Data-driven track geometry fault localisation using unsupervised machine learning

Author:

Popov K.ORCID,De Bold R.,Chai H.-K.,Forde M.C.,Ho C.L.,Hyslip J.P.,Kashani H.F.,Kelly R.,Hsu S.S.,Rippin M.

Publisher

Elsevier BV

Subject

General Materials Science,Building and Construction,Civil and Structural Engineering

Reference31 articles.

1. Challenges of Big Data analysis;Fan;Natl. Sci. Rev.,2014

2. Establishment of Track Quality Index Standard Recommendations for Beijing Metro;Liu;Discret. Dyn. Nat. Soc.,2015

3. Development of Railway Track Geometry Indexes Based on Statistical Distribution of Geometry Data;Sadeghi;J. Transp. Eng.,2010

4. Analyzing Major Track Quality Indices and Introducing a Universally Applicable TQI;Offenbacher;Appl. Sci.,2020

5. Intelligent index for railway track quality evaluation based on Bayesian approaches;Movaghar;Struct. Infrastruct. Eng.,2020

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