PHM for railway system &amp;#x2014; A case study on the health assessment of the point machines

Ardakani, Hossein Davari; Lucas, Christina; Siegel, David; Chang, Shuo; Dersin, Pierre; Bonnet, Benjamin; Lee, Jay

doi:10.1109/icphm.2012.6299533

Cited by 29 publications

(21 citation statements)

References 12 publications

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“…In this paper, only FD was considered. Several other published papers dealing only with FD for S&C systems were found [91][92][93][94][95][96][97].…”

Section: Fd Methodsmentioning

confidence: 99%

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

et al. 2019

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Railway switch and crossing (S&C) systems have a very complex structure that requires not only a large number of components (such as rails, check rails, switches, crossings, turnout bearers, slide chair, etc.) but also different types of components and technologies (mechanical devices to operate switches, electrical and/or electronic devices for control, etc.). This complexity of railway S&C systems makes them vulnerable to failures and malfunctions that can ultimately cause delays and even fatal accidents. Thus, it is crucial to develop suitable condition monitoring techniques to deal with fault detection and diagnosis (FDD) in railway S&C systems. The main contribution of this paper is to present a comprehensive review of the existing FDD techniques for railway S&C systems. The aim is to overview the state of the art in rail S&C and in doing so to provide a platform for researchers, railway operators, and experts to research, develop and adopt the best methods for their applications; thereby helping ensure the rapid evolution of monitoring and fault detection in the railway industry at a time of the increased interest in condition based maintenance and the use of high-speed trains on the rail network.

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“…In this paper, only FD was considered. Several other published papers dealing only with FD for S&C systems were found [91][92][93][94][95][96][97].…”

Section: Fd Methodsmentioning

confidence: 99%

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

et al. 2019

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“…In the case of failures that have occurred, the solution time of the failure can be shortened by obtaining information about the root cause of the failure and the railway operation can be returned to its normal course with minimum interruption. Statistical analysis, classification, and model-based methods are used in the literature to evaluate data related to failures and to make predictions [2][3][4]. In particular, classification methods are frequently used in the detection of railway failures [2].…”

Section: Introductionmentioning

confidence: 99%

Prediction of railway switch point failures by artificial intelligence methods

Arslan¹,

Tiryaki²

2020

Turk J Elec Eng & Comp Sci

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In recent years, railway transport has been preferred intensively in local and intercity freight and passenger transport. For this reason, it is of utmost importance that railway lines are operated in an uninterrupted and safe manner. In order to carry out continuous operation, all systems must continue to operate with maximum availability. In this study, data were collected from switch motors, which are the important equipment of railways, and the related equipment and these data were evaluated with sector experience and the results related to the failure status of the switch points were revealed. The obtained results were processed with support vector machines and artificial neural networks, which are artificial intelligence methods, and machine learning was performed. In the light of this learning, a decision support model, which predicts possible failures and gives information about the root cause of the failures that have occurred, was developed. This model aims to ensure that the data obtained in each movement of the railway switch point are processed and the necessary corrective and preventive actions are communicated to the maintenance personnel; thus, failures are eliminated before they affect the railway operation and the solution process of the failures that have occurred is shortened. Considering the six switch points from which the data were collected, the experimental results were predicted with 24 % RMSE error rates in the SVM method, while they were successfully predicted with RMSE error rates ranging from 2.4 % to 6.6 % in the ANN method. Therefore, it is observed that the ANN method is more appropriate in the implementation of the established model.

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“…There are several studies with focus on railway applications, using diagnostic data to assess system condition and perform fault detection for railway systems, including turnout systems [15]- [17], and track circuits [18]. There are also selected studies considering rolling stock systems, including door systems [19], [20], which is also the analysed system in the present study.…”

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confidence: 99%

Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures

2015

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In this paper, a fuzzy classification approach applying a combination of Echo-State Networks (ESNs) and a Restricted Boltzmann Machine (RBM) is proposed for predicting potential railway rolling stock system failures using discrete-event diagnostic data. The approach is demonstrated on a case study of a railway door system with real data. Fuzzy classification enables the use of linguistic variables for the definition of the time intervals in which the failures are predicted to occur. It provides a more intuitive way to handle the predictions by the users, and increases the acceptance of the proposed approach. The research results confirm the suitability of the proposed combination of algorithms for use in predicting railway rolling stock system failures. The proposed combination of algorithms shows good performance in terms of prediction accuracy on the railway door system case study.Index Terms-Discrete-event diagnostic data, Echo-state networks, failure prediction, fuzzy sets, railway door system failures, restricted Boltzman machines.

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PHM for railway system — A case study on the health assessment of the point machines

Cited by 29 publications

References 12 publications

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview

Prediction of railway switch point failures by artificial intelligence methods

Fuzzy Classification With Restricted Boltzman Machines and Echo-State Networks for Predicting Potential Railway Door System Failures

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