The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 29-356
Full Name: Rizwan Faiz
Position: Ph.D. Candidate
Age: ON
Sex: Male
Address: Department of Computer Science,FH, Garendon Wing, Holleywell Park, Loughborough University,Leicestershire.
Country: UNITED KINGDOM
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E-mail address: r.faiz@lboro.ac.uk
Other E-mails: rizwanbinfaiz@hotmail.com
Title of the Paper: Information analysis of rail track for predictive maintenance
Authors as they appear in the Paper: R B Faiz , S.Singh
Email addresses of all the authors: r.faiz@lboro.ac.uk, s.singh@lboro.ac.uk
Number of paper pages: 11
Abstract: Track defects are deviation of actual from theoretical values of the tracks geometrical characteristics. Track defects are macroscopic and geometric in nature and are exclusively the consequence of train traffic. [5] Rail track maintenance in terms of track geometry and other modalities like rail profile, ultrasonic's etc has been typically based on use of reactive maintenance. In case of such reactive maintenance discrete exeecedence of track geometry parameter measurements are compared against a pre-set threshold such that if they exceed the threshold then reactive maintenance is required to be done. This is particularly so in cases when current practices rely on conservative, engineering, decision. Besides it will not inform staff regarding defects in rail track which can subsequently helps in predictive maintenance. This paper focuses on variable and time based linear regression analysis of track geometry parameters which can lead to significant predictive main!
tenance of track geometry. The over all aim of this research paper is to propose a predictive maintenance frame work that would assist in predicting future changes in rail track geometry measurements. Such framework can evaluate and prioritise track geometry maintenance effort across network rail. It will cause alarms before the defects will actually happen. Such research will result in effective and efficient rail track maintenance management resulting in low operating cost better train transit times for rail industry [2].
Keywords: Predictive Maintenance Management, Rail Track Geometry, CURV, Cant Def, Cross Level, Dipped Left, Gauge.
EXTENSION of the file: .doc
Special (Invited) Session: Predictive Maintenance Management of Rail Track
Organizer of the Session: 615-079
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