The goal is to develop a software able to support the operator in the ordinary maintenance operations by automatically identifying the engines with an higher risk and therefore higher priority.
In order to classify the risk category of the device we use the available data from the analysis of chemical and physical properties of the oil performed on samples taken at different stages of the engine life. The main difficulty is due to an incomplete and inconsistent data history.
We developed a classification algorithm on Support Vector Machine (SVM). To handle the missing data we built an ad-hoc modification of the SVM kernel was used to handle the missing data in order to maximize the separation between classes.
Results and Benefits
Our classifier is able to correctly recognize the risk category of the engine with more than 85% accuracy. In addition, the model is interpretable, thus allowing the identification of the chemical and physical parameters considered most relevant for classification.