Machine learning model for predictive maintenance on linear accelerators
Date
2024
Authors
Tonui, A. K. K.
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
Predicting machine failures is the next frontier in industrial machine maintenance. However, the ideal implementation of such a program will require the fitting of industrial machines with sensors that can constantly monitor a machine’s vital parameters while feeding them to a supervisory module for analysis and possible action. However, such an undertaking will require massive capital and time investments to achieve. This is where log file mining and analysis come in. By analysing the already existing machine log files of medical linear accelerators, a prediction model was developed to anticipate motor problems and notify engineers without investing the capital outlay of fitting new sensors on a machine. Mining of the log files yielded an imbalanced dataset containing 3.367% anomalies. This study tested three algorithms for their predictive power with the Random Forest classifier coming out on top with 99% precision, recall and accuracy. It was closely followed by Logistic Regression and an anomaly detector, Isolation Forest, with a precision of 59%. These strong results indicate the potential of machine learning for predicting machine breakdowns to anticipate machine failures and enable engineers to take proactive maintenance action.
Keywords: TrueBeam, Predictive maintenance, log files.
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Full - text thesis