Bayesian network based fault diagnosis and maintenance for high-speed train control systems

Cheng, Yu; Xu, Tianhua; Yang, Lianbao

doi:10.1109/qr2mse.2013.6625915

Cited by 15 publications

(8 citation statements)

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“…We utilize the simulation software named GeNIe version 2.0 for the inference usage of the developed BN models. Cheng et al [17] modeled the dynamic Bayesian network, which is based on the GeNIe software, to locate the fault of the train control system and deduce the system maintenance strategies. Yang et al [18] modeled the integrated and sub-Bayesian network of TCMS, which is based on the GeNIe software, to utilize the prior information and find the reliability weaknesses.…”

Section: Results Of the Experimental Analysismentioning

confidence: 99%

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

Wang

Chen

et al. 2021

IEEE Access

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Section: Results Of the Experimental Analysismentioning

confidence: 99%

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

Wang

Chen

et al. 2021

IEEE Access

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“…Train operation is controlled by a speed-distance curve, which is generated through information interaction. There is self-diagnostic software in train control system; the self-diagnostic result is written into the “txt file.” Cheng et al 70 used Bayesian network to build the model from the “txt file” data, and then the reverse reasoning ability of the Bayesian network was applied to locate the maximum possible cause. There are other types of data in train control system; the suitable fault diagnosis methods will be verified in the next section.…”

Section: Literature Review On Fault Diagnosis Methods and Applicationmentioning

confidence: 99%

Methods for fault diagnosis of high-speed railways: A review

Zang

Shangguan

Cai

et al. 2019

Proceedings of the IMechE

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High-speed railways have a high demand for safety, but they are complex systems when it comes to fault diagnosis. The failure propagation path is difficult to trace which makes it hard to detect and identify a fault in the traditional way like signal-based methods. In recent years, artificial intelligence methods have been successfully applied in system health diagnosis and prognosis. Fault diagnosis methods based on artificial intelligence methods provide a new inspiration for fault diagnosis in the high-speed railway systems. In this article, the current research status of fault diagnosis was introduced, and the practical application of fault diagnosis methods in high-speed railways was summarized. Then taking the train control system as an example, fault diagnosis based on the artificial intelligence methods was discussed using several case studies; the results proved that the fusion of different methods has the potential to improve the diagnostic accuracy. Finally, the future research direction of fault diagnosis for high-speed railways was proposed.

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“…The mobile robot is controlled via a wireless network by a control station in order to achieve a target solution. The developed BN is characterized by a multilayer structure that many works [1], [2], [13], [16] have proved its efficiency in a diagnosis procedure. Besides, the BN is distributed between the system nodes to have a Modular Bayesian Network (MBN).…”

Section: Introductionmentioning

confidence: 99%

Distributed to embedded Bayesian Network for diagnosis of a networked robot

Sassi

Gouin

Thiriet

2017

2017 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applicati

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Mobile networked robots are distributed systems controlled by a distant station i.e, the controller is implemented on a control station. The main mission of the mobile robot is reaching a target position starting from an initial one while receiving the instructions from the control station via a wireless network. The wireless network is characterized by a stochastic behavior and is sensitive to perturbations. The unreliability of wireless networks does not guarantee data transmission between system components (the robot and the station) which can cause system performance degradation. A distributed Bayesian Network (BN) was proposed in previous work to monitor and diagnose the system performance and to model causal uncertainties between failures. The developed BN is a modular Bayesian Network (MBN) which is composed of three Bayesian modules shared between the robot and the control station. In the case of system performance degradation because of a bad network state, the robot switches to embedded controller implemented on-board (autonomous behavior). The distributed diagnosis architecture must be updated and the diagnosis tool becomes embedded on the robot. A procedure of BNs assembly is described in this work in order to implement one monolithic BN on-board. The obtained monolithic BN is the result of combining two Bayesian modules from the modular Bayesian Network: the control and the operative Bayesian modules.

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Bayesian network based fault diagnosis and maintenance for high-speed train control systems

Cited by 15 publications

References 1 publication

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

Fault Diagnosis of Train Network Control Management System Based on Dynamic Fault Tree and Bayesian Network

Methods for fault diagnosis of high-speed railways: A review

Distributed to embedded Bayesian Network for diagnosis of a networked robot

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