On the Development of a Predictive Rail Maintenance Planning Methodology Utilizing Parametric Weibull Prediction Methods

Cronin, John J; Zarembski, Allan M.

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On the Development of a Predictive Rail Maintenance Planning Methodology Utilizing Parametric Weibull Prediction Methods

2020-09-01
By Cronin, John J ; Zarembski, Allan M.

File Language:

English

Details

Creators:

Cronin, John J ; Zarembski, Allan M.
Corporate Creators:

University of Delaware. Department of Civil and Environmental Engineering
Corporate Contributors:

University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability. USDOT Tier 1 ; United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
Subject/TRT Terms:
Publication/ Report Number:

RailTEAM UD-2
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

University of Nevada. University Transportation Center on Improving Rail Transportation Infrastructure Sustainability and Durability
Abstract:

The railroad industry has used for the past 50 years the 2-Parameter Weibull equation to determine the rate of rail fatigue defect occurrences and to forecast the fatigue life of railroad rail. With the advent of more powerful computers, more frequent data collection and new techniques to analyze data, a new field of data analysis has been created, Data Analytics, sometimes referred to as “Big Data”. This report makes use of this new area of Data Analytics to develop and implement an improved rail defect forecasting approach building upon the traditional Weibull equation to overcome many of its limitations and problems.
Format:

PDF
Funding:

69A3551747132
Download URL:

https://rosap.ntl.bts.gov/view/dot/80896/dot_80896_DS1.pdf
File Type:

[PDF - 6.62 MB]
Collection(s):

University Transportation Centers
Main Document Checksum:

urn:sha-512:d8ccf2db73c224ada6bafdf902c22b1c52dbea1e7b0e893ae21f02844895896fdc2c376733dfa36f873b76e0e7c1755c95de636e8a7683e7c4e9661884de4c09

File Language:

English

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