Continued research into the development of prototype optics based Top-of-Rail (TOR) Lubricity sensors has led to the discovery of benefits and drawbacks of different optical sensing technologies. Sensors of many different types have been investigated for their ability to detect track lubricants including Light Detection and Ranging (LIDAR), laser, and LED emitters. The individual performances of these different methods do not always translate directly when adapted for track lubricity measurements. This paper intends to unpack some of these conclusions and their specific relationships to the application for Top-of-Rail condition based monitoring in a revenue service environment.

Railroads use rail lubricants to reduce curving forces that contribute to rail and wheel wear and to increase train efficiency. Top-of-Rail Friction Modifiers (TORFM) and flange grease are applied to the rail by train wheels passing through wayside applicators. TORFM is inherently different from flange grease in that it is an engineered lubricant designed to manage the coefficient of friction to an optimal value for traction and braking forces while reducing lateral forces that contribute to wheel and rail wear. While track lubricants provide a compelling benefit for the railroads, there is a fair amount of ambiguity as to how much should be applied to the track, how far they carry down the track from the point of application, and how long they last beyond the time of application. The empirical methods that are currently available are ad hoc, imprecise, and subject to a large amount of error. It is desired to have sensors that can provide some level of objective assessment of the amount of lubricant available on the rail. Such a sensor can be used to answer many of the tor lubrication questions that currently remain unanswered.

A wide array of laboratory tests are carried out in this study to highlight the ability of several different optical sensors to detect track lubricants in a variety of environmental conditions. These tests are designed to determine the feasibility of each sensor and highlight any issues that may arise during field application. The tests serve as a prelude to field testing the sensors in a revenue service environment. The results of the study indicate that precision optical methods are able to detect the presence of TORFM on the rail surface although some optical methods are more robust in their ability to perform in adverse conditions than others.

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