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Article
Publication date: 7 September 2012

Ali 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…

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.

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Article
Publication date: 11 December 2017

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…

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87

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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Article
Publication date: 5 August 2014

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

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1076

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.

Details

Journal of Quality in Maintenance Engineering, vol. 20 no. 3
Type: Research Article
ISSN: 1355-2511

Keywords

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Abstract

Details

Harnessing the Power of Failure: Using Storytelling and Systems Engineering to Enhance Organizational Learning
Type: Book
ISBN: 978-1-78754-199-3

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Article
Publication date: 4 February 2014

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…

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.

Details

International Journal of Intelligent Unmanned Systems, vol. 2 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 1 December 1975

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…

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.

Details

Aircraft Engineering and Aerospace Technology, vol. 47 no. 12
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 5 August 2014

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.

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311

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

International Journal of Intelligent Unmanned Systems, vol. 2 no. 3
Type: Research Article
ISSN: 2049-6427

Keywords

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Article
Publication date: 1 February 1945

J.E.D. Bell

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…

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.

Details

Aircraft Engineering and Aerospace Technology, vol. 17 no. 2
Type: Research Article
ISSN: 0002-2667

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Article
Publication date: 1 January 1990

Bennett J. Price

Fire protection has several components: prevention, containment, detection, and suppression. While building codes and inspectors generally do not require special…

Abstract

Fire protection has several components: prevention, containment, detection, and suppression. While building codes and inspectors generally do not require special construction techniques or fire protection systems for computer rooms, economic and service factors may dictate that special protection be given such facilities. This article discusses emergency planning, the various types of fire detection and suppression systems, and future options, with particular attention given to halon and possiblehalon‐replacements.

Details

Library Hi Tech, vol. 8 no. 1
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 21 May 2021

Chang Liu, Samad M.E. Sepasgozar, Sara Shirowzhan and Gelareh Mohammadi

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the…

Abstract

Purpose

The practice of artificial intelligence (AI) is increasingly being promoted by technology developers. However, its adoption rate is still reported as low in the construction industry due to a lack of expertise and the limited reliable applications for AI technology. Hence, this paper aims to present the detailed outcome of experimentations evaluating the applicability and the performance of AI object detection algorithms for construction modular object detection.

Design/methodology/approach

This paper provides a thorough evaluation of two deep learning algorithms for object detection, including the faster region-based convolutional neural network (faster RCNN) and single shot multi-box detector (SSD). Two types of metrics are also presented; first, the average recall and mean average precision by image pixels; second, the recall and precision by counting. To conduct the experiments using the selected algorithms, four infrastructure and building construction sites are chosen to collect the required data, including a total of 990 images of three different but common modular objects, including modular panels, safety barricades and site fences.

Findings

The results of the comprehensive evaluation of the algorithms show that the performance of faster RCNN and SSD depends on the context that detection occurs. Indeed, surrounding objects and the backgrounds of the objects affect the level of accuracy obtained from the AI analysis and may particularly effect precision and recall. The analysis of loss lines shows that the loss lines for selected objects depend on both their geometry and the image background. The results on selected objects show that faster RCNN offers higher accuracy than SSD for detection of selected objects.

Research limitations/implications

The results show that modular object detection is crucial in construction for the achievement of the required information for project quality and safety objectives. The detection process can significantly improve monitoring object installation progress in an accurate and machine-based manner avoiding human errors. The results of this paper are limited to three construction sites, but future investigations can cover more tasks or objects from different construction sites in a fully automated manner.

Originality/value

This paper’s originality lies in offering new AI applications in modular construction, using a large first-hand data set collected from three construction sites. Furthermore, the paper presents the scientific evaluation results of implementing recent object detection algorithms across a set of extended metrics using the original training and validation data sets to improve the generalisability of the experimentation. This paper also provides the practitioners and scholars with a workflow on AI applications in the modular context and the first-hand referencing data.

1 – 10 of 306