Wheel flat and out-of round formation and growth

2003 ◽  
Author(s):  
T. Snyder ◽  
D.H. Stone ◽  
J. Kristan
Keyword(s):  
Wheel Flat

Wheel-flat diagnostic tool via wavelet transform

2006 ◽  
Vol 20 (8) ◽  
pp. 1953-1966 ◽  
Author(s):  
Vittorio Belotti ◽  
Francesco Crenna ◽  
Rinaldo C. Michelini ◽  
Giovanni B. Rossi
Keyword(s):  
Wavelet Transform ◽  
Diagnostic Tool ◽  
Wheel Flat

scholarly journals Simulation Analysis of the Effects of a Rail Vehicle Running with Wheel Flat

2016 ◽  
Vol 16 (5) ◽  
pp. 889-896 ◽  
Author(s):  
Ján Dižo ◽  
Stasys Steišūnas ◽  
Miroslav Blatnický
Keyword(s):  
Simulation Analysis ◽  
Rail Vehicle ◽  
Wheel Flat

scholarly journals Wheel fault diagnosis model based on multichannel attention and supervised contrastive learning

2021 ◽  
Vol 13 (12) ◽  
pp. 168781402110670
Author(s):  
Yanxiang Chen ◽  
Zuxing Zhao ◽  
Euiyoul Kim ◽  
Haiyang Liu ◽  
Juan Xu ◽  
...  
Keyword(s):  
Network Architecture ◽  
Feature Learning ◽  
Feature Representation ◽  
Diagnostic Methods ◽  
Learning Ability ◽  
Noise Interference ◽  
Backbone Network ◽  
Mechanical Methods ◽  
Wheel Flat ◽  
Wheel Radius

As wheels are important components of train operation, diagnosing and predicting wheel faults are essential to ensure the reliability of rail transit. Currently, the existing studies always separately deal with two main types of wheel faults, namely wheel radius difference and wheel flat, even though they are both reflected by wheel radius changes. Moreover, traditional diagnostic methods, such as mechanical methods or a combination of data analysis methods, have limited abilities to efficiently extract data features. Deep learning models have become useful tools to automatically learn features from raw vibration signals. However, research on improving the feature-learning capabilities of models under noise interference to yield higher wheel diagnostic accuracies has not yet been conducted. In this paper, a unified training framework with the same model architecture and loss function is established for two homologous wheel faults. After selecting deep residual networks (ResNets) as the backbone network to build the model, we add the squeeze and excitation (SE) module based on a multichannel attention mechanism to the backbone network to learn the global relationships among feature channels. Then the influence of noise interference features is reduced while the extraction of useful information features is enhanced, leading to the improved feature-learning ability of ResNet. To further obtain effective feature representation using the model, we introduce supervised contrastive loss (SCL) on the basis of ResNet + SE to enlarge the feature distances of different fault classes through a comparison between positive and negative examples under label supervision to obtain a better class differentiation and higher diagnostic accuracy. We also complete a regression task to predict the fault degrees of wheel radius difference and wheel flat without changing the network architecture. The extensive experimental results show that the proposed model has a high accuracy in diagnosing and predicting two types of wheel faults.

scholarly journals Time series data interpretation for ‘wheel-flat’ identification including uncertainties

2019 ◽  
pp. 147592171988711
Author(s):  
Wen-Jun Cao ◽  
Shanli Zhang ◽  
Numa J Bertola ◽  
I F C Smith ◽  
C G Koh
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Model Updating ◽  
Information Gain ◽  
Data Interpretation ◽  
Detection Methods ◽  
Series Data ◽  
Point Selection ◽  
Modelling Uncertainties ◽  
Wheel Flat

Train wheel flats are formed when wheels slip on rails. Crucial for passenger comfort and the safe operation of train systems, early detection and quantification of wheel-flat severity without interrupting railway operations is a desirable and challenging goal. Our method involves identifying the wheel-flat size by using a model updating strategy based on dynamic measurements. Although measurement and modelling uncertainties influence the identification results, they are rarely taken into account in most wheel-flat detection methods. Another challenge is the interpretation of time series data from multiple sensors. In this article, the size of the wheel flat is identified using a model-falsification approach that explicitly includes uncertainties in both measurement and modelling. A two-step important point selection method is proposed to interpret high-dimensional time series in the context of inverse identification. Perceptually important points, which are consistent with the human visual identification process, are extracted and further selected using joint entropy as an information gain metric. The proposed model-based methodology is applied to a field train track test in Singapore. The results show that the wheel-flat size identified using the proposed methodology is within the range of true observations. In addition, it is also shown that the inclusion of measurement and modelling uncertainties is essential to accurately evaluate the wheel-flat size because identification without uncertainties may lead to an underestimation of the wheel-flat size.

scholarly journals Assessment of a rail vehicle running with the damaged wheel on a ride comfort for passengers

2018 ◽  
Vol 157 ◽  
pp. 03004 ◽  
Author(s):  
Ján Dižo ◽  
Miroslav Blatnický ◽  
Stasys Steišūnas ◽  
Blanka Skočilasová
Keyword(s):  
Ride Comfort ◽  
Railway Wheel ◽  
Rail Vehicle ◽  
Rail Vehicles ◽  
Dynamic Analyses ◽  
Wheel Flat

In certain conditions rail vehicles wheels can be during operation damaged. Then, the profile of wheels is no longer circular, but it is changed depending on the type and severity of defects. When such rail vehicle with the damaged wheel operates, the quality of a ride comfort for passenger is degraded. This article is focused on the assessment of ride comfort for passenger based on results obtained from dynamic analyses. Simulations and calculations were carried out in commercial multibody software. In our research we considered one type of the railway wheel untrueness – wheel-flat. This type of wheel damaging is relatively common and has such influence on the ride comfort for passenger worsening, which needs to be detected and investigated.

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