Advanced Search

Journal Navigation

Journal Home

Subscriptions

Archive

Contact Us

Table of Contents

CiteULike is a free service for managing and discovering scholarly references - click here to get started.

Sign In to gain access to subscriptions and/or personal tools.
Structural Health Monitoring
This Article
Right arrow Full Text (PDF)
Right arrow References
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in Web of Science
Right arrow Alert me to new issues of the journal
Right arrow Add to Saved Citations
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Request Reprints
Right arrow Add to My Marked Citations
Citing Articles
Right arrow Citing Articles via Google Scholar
Right arrow Citing Articles via Scopus
Google Scholar
Right arrow Articles by Jia, S.
Right arrow Articles by Dhanasekar, M.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Complore   Add to Connotea   Add to Del.icio.us   Add to Digg   Add to Reddit   Add to Technorati   Add to Twitter  
What's this?

Detection of Rail Wheel Flats using Wavelet Approaches

S. Jia

Centre for Railway Engineering Central Queensland University, Australia

M. Dhanasekar

Centre for Railway Engineering Central Queensland University, Australia, m.dhanasekar{at}cqu.edu.au

Sudden application of heavy traction/braking torque induces flats in rail wagon wheelsets. Flat wheels generate high frequency impact forces sufficient to cause severe damage to the rail head surface. Early detection of flats would facilitate re-profiling the wheels before they cause serious damage to critical rail components, such as switches, crossings, and insulated rail joints. Although there are many sensors available in the market to detect/record wagon accelerations, there are no powerful signal processing tools readily available to detect the presence of wheel flats from the recorded acceleration signatures. This study presents two wavelet approaches to overcome the difficulties in the on-board monitoring and detection systems of rail wheel flats using vibration signals. Signal average techniques, wavelet local energy average concept, and wavelet decomposition are employed in this study. A Matlab-Simulink based dynamic simulation system capable of modeling the wheel flats and track irregularities is also developed for predicting the wheelset/bogie frame acceleration time series. An analysis of the numerical simulation results demonstrates that the methods proposed in this study are effective for the on-board monitoring of wheel flats of sizes smaller than the condemning limits.

Key Words: rail wheel flat • vibration monitoring • wavelet approaches • daubechies wavelets • signal processing

Structural Health Monitoring, Vol. 6, No. 2, 121-131 (2007)
DOI: 10.1177/1475921706072066
Add to CiteULike CiteULike   Add to Complore Complore   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us   Add to Digg Digg   Add to Reddit Reddit   Add to Technorati Technorati   Add to Twitter Twitter    What's this?