Abstract
Lubricant testing often requires a post-test examination of specimens to obtain the desired critical measurement. Advancing these analysis methods aids in developing higher-performing products by allowing for improved insight into the lubricants’ performance. Building off previous work in Computer Vision and Machine Learning, this work aims to extend the use of these methods into the lubricant testing realm. Minimizing defects is a desirable outcome since part of the lubricants’ role is to protect the bearing’s surface. While large-scale defects are easy to interpret, it becomes difficult to differentiate between test results when comparing bearing examples with less apparent defects. Providing a more consistent, granular analysis of these tests can help lubricant development withstand stringent requirements.R-Mask CNN methods provide an option to apply instance segmentation techniques to classify areas of interest, allowing for an image with multiple instances of these defects. Since big data is the fuel for a system like this, there are certain limitations regarding the number of examples for lubricant bearing surface defect data. Leveraging data amplification techniques allows for a synthetic ‘big’ dataset to accommodate the model’s needs. This paper lays out how these tools work synergistically to provide a model that can operationalize for a company sooner than waiting to generate a complete set of ideal data