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An Unsupervised Fault Detection Support System for Railway Turnouts
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10182-013-0213-1openalex-extracted
A survey of functional principal component analysis
inferredcites →https://doi.org/10.1016/j.engfailanal.2021.105446openalex-extracted
A hierarchical method based on improved deep forest and case-based reasoning for railway turnout fault diagnosis
inferredcites →https://doi.org/10.1016/j.matcom.2024.04.017openalex-extracted
Different PCA approaches for vector functional time series with applications to resistive switching processes
inferredcites →https://doi.org/10.1016/j.measurement.2018.01.062openalex-extracted
An auto-associative residual based approach for railway point system fault detection and diagnosis
inferredcites →https://doi.org/10.1016/j.measurement.2019.107162openalex-extracted
Railway turnout system RUL prediction based on feature fusion and genetic programming
inferredcites →https://doi.org/10.1016/j.sigpro.2019.107299openalex-extracted
Selective review of offline change point detection methods
inferredcites →https://doi.org/10.1016/j.trc.2017.03.002openalex-extracted
Empirically quantifying city-scale transportation system resilience to extreme events
inferredcites →https://doi.org/10.1016/j.trc.2019.02.001openalex-extracted
Predictive maintenance using tree-based classification techniques: A case of railway switches
inferredcites →https://doi.org/10.1016/j.trc.2020.102737openalex-extracted
Detection and identification of significant events in historical aircraft trajectory data
inferredcites →https://doi.org/10.1016/j.trc.2022.103679openalex-extracted
A literature review of Artificial Intelligence applications in railway systems
inferredcites →https://doi.org/10.1016/s0169-7439(99)00047-7openalex-extracted
The Mahalanobis distance
inferredcites →https://doi.org/10.1049/itr2.12299openalex-extracted
A self‐adaptive phase‐segmentation and health assessment framework for point machines
inferredcites →https://doi.org/10.1080/08982112.2014.933980openalex-extracted
B-Spline Approach for Failure Detection and Diagnosis on Railway Point Mechanisms Case Study
inferredcites →https://doi.org/10.1093/tse/tdac036openalex-extracted
Data-driven technology of fault diagnosis in railway point machines: review and challenges
inferredcites →https://doi.org/10.1109/demped.2009.5292755openalex-extracted
Failure diagnostics for railway point machines using expert systems
inferredcites →https://doi.org/10.1109/iciss55894.2022.9915046openalex-extracted
Predictive Maintenance on Railway Turnout System: A Systematic Literature Review
inferredcites →https://doi.org/10.1109/icmla.2019.00083openalex-extracted
A Comparative Analysis of Unsupervised Learning Techniques for Anomaly Detection in Railway Systems
inferredcites →https://doi.org/10.1109/icmla.2019.00172openalex-extracted
Evaluating Unsupervised Anomaly Detection Models to Detect Faults in Heavy Haul Railway Operations
inferredcites →https://doi.org/10.1109/jiot.2023.3243391openalex-extracted
Unsupervised Deep Learning for IoT Time Series
inferredcites →https://doi.org/10.1109/tim.2020.2998863openalex-extracted
An Unsupervised Fault-Detection Method for Railway Turnouts
inferredcites →https://doi.org/10.1146/annurev-statistics-041715-033624openalex-extracted
Functional Data Analysis
inferredcites →https://doi.org/10.1155/2017/3192967openalex-extracted
Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization
inferredcites →https://doi.org/10.2219/rtriqr.47.182openalex-extracted
Electric Switch Machine Failure Detection Using Data-Mining Technique
inferredcites →https://doi.org/10.23919/iconac.2018.8749078openalex-extracted
Fault Diagnosis of High-Speed Railway Turnout Based on Convolutional Neural Network
inferredcites →https://doi.org/10.3233/jifs-189688openalex-extracted
An intelligent fault diagnosis method based on curve segmentation and SVM for rail transit turnout
inferredcites →https://doi.org/10.3390/app9235129openalex-extracted
On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
inferredcites →https://doi.org/10.3390/app9235139openalex-extracted
Fault Diagnosis of Railway Point Machines Using the Locally Connected Autoencoder
inferredcites →https://doi.org/10.3390/s20092692openalex-extracted
Railway Point-Operating Machine Fault Detection Using Unlabeled Signaling Sensor Data
inferredcites →https://doi.org/10.3390/s20164627openalex-extracted
An Online Classification Method for Fault Diagnosis of Railway Turnouts
What points at this work
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inferred← citeshttps://doi.org/10.3390/app152211934openalex-snapshot
State Evaluation of Wheel–Rail Force in High-Speed Railway Turnouts Based on Multivariate Analysis and Unsupervised Clustering
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