[go: up one dir, main page]

Ni et al., 2018 - Google Patents

A Bayesian machine learning approach for online wheel condition detection using track-side monitoring

Ni et al., 2018

Document ID
5460997423744987625
Author
Ni Y
Zhang Q
Publication year
Publication venue
2018 International Conference on Intelligent Rail Transportation (ICIRT)

External Links

Snippet

Online wheel condition monitoring can suffer from the stochastic wheel/rail dynamics and measurement noises. This paper aims to develop a Bayesian statistical approach for probabilistic assessment of wheel conditions using track-side monitoring. In this approach …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/46Processing the detected response signal, e.g. electronic circuits specially adapted therefor by spectral analysis, e.g. Fourier analysis or wavelet analysis
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M17/00Testing of vehicles
    • G01M17/007Testing of vehicles of wheeled or endless-tracked vehicles
    • G01M17/02Testing of vehicles of wheeled or endless-tracked vehicles of tyres
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/22Details, e.g. general constructional or apparatus details
    • G01N29/24Probes
    • G01N29/2493Wheel shaped probes
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01MTESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING STRUCTURES OR APPARATUS NOT OTHERWISE PROVIDED FOR
    • G01M13/00Testing of machine parts
    • G01M13/02Testing of gearing or of transmission mechanisms
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01RMEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
    • G01R31/00Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere

Similar Documents

Publication Publication Date Title
Wang et al. A Bayesian probabilistic approach for acoustic emission‐based rail condition assessment
Mosleh et al. Early wheel flat detection: an automatic data-driven wavelet-based approach for railways
Ni et al. A Bayesian machine learning approach for online detection of railway wheel defects using track-side monitoring
US20170052060A1 (en) Method and system for automatically detecting faults in a rotating shaft
Atamuradov et al. Machine health indicator construction framework for failure diagnostics and prognostics
Wang et al. Entropy-based local irregularity detection for high-speed railway catenaries with frequent inspections
US9607451B2 (en) Method and a system for merging health indicators of a device
Papp et al. A real-time algorithm for train position monitoring using optical time-domain reflectometry
CN108254179A (en) A kind of bullet train wheel set bearing method for diagnosing faults based on MEEMD arrangement entropys
Lourenço et al. Adaptive time series representation for out-of-round railway wheels fault diagnosis in wayside monitoring
Ni et al. A Bayesian machine learning approach for online wheel condition detection using track-side monitoring
AU2021413008A1 (en) Train compartment vibration monitoring method, vibration signal feature library establishment method and vibration signal feature library application method
Sysyn et al. Improvement of inspection system for common crossings by track side monitoring and prognostics
Rahman et al. Deep learning model for railroad structural health monitoring via distributed acoustic sensing
Wijaya et al. Distributed optical fibre sensor for condition monitoring of mining conveyor using wavelet transform and artificial neural network
Jorge et al. Early identification of out-of-roundness damage wheels in railway freight vehicles using a wayside system and a stacked sparse autoencoder
CN115943103B (en) Method for monitoring railway track and monitoring system for monitoring railway track
Joshuva et al. A machine learning approach for vibration signal based fault classification on hydraulic braking system through c4. 5 decision tree classifier and logistic model tree classifier
Yang et al. Improving unsupervised long-term damage detection in an uncontrolled environment through noise-augmentation strategy
CN117892084B (en) A bridge bearing static displacement probability prediction model construction method and early warning method
Lourenço et al. Automated green machine learning for condition-based maintenance
Mal et al. Modern condition monitoring systems for railway wheel-set dynamics: Performance analysis and limitations of existing techniques
Lourenço et al. Time series data mining for railway wheel and track monitoring: a survey
Ngetich et al. A Data-Driven Machine Learning Approach for Damage Size Quantification in Structural Elements
Liu et al. Wheel tread defect detection for high-speed trains using wheel impact load detector