Agent based heterogeneous data integration and maintenance decision support for high-speed railway signal system

Yang, Lianbao; Xu, Tianhua; Wang, Zhenxian

doi:10.1109/itsc.2014.6957995

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…For instance, aiming to carry out the maintenance operation for faulty equipment, the authors in Reference [ 116 ], by annotating the fault features recorded during system maintenance, employ the CBR framework to diagnose faults in vehicle onboard equipment of high-speed trains. On this basis, Reference [ 117 ] develops a new intelligent maintenance decision system, by integrating into CBR methods, to obtain the desired FD results of CTCS-300T vehicle onboard equipment in high-speed trains.…”

Section: Applications Of Qualitative Ifd Approach In High-speed Trmentioning

confidence: 99%

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Cheng

Wang

Chen

et al. 2020

Entropy

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For ensuring the safety and reliability of high-speed trains, fault diagnosis (FD) technique plays an important role. Benefiting from the rapid developments of artificial intelligence, intelligent FD (IFD) strategies have obtained much attention in the field of academics and applications, where the qualitative approach is an important branch. Therefore, this survey will present a comprehensive review of these qualitative approaches from both theoretical and practical aspects. The primary task of this paper is to review the current development of these qualitative IFD techniques and then to present some of the latest results. Another major focus of our research is to introduce the background of high-speed trains, like the composition of the core subsystems, system structure, etc., based on which it becomes convenient for researchers to extract the diagnostic knowledge of high-speed trains, where the purpose is to understand how to use these types of knowledge. By reasonable utilization of the knowledge, it is hopeful to address various challenges caused by the coupling among subsystems of high-speed trains. Furthermore, future research trends for qualitative IFD approaches are also presented.

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Section: Applications Of Qualitative Ifd Approach In High-speed Trmentioning

confidence: 99%

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

Cheng

Wang

Chen

et al. 2020

Entropy

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“…Some studies have been developed to integrate data-driven models within a decision-making framework for railway maintenance, considering different assets and case studies. Morant et al [33] and Yang et al [34] considered, for example, the issue of maintaining the signaling systems of a rail line, while Núñez et al [35], Jamshidi et al [36], and Consilvio et al [37] focused their studies on the maintenance of rail tracks.…”

Section: Literature Reviewmentioning

confidence: 99%

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

et al. 2020

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The objective of this study is to show the applicability of machine learning and simulative approaches to the development of decision support systems for railway asset management. These techniques are applied within the generic framework developed and tested within the In2Smart project. The framework is composed by different building blocks, in order to show the complete process from data collection and knowledge extraction to the real-world decisions. The application of the framework to two different real-world case studies is described: the first case study deals with strategic earthworks asset management, while the second case study considers the tactical and operational planning of track circuits’ maintenance. Although different methodologies are applied and different planning levels are considered, both the case studies follow the same general framework, demonstrating the generality of the approach. The potentiality of combining machine learning techniques with simulative approaches to replicate real processes is shown, evaluating the key performance indicators employed within the considered asset management process. Finally, the results of the validation are reported as well as the developed human–machine interfaces for output visualization.

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“…The terms, such as train control system, onboard equipment, SDU, are the instances corresponding to the concepts, System, Subsystem, and Component, respectively. At abstract level, the railway fault ontology is a structure of the form: = ( , , , → ) [16]. The represents concepts in the railway fault diagnostic domain.…”

Section: Data Acquisition and Feature Extractionmentioning

confidence: 99%

“…Case-base creation jobs are formulated using the MapReduce parallel data processing model. Yang and Xu et al [16] prescribe an agent-based heterogeneous data integration and maintenance decision support for highspeed railway signal system, in which ontology and CBR are integrated for fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

Zhong

Wang

et al. 2018

Mathematical Problems in Engineering

Self Cite

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In Discrete Event System, such as railway onboard system, overwhelming volume of textual data is recorded in the form of repair verbatim collected during the fault diagnosis process. Efficient text mining of such maintenance data plays an important role in discovering the best-practice repair knowledge from millions of repair verbatims, which help to conduct accurate fault diagnosis and predication. This paper presents a text case-based reasoning framework by cloud computing, which uses the diagnosis ontology for annotating fault features recorded in the repair verbatim. The extracted fault features are further reduced by rough set theory. Finally, the case retrieval is employed to search the best-practice repair actions for fixing faulty parts. By cloud computing, rough set-based attribute reduction and case retrieval are able to scale up the Big Data records and improve the efficiency of fault diagnosis and predication. The effectiveness of the proposed method is validated through a fault diagnosis of train onboard equipment.

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Agent based heterogeneous data integration and maintenance decision support for high-speed railway signal system

Cited by 6 publications

References 4 publications

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

A Review of Intelligent Fault Diagnosis for High-Speed Trains: Qualitative Approaches

On Applying Machine Learning and Simulative Approaches to Railway Asset Management: The Earthworks and Track Circuits Case Studies

Text Case-Based Reasoning Framework for Fault Diagnosis and Predication by Cloud Computing

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