Using different machine learning approaches to evaluate performance on spare parts request for aircraft engines

Abstract

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.

Applications of Machine Learning in Aircraft Maintenance

“…Capodieci et al [26] employed various machine learning algorithms to evaluate spare parts request performance for aircraft engine by developing iterative Artificial Intelligence-based algorithms to outline the plan for engine removal and maintenance, enhance maintenance costs and engine availability at the customer, and also obtain an acquisition plan of integrated parts with intervention planning and maintenance scheme execution. Machine Learning was used on a workshop dataset to optimize the amount, cost, and lead-time of warehouse spare parts in order to achieve this goal.…”

“…It's a classification method built on Bayes' Theorem [26]. Naive Bayes classifier postulates that a feature existing in a class has no bearing on the presence of any other features.…”

“…In this analytics approach, dependent variables are either finite or in categories i.e., either a binary regression (A or B) or a multiple/multinomial regression (A, B, C, or D). Logistic regression helps to understand the connection between a dependent variable and one or more independent variables by estimating probabilities [26]. Analysis of multiple variables with this tools helps to reduce confounding factor in variables [48].…”

“…Random forest, developed by Breiman et al [52] employs an ensemble learning method for solving complex problems by merging multiple classifiers [26,37]. RF algorithm consist of many decision trees which can be trained using bagging or bootstrap aggregation.…”

“…The replacement of aircraft components is one of the most significant tasks performed by the aviation maintenance staff [22]. Instead of doing scheduled maintenance at predetermined intervals, replacement may be necessary based on the state of the aircraft component to maximize the lifetime of the aircraft equipment [25,26]. An intelligent analysis of the data collected from sensors can be used to gain information on the state of various aircraft components [30].…”