The degradation of the grease used to lubricate railroad bearings is believed to be caused by two processes: the mechanical processes occurring within the bearing and a diffusion process. Appropriate lubrication of the bearings is critical during railroad service operation. The study presented here will focus on the development of empirical models that can accurately predict the residual useful life of railroad bearing grease. Modeling techniques to be employed include regression, regression trees and split plots. The data set used in the development of the model consists of more than 100 samples of grease that were taken from railroad bearings. The bearings have been subjected to experimental variables such as load conditions, rotational speed, temperature, and mileage all of which have been observed in a laboratory setting. The mileage parameter is consistent with the total miles that were run using the grease from which the sample has been taken. Load, speed, and temperature values fluctuate within the total service operation of the bearing; therefore, a high value, a low value, and a weighted average are taken for the aforementioned parameters. The grease samples are taken from critical locations of the bearing, the inboard raceway, the outboard raceway and the spacer ring area, meaning that there are three samples collected from each railroad bearing, each having their own set of corresponding parameters. The oxidation induction time (OIT) of the grease is an indicator of the residual life of the grease; therefore, the OIT for each sample had been acquired using a differential scanning calorimeter (DSC). OIT is dependent upon mileage, load, speed, and temperature. This study was successful in developing an empirical model which can be utilized to predict the residual life for given operational characteristics.

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