Aircraft predictive maintenance modeling using a hybrid imbalance learning approach

The continued development of the industrial internet of things (IIoT) has caused an increase in the availability of industrial datasets. The massive availability of assets operational dataset has prompted more research interest in the area of condition-based maintenance, towards the API-lead integration for assets predictive maintenance modelling. The large data generated by industrial processes inherently comes along with different analytical challenges. Data imbalance is one of such problems that exist in datasets. It affects the performance of machine learning algorithms, which yields imprecise prediction. In this paper, we propose an advanced approach to handling imbalance classification problems in equipment heterogeneous datasets. The technique is based on a hybrid of soft mixed Gaussian processes with the EM method to improves the prediction of the minority class during learning. The algorithm is then used to develop a prognostic model for predicting aircraft component replacement. We validate the feasibility and effectiveness of our approach using real-time aircraft operation and maintenance datasets. The dataset spans over seven years. Our approach shows better performance compared to other similar methods.