Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track/vehicle transmission system

Debiolles, A.; Denoeux, Th.; Oukhellou, Latifa; Aknin, Patrice

doi:10.1109/icif.2006.301611

Cited by 12 publications

(4 citation statements)

References 6 publications

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“…Finally, an overview of the proposed diagnosis method is presented. For all the methods, 70% of the samples were used for training, the remaining ones being used for testing The track circuit is an essential component of the automatic train control system [15,28]. Its main function is to detect the presence or absence of vehicle traffic within a specific section of railway track.…”

Section: Fault Diagnosis In Railway Track Circuitsmentioning

confidence: 99%

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Côme

Oukhellou

Denœux

et al. 2011

Pattern Anal Applic

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Independent factor analysis (IFA) defines a generative model for observed data that are assumed to be linear mixtures of some unknown non-Gaussian, mutually independent latent variables (also called sources or independent components). The probability density function of each individual latent variable is modelled by a mixture of Gaussians. Learning in the context of this model is usually performed within an unsupervised framework in which only unlabelled samples are used. Both the mixing matrix and the parameters of latent variable densities are learned from the observed data. This paper investigates the possibility of estimating an IFA model in a noiseless setting when two kinds of prior information are incorporated, namely constraints on the mixing process and partial knowledge on the cluster membership of some training samples. Semi-supervised or partially supervised learning frameworks can thus be handled. The investigation of these two kinds of prior information was motivated by a realworld application concerning the fault diagnosis of railway track circuits. Simulated data, resulting from both these applications, are provided to demonstrate the capacity of our approach to enhance estimation accuracy and remove the indeterminacy commonly encountered in unsupervised IFA, such as source permutations.

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Section: Fault Diagnosis In Railway Track Circuitsmentioning

confidence: 99%

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Côme

Oukhellou

Denœux

et al. 2011

Pattern Anal Applic

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“…This was applied to French track circuits and vehicle equipment [9]. Two years later, to avoid the least squares approach, Debiolles et al proposed a fault detection method using a compensation capacitor based on a segmented selection output coding strategy, taking the segmented short-circuit current as the characteristic parameter [10]. In 2008, Chen et al presented laboratory research results of track circuit fault monitoring and detection methods [11].…”

Section: Introductionmentioning

confidence: 99%

Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

2020

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With the rapid development of railway traffic, traffic safety has become a focus. The ZPW-2000A jointless track circuit is an important part of train control systems. Currently, the fault detection of the ZPW-2000A jointless track circuit still relies on the experience of maintenance personnel, which can introduce several problems, such as a low fault detection efficiency and large amounts of required labor. Although some artificial intelligence fault detection algorithms for the ZPW-2000A track circuit have been developed, their detection accuracy is not high enough to meet the needs of large-scale applications, and due to security requirements, the actual ZPW-2000A track circuit fault data cannot be directly obtained in large quantities. To solve these problems, an equivalent theoretical model of the Chinese ZPW-2000A jointless track circuit is proposed by using four-terminal network theory. Through this equivalent theoretical model, the original fault data were collected. Considering that the relationship between fault data and fault types of the ZPW-2000A jointless track circuit is not obvious, a deep belief network was designed to detect the fault modes of the ZPW-2000A jointless track circuit. In order to optimize the deep belief network performance, the particle swarm optimization algorithm optimized by the genetic algorithm (GAPSO) was selected to optimize the deep belief network. The simulation experiments indicated that the optimized deep belief network could achieve a 98.5% fault detection accuracy and a 98.6% F1 Score rate, which showed that the deep belief network optimization by the particle swarm optimization algorithm which was optimized by the genetic algorithm (GAPSO-DBN) model proposed in this paper, had high accuracy and robustness. The results show that it had higher accuracy and robustness than other fault detection methods, and it can greatly improve the level of ZPW-2000A track circuit fault detection in the future. INDEX TERMS ZPW-2000A jointless track circuit, deep belief network, fault detection, particle swarm optimization, four-terminal network, genetic algorithm.

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“…To identify and diagnose failures of the track circuits several methods can be applied: electric models [1], control and diagnosis equipment [2][3][4], neuro-fuzzy systems, neural networks [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

Spunei

Piroi

Muscai

et al. 2018

IOP Conf. Ser.: Mater. Sci. Eng.

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Output coding of spatially dependent subclassifiers in evidential framework. Application to the diagnosis of railway track/vehicle transmission system

Cited by 12 publications

References 6 publications

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Fault diagnosis of a railway device using semi-supervised independent factor analysis with mixing constraints

Research on Fault Detection for ZPW-2000A Jointless Track Circuit Based on Deep Belief Network Optimized by Improved Particle Swarm Optimization Algorithm

Truck circuits diagnosis for railway lines equipped with an automatic block signalling system

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