The performance of Remaining Useful Life (RUL) prediction models is often limited by data scarcity, especially in safety-critical systems like aircraft engines where failure data is rare. To address this challenge, we propose the Super-SpaceTime GAN, a framework for generating synthetic condition monitoring (CM) data to enhance RUL predictions. The framework incorporates dual-conditioning on operating conditions (OCs) and RUL labels, an autoencoder-based latent space for denoising, and a supervised loss function to align synthetic data with real degradation trajectories. Evaluated on the CMAPSS FD004 dataset, the SuperSpaceTime GAN generates synthetic data that closely mimic real distributions, as verified using JensenShannon Distance, Principal Component Analysis, t-distributed Stochastic Neighbor Embedding, and a novel autoencoder-based health monitoring metric. The framework demonstrates improvement in prognostic performance in limited training data scenarios, with gains persisting even in data-rich settings. These findings highlight the potential of the Super-SpaceTime GAN to improve RUL predictions by addressing data scarcity, making it a valuable tool for Prognostics and Health Management (PHM).

Research aimed at improving engine maintenance practices is vital to ensuring the aviation industry's high safety standards. Both preventive and corrective maintenance approaches result in either higher costs or increased failures. Predictive maintenance aims to achieve an optimal balance by using time series and historical failure data to anticipate future problems and plan engine maintenance activities. While extensive research has been done on the application of AI models for predictive maintenance using simulated datasets, limited research has been done using real world engine data. Simulated datasets lack the complexity of real world systems which undermines the applicability of their findings to real world situations. This paper addresses the gap between theory and industry by utilizing eight years of operating and maintenance data from the GEnx-1B, provided by KLM Engine Services. We expand classic residual based methods with a novel approach, sister engine analysis, to improve the performance of an LSTM-based fault detection model. The data is labeled based on maintenance reports, which provide the most accurate link to the underlying degradation state of an engine. These reports reveal a significant bias toward high pressure turbine degradation in the GEnx-1B. Furthermore, our findings indicate that incorporating sister engine analysis enhances the average F1 score of an AI-based fault detection model by 7.7% and that AI methods outperform the current industry standard when provided with the same input data. The use of physically relevant model features and LIME analysis ensures the model's behavior is interpretable. These results are significant as they help build trust in AI-based solutions for predictive maintenance, which is crucial for their broader adoption.

Inherent subjectivity, inefficiencies, and the substantial cost related to human-based visual inspection of high-pressure turbine (HPT) blades has driven research in alternative automated techniques. The
combination of computer vision (CV) and deep learning (DL) provides a compelling alternative. However, as DL models increase in capability, their large parameter spaces require substantial data to
achieve robust training. Therefore, this study explores the use of two Auxiliary Classifier Generative Adversarial Networks (AC-GANs) to augment proprietary datasets for two failure modes: (a) a 3-
channel red-green-blue (RGB) model for obstructed holes, and (b) a 4-channel RGB plus depth (RGBD) model for foreign object damage, with the depth reconstructed using monocular depth estimation. A Differential Evolution Optimizer (DEO) was used for hyperparameter optimization of a ResNet-18 classification target model. In the obstructed hole dataset, GAN-based augmentation showed significantly improved accuracy, recall, F1, and AUC-ROC (p < 0.05), competing with traditional methods using less augmentation. In the foreign object damage dataset, the inclusion of depth information significantly enhanced accuracy, precision, F1, and AUC-ROC performance (p < 0.05). However, the RGBD augmentation mainly resulted in a trade-off between precision and recall, without statistically significant differences (p > 0.05).

For the remaining useful life (RUL) prediction of bearings across varying operating conditions, transfer learning models have demonstrated high accuracy. To make use of the maximum amount of bearing degradation information, multi-source domain adaptation models have been developed to enable predictions across operating conditions and bearing types. These models typically make use of vibration data in the time, frequency, and time-frequency domains. However, in practice, some systems only collect data in the frequency domain, such that prediction models developed for these systems lack access to degradation features from the time or time-frequency domains. To address this, this study proposes a multi-source domain adaptation (MSDA) model for bearing RUL prediction across operating conditions and bearing types using only frequency domain data as input data. The model employs a common feature extractor to capture domain invariant features, and domain specific regressors for RUL prediction. Domain adaptation is done by minimising the maximum mean discrepancy between features from the source and target domains, while the domain specific RUL predictions are aligned by minimising the distance between the RUL predictions. Experimental results across three scenarios demonstrate that the MSDA model achieves accurate RUL predictions. Even with only frequency domain data, the RMSE and Score are comparable to those of other advanced algorithms that use both frequency and time domain data.

During operation aircraft brakes degrade due to wear. This degradation can be continuously monitored using brake degradation sensors. Using this monitored degradation data the remaining useful life of the brakes can be estimated by means of a prognostic model based on a Gamma probability distribution. Using the remaining useful life estimations a maintenance schedule can be optimized for a fleet of aircraft each fitted with multiple brakes accounting for maintenance constraints. Considered maintenance constraints include the availability of the maintenance hangar and the aircraft flight schedules. Because the problem contains multi-unit systems opportunistic maintenance can be applied, specifically economic dependance between components is considered in the optimization. Using a rolling horizon approach a long-term maintenance schedule can be created and evaluated. This long-term schedule is simulated using Monte Carlo simulation to achieve robust results on which meaningful conclusions can be drawn. To compare the results of the condition-based maintenance strategy a time-based maintenance strategy with fixed replacement intervals is used. It has been shown that the application of a condition-based maintenance strategy which makes use of prognostics can significantly improve the scheduling performance.