This study provides a way to design and optimize the structure of the sensor and a theoretical basis for the development. The results can improve and expand the technology of the axle counting sensor.
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Matthias Asplund, Stephen Famurewa and Matti Rantatalo
The purpose of this paper is to investigate the failure-driven capacity consumption of wheels on the track, to determine whether there are some relations to vehicle wheel…
Abstract
Purpose
The purpose of this paper is to investigate the failure-driven capacity consumption of wheels on the track, to determine whether there are some relations to vehicle wheel configurations that show a larger amount of failures, and to ascertain the influence of the temperature and the travelling direction of the train on the number of events. This information can be used to develop prognostic health management so that more track capacity can be gained without modifications, re-building or re-investments.
Design/methodology/approach
This paper presents a study of 1,509 warning and alarm events concerning train wheels. The data come from the infrastructure manager's wheel defect detectors and wheel profile measurement system. These data have been analysed and processed to find patterns and connections to different vehicles, travelling directions and temperatures.
Findings
Lower temperatures increase the probability of wheels having high vertical forces. Trains with different wheel configurations show different results. With high vertical forces, the probability of wheel failures at axles 6 and 7 is high for locomotives with two bogies and three axles in each bogie (2×3). All these findings can be used to develop the maintenance, monitoring and inspection principles for wheels.
Practical implications
The inspection of wheels to detect failures needs to be more frequent on days and in seasons with lower temperatures. The wheel inspection should be performed more frequently at axles 6 and 7 for locomotives with a 2×3 wheel configuration. The inspection and monitoring of wheels need to be carried out more carefully for trains travelling south, to avoid a large amount of wheels with high force levels rolling in the southern direction.
Originality/value
The analysis carried out in this paper identifies important factors that correlate with the high occurrence of wheel defects. It also proposes a conceptual e-maintenance model for the combination of wheel condition monitoring data from different system. The value of this study is the provision of information to support prognostic and health management system to support proactive maintenance.
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Qi Xiao, Weidong Yu, Guangrong Tian and Fangxuan Li
This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.
Abstract
Purpose
This study aims to introduce the achievements and benefits of applying wheel/rail-force–based maintenance interval extension of the C80 series wagon in China.
Design/methodology/approach
Chinese wagons' existing maintenance strategy had left a certain safety margin for the characteristics of widely running range, unstable service environment and submission to transportation organization requirements. To reduce maintenance costs, China railway (CR) has attempted to extend the maintenance interval since 2020. The maintenance cycle of C80 series heavy haul wagons is extended by three months (no stable routing) or 50,000 km (regular routing). However, in the meantime, the alarming rate of the running state, a key index to reflect the severe degree of hunting stability, by the train performance detection system (TPDS) for the C80 series heavy haul wagons has increased significantly.
Findings
The present paper addresses a big data statistical way to evaluate the risk of allowing the C80 series heavy haul wagons to remain in operation longer than stipulated by the maintenance interval initial set. Through the maintenance and wayside-detector data, which is divided into three stages, the extension period (three months), the current maintenance period and the previous maintenance period, this method reveals the alarming rate of hunting was correlated with maintenance interval. The maintainability of wagons will be achieved by utilizing wagon performance degradation modeling with the state of the wheelset and the often-contact side bearing. This paper also proposes a statistical model to return to the average safety level of the previous maintenance period's baseline through correct alarming thresholds for unplanned corrective maintenance.
Originality/value
The paper proposes an approach to reduce safety risk due to maintenance interval extension by effective maintenance program. The results are expected to help the railway company make the optimal solution to balance safety and the economy.
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Shima Mousavi and Khashayar Khorasani
A decentralized dynamic neural network (DNN)-based fault detection (FD) system for the reaction wheels of satellites in a formation flying mission is proposed. The paper aims to…
Abstract
Purpose
A decentralized dynamic neural network (DNN)-based fault detection (FD) system for the reaction wheels of satellites in a formation flying mission is proposed. The paper aims to discuss the above issue.
Design/methodology/approach
The highly nonlinear dynamics of each spacecraft in the formation is modeled by using DNNs. The DNNs are trained based on the extended back-propagation algorithm by using the set of input/output data that are collected from the 3-axis of the attitude control subsystem of each satellite. The parameters of the DNNs are adjusted to meet certain performance requirements and minimize the output estimation error.
Findings
The capability of the proposed methodology has been investigated under different faulty scenarios. The proposed approach is a decentralized FD strategy, implying that a fault occurrence in one of the spacecraft in the formation is detected by using both a local fault detector and fault detectors constructed specifically based on the neighboring spacecraft. It is shown that this method has the capability of detecting low severity actuator faults in the formation that could not have been detected by only a local fault detector.
