Fault detection and diagnosis for railway switching points using fuzzy neural network

Cheng, Y.H.; Zhao, Huibing

doi:10.1109/iciea.2015.7334231

Cited by 11 publications

(11 citation statements)

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“…A model-based FDD algorithm was applied to S&C system fault diagnosis by Cheng and Zhao [12]. In this work, the model-based FDD method developed was applied to an S&C system with a speed of up to 160 km/h.…”

Section: Fdd Methodsmentioning

confidence: 99%

“…From the above reviewed literature, the lack of papers applying a model-based FDD method for S&C systems is obvious. Further, very few among these published papers [12,76,84] actually applied and used the residuals principle for detecting and then diagnosing S&C system faults. Thus, there is an opportunity for researchers to consider applying model-based methods to deal with FD, FDD, and/or FP of the different possible fault modes (previously shown in Figure 9) in railway S&C…”

Section: Fp Methodsmentioning

confidence: 99%

“…Just 5.5% of S&C track of the total track length of the Swedish railway network was found to cost more than 13% of the total maintenance cost [10] and its failures caused 14% of all train delays [11]. Statistics from the railway from Zhuzhou to Hengyang in China showed that more than 40% of the total failures were related to S&C equipment failure [12]. In addition, in a cost-consolidation exercise within the INNOTRACK project in 2010 [13], it was found that S&C system maintenance and inspection accounted for around 19% of the total maintenance costs in the UK, and in the financial year 2011/2012, S&C cost the rail infrastructure owner 24% of the total track maintenance budget and 23% of renewal budgets, whilst accounting for only 5% of network mileage [14].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Fdd Methodsmentioning

confidence: 99%

Section: Fp Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Therefore, it is important to detect turnout defects timely. Switch is one of the three outdoor railway equipment [7]. It has many characteristics such as large quantity, complex structure, short using life, limiting the train speed, low driving safety, big investment for maintenance etc.…”

Section: Introductionmentioning

confidence: 99%

“…It has many characteristics such as large quantity, complex structure, short using life, limiting the train speed, low driving safety, big investment for maintenance etc. [7]. To locate railway vehicles exact and reliably in railroad network, collision avoidance systems are required.…”

Section: Introductionmentioning

confidence: 99%

A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

Taştimur

Karaköse

Akın

2016

International Journal of Applied Mathematics, Electronics and Computers

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Rail track, turnout and level crossing are critical parts of railway components. So faults of rail surface are directly related to the operation safety. The defects occurring in switches can lead to accidents and the derailment of the train. Therefore, it is necessary to utilize advanced technology to monitor rail damage of the switch and level crossing zones. In this study, the switch and level crossing detection are performed with vision based contactless image processing techniques. Edge detection algorithm, image processing filters, morphological feature extraction, Hough transform and hierarchical SVM classifier are utilized to detect turnout and level crossing regions in the railway images. While switch points are detected, images of the area in front of the train are continuously taken from camera placed on the train. These images contain foreign objects such as buildings, trees, etc. The foreign objects existing in the railway image make it difficult to detect accurately the rail track. The turnout and level crossing detection cannot also be performed using the information of the rail track that cannot be correctly detected. The several processes, such as some image processing filters and morphological feature extraction, have been performed to solve this problem. With destroying the foreign objects from the railway images, both the accuracy rate of the proposed method has been increased and real-time implementation of the method is provided. The Hough transform is utilized to detect rail track, switch and level crossing in the proposed method. The turnout detection is performed by applying SVM classifier to railway image. The proposed method provides real-time rail track, level crossing and turnout detection. Furthermore, this method has gained dynamic feature due to not be utilized any template image for switch detection. When evaluated from these aspects, both the proposed method is superior to other methods in literature and results were obtained in a shorter time thanks to applying ROI Segmentation to the railway image.

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A Novel Group Teaching Optimization Algorithm Based Artificial Neural Network for Classification

Moosavi

Younis

Zafar

et al. 2022

Communications in Computer and Information Science

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Fault detection and diagnosis for railway switching points using fuzzy neural network

Cited by 11 publications

References 1 publication

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

A Vision Based Condition Monitoring Approach for Rail Switch and Level Crossing using Hierarchical SVM in Railways

A Novel Group Teaching Optimization Algorithm Based Artificial Neural Network for Classification

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