Quantification of the Sensitivity of Two Prevalent Track Inspection Systems

Al-Nazer, Leith; Raslear, Thomas; Wilson, Robert; Kidd, John

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Quantification of the Sensitivity of Two Prevalent Track Inspection Systems

2017-05-01
By Al-Nazer, Leith ; Raslear, Thomas ; Wilson, Robert

File Language:

English

Details

Creators:

Al-Nazer, Leith ; Raslear, Thomas ; Wilson, Robert ; Kidd, John
Corporate Creators:

United States. Federal Railroad Administration. Office of Research, Development, and Technology ; QinetiQ North America, Inc.
Corporate Contributors:

United States. Federal Railroad Administration. Office of Research and Development ; United States. Federal Railroad Administration. Office of Railroad Policy and Development
Subject/TRT Terms:
Publication/ Report Number:

DOT/FRA/ORD-17/03
Resource Type:

Tech Report
Geographical Coverage:

United States
Corporate Publisher:

United States. Federal Railroad Administration. Office of Research, Development, and Technology
Abstract:

This report uses signal detection theory to quantify the sensitivity of two common track inspection systems (manual visual inspection via a hi-rail vehicle and automated track geometry inspection). Quantifying the sensitivity of such systems can provide baseline input data for risk-based, probabilistic track inspection programs, which cannot determine optimal track inspection methodologies without accurate probabilities of detection for various track inspection methods. The results show that there are no overall differences in sensitivity (ability to detect deteriorated or anomalous track conditions) between visual and automated inspection methods. However, there is a strong bias with both systems to indicate “no defect,” which results in a high rate of misses of deteriorated and anomalous track conditions.
Format:

PDF
Download URL:

https://rosap.ntl.bts.gov/view/dot/37340/dot_37340_DS1.pdf
File Type:

[PDF - 3.48 MB]
Collection(s):

Federal Railroad Administration
Main Document Checksum:

urn:sha256:7f9841132fe1e72591c5d926ab8504670c4cf448216ca379bd9504a86cc15923

File Language:

English

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