@inproceedings{5e42fa8d45db471ebc4f4c64c7f4b5e5,
title = "Automating predictive maintenance using oil analysis and machine learning",
abstract = "Predictive maintenance aims to reduce costly and time consuming repairs, and also avoid unnecessary activities by proposing a maintenance strategy that is informed by machine condition monitoring. The majority of mechanical systems are oil lubricated, therefore oil analysis provides a rich source of machine condition data for many mechanical systems. This research investigates the use of random forests, feed-forward neural networks and logistic regression models trained using oil analysis data for classifying machine conditions. The RF model outperformed the other classifiers for all machine conditions. The interpretation of the feature importance for the RF models were found to be consistent with industry knowledge, demonstrating the potential use of RF as a diagnostic tool in predictive maintenance.",
keywords = "Machine learning, Oil analysis, Predictive maintenance",
author = "Sarah Keartland and {Van Zyl}, {Terence L.}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Southern African Universities Power Engineering Conference/Robotics and Mechatronics/Pattern Recognition Association of South Africa, SAUPEC/RobMech/PRASA 2020 ; Conference date: 29-01-2020 Through 31-01-2020",
year = "2020",
month = jan,
doi = "10.1109/SAUPEC/RobMech/PRASA48453.2020.9041003",
language = "English",
series = "2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 International SAUPEC/RobMech/PRASA Conference, SAUPEC/RobMech/PRASA 2020",
address = "United States",
}