Using Different Machine Learning Approaches to Evaluate Performance on Spare Parts Request for Aircraft Engines

The Aircraft uptime is getting increasingly important as the transport solutions become more complex and the transport industry seeks new ways of being competitive. To reach this objective, traditional Fleet Management systems are gradually extended with new features to improve reliability and then provide better maintenance planning. Main goal of this work is the development of iterative algorithms based on Artificial Intelligence to define the engine removal plan and its maintenance work, optimizing engine availability at the customer and maintenance costs, as well as obtaining a procurement plan of integrated parts with planning of interventions and implementation of a maintenance strategy. In order to reach this goal, Machine Learning has been applied on a workshop dataset with the aim to optimize warehouse spare parts number, costs and lead-time. This dataset consists of the repair history of a specific engine type, from several years and several fleets, and contains information like repair claim, engine working time, forensic evidences and general information about processed spare parts. Using these data as input, several Machine Learning models have been built in order to predict the repair state of each spare part for a better warehouse handling. A multi-label classification approach has been used in order to build and train, for each spare part, a Machine Learning model that predicts the part repair state as a multiclass classifier does. Mainly, each classifier is requested to predict the repair state (classified as “Efficient”, “Repaired” or “Replaced”) of the corresponding part, starting from two variables: the repairing claim and the engine working time. Then, global results have been evaluated using the Confusion Matrix, from which Accuracy, Precision, Recall and F1-Score metrics are retrieved, in order to analyse the cost of incorrect prediction. These metrics are calculated for each spare part related model on test sets and, then, a final single performance value is obtained by averaging results. In this way, three Machine Learning models (Naïve Bayes, Logistic Regression and Random Forest classifiers) are applied and results are compared. Naïve Bayes and Logistic Regression, that are fully probabilistic methods, have best global performances with an accuracy value of almost 80%, making the models being correct most of the times.