Predictive maintenance for aircraft cost reduction using machine learning

Abstract

Predictive maintenance has become an important strategy in the aviation industry for improving aircraft reliability, safety, and operational efficiency. With the increasing availability of sensor data from aircraft engines, Machine Learning (ML) techniques can be used to detect degradation patterns and predict failures before they occur. However, many previous studies focus on isolated tasks such as fault detection or Remaining Useful Life (RUL) prediction and often rely on complex Deep Learning (DL) models without efficient feature selection, leading to high computational cost and limited interpretability. The study proposes a hybrid predictive maintenance framework that combines Temporal Convolutional Networks (TCN) for temporal feature extraction with Genetic Algorithms (GA) to select optimal features and Variational Autoencoders (VAE) for detecting anomalies and Transformer-based models to classify failures and predict RUL. The model is evaluated using the NASA CMAPSS turbofan engine dataset. Experimental results indicate that the proposed method is able to achieve a high classification accuracy of 98.71% and reduce costs associated with maintenance by 46.25%, compared to traditional methods of maintaining an aircraft. The results of this study indicate that the proposed hybrid method has the potential to improve maintenance-related decision-making, improve aircraft reliability, and reduce operational cost. Additionally, these results show that there are a significant number of opportunities to deploy intelligent predictive maintenance systems to aviation operations across the globe.