Analysis of Wheel Wear and Forecasting of Wheel Life for Transit Rail Operations: A Machine Learning Approach

Ebersole, K.; Zarembski, Allan M.; Palese, Joseph; University of Delaware. Department of Civil and Environmental Engineering

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ROSA P serves as an archival repository of USDOT-published products including scientific findings, journal articles, guidelines, recommendations, or other information authored or co-authored by USDOT or funded partners. As a repository, ROSA P retains documents in their original published format to ensure public access to scientific information.

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Analysis of Wheel Wear and Forecasting of Wheel Life for Transit Rail Operations: A Machine Learning Approach

2019-03-27
By Ebersole, K. ; Zarembski, Allan M. ; Palese, Joseph

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Creators:

Ebersole, K. ; Zarembski, Allan M. ; Palese, Joseph

Ebersole, K. ; Zarembski, Allan M. ; Palese, Joseph Less -
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

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 Less -
Subject/TRT Terms:

[+]

Deterioration Machine Learning Measurement Stochastic Processes Tire Treads Wear Wheels
Publication/ Report Number:

RailTEAM UD-4
Resource Type:

Tech Report
Geographical Coverage:

United States ; New York (New York)

United States ; New York (New York) Less -
Corporate Publisher:

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

As transit vehicle wheels accrue mileage, they experience flange and tread wear based on the contact between the railhead and wheel-running surface. When wheels wear excessively, the likelihood of accidents and derailments increases. Thus, regular maintenance is performed on the wheels, until they require replacement. One common maintenance practice is truing; using a specially designed cutting machine to bring a wheel back to an acceptable profile. This process removes metal from the wheel and is often based on wheel flange thickness standards (and sometimes wheel flange angle). Wheel replacement is usually driven by rim thickness, which is continuously reduced by wear, as well as metal removal during truing. This research study used wheel wear data provided by the New York City Transit Authority (NYCTA) to analyze wheel wear trends and forecast wheel maintenance (truing based on flange thickness) and wheel life (replacement based on rim thickness).
Format:

PDF
Funding:

69A3551747132
Collection(s):

University Transportation Centers
Main Document Checksum:

[+]

urn:sha-512:b8c639fc843aa8c3135749f7cee5faff0b7a8327c424d47fd66a164ff8ab448e68eb96c18e6a980c842a229e5041a7623d99583bcae15e52cb072d2fcbbf10ce
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https://rosap.ntl.bts.gov/view/dot/80984/dot_80984_DS1.pdf
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Analysis of Wheel Wear and Forecasting of Wheel Life for Transit Rail Operations: A Machine Learning Approach

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