Transactions on Machine Learning and Data Mining
P-ISSN: 1865-6781, E-ISSN 2509-9337
ISBN 978-3-942952-89-7

Volume 14 - Number 2 - October 2021 - Pages 55-69

Bearing Lubricant Defect Segmentation Using Synthetic Data

Richard Bellizzi1, Jason Galary2, Alfa Heryudono3

1,2 Applications Development & Validation Testing Lab, Nye Lubricants, USA

3 University of Massachusetts Dartmouth, USA


Lubricant testing often requires a post-test examination of specimens to obtain the desired critical measurement. Advancing these analysis methods aids in develop-ing higher-performing products by allowing for improved insight into the lubri-cants’ performance. Building off previous work in Computer Vision and Ma-chine Learning, this work aims to extend the use of these methods into the lubri-cant 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 con-sistent, granular analysis of these tests can help lubricant development withstand stringent requirements. R-Mask CNN methods provide an option to apply instance segmentation tech-niques 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’ data set 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.

Keywords:Machine Learning, Computer Vision, Defect Segmentation, Synthetic Data

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