Originality/value
The nonlinear dynamics of the formation flying of spacecraft are represented by multilayer DNNs, in which conventional static neurons are replaced by dynamic neurons. In our proposed methodology, a DNN is utilized in each axis of every satellite that is trained based on the absolute attitude measurements in the formation that may nevertheless be incapable of detecting low severity faults. The DNNs that are utilized for the formation level are trained based on the relative attitude measurements of a spacecraft and its neighboring spacecraft that are then shown to be capable of detecting even low severity faults, thereby demonstrating the advantages and benefits of our proposed solution.
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In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of…
Abstract
Purpose
In this paper, a data mining approach is proposed for monitoring the conditions leading to a rail wheel high impact load. The proposed approach incorporates logical analysis of data (LAD) and ant colony optimization (ACO) algorithms in extracting patterns of high impact loads and normal loads from historical railway records. In addition, the patterns are employed in establishing a classification model used for classifying unseen observations. A case study representing real-world impact load data is presented to illustrate the impact of the proposed approach in improving railway services.
Design/methodology/approach
Application of artificial intelligence and machine learning approaches becomes an essential tool in improving the performance of railway transportation systems. By using these approaches, the knowledge extracted from historical data can be employed in railway assets monitoring to maintain the assets in a reliable state and to improve the service provided by the railway network.
Findings
Results achieved by the proposed approach provide a prognostic system used for monitoring the conditions surrounding rail wheels. Incorporating this prognostic system in surveilling the rail wheels indeed results in better railway services as trips with no-delay or no-failure can be realized. A comparative study is conducted to evaluate the performance of the proposed approach versus other classification algorithms. In addition to the highly interpretable results obtained by the generated patterns, the comparative study demonstrates that the proposed approach provides classification accuracy higher than other common machine learning classification algorithms.
Originality/value
The methodology followed in this research employs ACO algorithm as an artificial intelligent technique and LDA as a machine learning algorithm in analyzing wheel impact load alarm-collected datasets. This new methodology provided a promising classification model to predict future alarm and a prognostic system to guide the system while avoiding this alarm.
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G.H. Garbett and AMRAeS
Smiths Industries is to supply the head‐up display system for the Sea Harrier. The company will design, develop and make the electronic head‐up display and weapon aiming computer…
Abstract
Smiths Industries is to supply the head‐up display system for the Sea Harrier. The company will design, develop and make the electronic head‐up display and weapon aiming computer system for the latest version of the HS Harrier which will operate from Royal Navy ships.
Min Hyuc Ko, Kyoung Chul Kim, Abhijit Suprem, N. Prem Mahalik and Boem Sahng Ryuh
– The purpose of this paper is to demonstrate System-of-Systems (SoS) approach to design and development of unmanned robotic platform for greenhouse agricultural application.
Abstract
Purpose
The purpose of this paper is to demonstrate System-of-Systems (SoS) approach to design and development of unmanned robotic platform for greenhouse agricultural application.
Design/methodology/approach
SoS design approach is important in developing engineering products. It was observed that while system integration considers designs in a multi-disciplinary level framework, SoS is viewed as a solution focussed approach. In this paper, the authors have demonstrated SoS approach to develop a mobile robot platform. The wheels of the platform are independently controlled by using brushless DC and stepper motors based on fieldbus type Distributed Control System scheme.
Findings
The constraints for autonomous traveling were identified during the first phase followed by development of 12 distinct sub-routines during second phase of training. Optimal camera installation angle, driving speeds, steering angle per pixel were found to be valuable constraints for feed-forward parameters for real-time driving. The platform was field tested in a tomato planted greenhouse for yield and weed mapping.
Research limitations/implications
The paper focusses on studying vision-based autonomous four-wheel-drive (4WD) constraints and their implementation limitations.
Practical implications
The platform was field tested in a tomato planted greenhouse for yield and weed mapping.
Social implications
The platform can be used for agricultural operations such as crop scouting, monitoring, spraying, and mapping in a medium to large-scale greenhouse setting.
Originality/value
The research and presentation is original. Starting from its mechanical specification to wheel performance study, development of path patterns for training and global navigation algorithm for testing and validation were achieved. The platform can autonomously be driven without any manual intervention.
Details
Keywords
THE demands of war have created an increasing necessity for very strict inspection of component parts of aero and other internal combustion engines ; more particularly with the…
Abstract
THE demands of war have created an increasing necessity for very strict inspection of component parts of aero and other internal combustion engines ; more particularly with the former, as more and more power is demanded from them.