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Journal ArticleDOI

Fault detection and diagnosis for railway track circuits using neuro-fuzzy systems

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TLDR
It is shown that the proposed method correctly detects and diagnoses the most commonly occurring track circuit failures in a laboratory test rig of one type of audio frequency jointless track circuit.
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This article is published in Control Engineering Practice.The article was published on 2008-05-01. It has received 123 citations till now. The article focuses on the topics: Fault detection and isolation & Neuro-fuzzy.

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Citations
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Journal ArticleDOI

Railway Track Circuit Fault Diagnosis Using Recurrent Neural Networks

TL;DR: The long-short-term memory (LSTM) recurrent neural network is proposed to accomplish fault detection and identification tasks based on the commonly available measurement signals by considering the signals from multiple track circuits in a geographic area.
Patent

Trip optimization system and method for a train

TL;DR: In this paper, a system for operating a train having one or more locomotive consists with each locomotive consist comprising one or multiple locomotives is presented, which includes a locator element to determine a location of the train, a track characterization element to provide information about a track, a sensor for measuring an operating condition of the locomotive, a processor operable to receive information from the locator elements, the track characterizing element, and the sensor, and an algorithm embodied within the processor having access to the information to create a trip plan that optimizes performance of the
Journal ArticleDOI

Fault diagnosis for temperature, flow rate and pressure sensors in VAV systems using wavelet neural network

TL;DR: Wavelet neural network, the integration of wavelet analysis and neural networks, is presented to diagnose the faults of sensors including temperature, flow rate and pressure in variable air volume (VAV) systems to ensure well capacity of energy conservation.
Journal ArticleDOI

Fault diagnosis network design for vehicle on-board equipments of high-speed railway

TL;DR: An automated diagnosis network of VOBE for high-speed train via a deep learning approach, which improves the accuracy of fault diagnosis for VOBEs to 9095% in HSRs and outperforms both KNN and ANN-BP.
Journal ArticleDOI

Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics

TL;DR: This systematic survey aims at presenting the current trends in diagnostics and prognostics giving special attention to multi-model approaches and summarizing the current challenges and research opportunities.
References
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Journal ArticleDOI

ANFIS: adaptive-network-based fuzzy inference system

TL;DR: The architecture and learning procedure underlying ANFIS (adaptive-network-based fuzzy inference system) is presented, which is a fuzzy inference System implemented in the framework of adaptive networks.
Book

Introduction to Linear Regression Analysis

TL;DR: In this paper, the authors propose a simple linear regression model with variable selection and multicollinearity for robust regression, and validate the model using regression analysis and validation of regression models.
Book

Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence

TL;DR: This text provides a comprehensive treatment of the methodologies underlying neuro-fuzzy and soft computing with equal emphasis on theoretical aspects of covered methodologies, empirical observations, and verifications of various applications in practice.
Journal ArticleDOI

Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

TL;DR: Interestingly, neuro fuzzy and soft computing a computational approach to learning and machine intelligence that you really wait for now is coming.
Book

Robust Model-Based Fault Diagnosis for Dynamic Systems

TL;DR: Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research.
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