The scheduling optimization and task assignment of aero-engine fleets are complex and dynamic, presenting a significant challenge in aviation engineering. This study proposes a Dual-layer Collaborative Optimization Framework (DCOF) to address these challenges. The problem is decomposed into two interrelated sub-problems, scheduling optimization and task assignment, with distinct mathematical models formulated for each. For scheduling optimization, this study proposes an Improved Gravity Particle Swarm Optimization (IGPSO) algorithm. The algorithm enhances global search capability and convergence speed through dynamic weight adjustment and constraint processing repair strategies, effectively handling dynamic variations in engine health and remaining life. For task assignment, an Improved Branch-and-Price (IB&P) method is used. This method combines column generation with branch-and-bound strategies, while integrating heuristic rules and parallel computing techniques to efficiently find optimal solutions under multi-dimensional constraints. By clearly distinguishing between operational and maintenance tasks and considering their interdependencies, the proposed DCOF better captures real operational needs, improving fleet scheduling efficiency and reliability. Experimental validation and engineering simulations confirm the method’s effectiveness, showing advantages in repair balance, task assignment balance, and minimizing engine life waste. The approach enhances both usage efficiency and maintenance management of aero-engine fleets.

The original monitoring data from aero-engines possess characteristics such as high dimensionality, strong noise, and imbalance, which present substantial challenges to traditional anomaly detection methods. In response, this paper proposes a method based on Fuzzy Fusion of variables and Discriminant mapping of features for Clustering (FFD-Clustering) to detect anomalies in original monitoring data from Aircraft Communication Addressing and Reporting System (ACARS). Firstly, associated variables are fuzzily grouped to extract the underlying distribution characteristics and trends from the data. Secondly, a multi-layer contrastive denoising-based feature Fusion Encoding Network (FEN) is designed for each variable group, which can construct representative features for each variable group through eliminating strong noise and complex interrelations between variables. Thirdly, a feature Discriminative Mapping Network (DMN) based on reconstruction difference re-clustering is designed, which can distinguish dissimilar feature vectors when mapping representative features to a unified feature space. Finally, the K-means clustering is used to detect the abnormal feature vectors in the unified feature space. Additionally, the algorithm is capable of reconstructing identified abnormal vectors, thereby locating the abnormal variable groups. The performance of this algorithm was tested on two public datasets and real original monitoring data from four aero-engines’ ACARS, demonstrating its superiority and application potential in aero-engine anomaly detection.

Aeroengines, as the sole power source for aircraft, play a vital role in ensuring flight safety. The gas path, which represents the fundamental pathway for airflow within an aeroengine, directly impacts the aeroengine's performance, fuel efficiency, and safety. Therefore, timely and accurate evaluation of gas path performance is of paramount importance. This paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method, combining Fingerprint and gas path parameter deviation values. Firstly, Fingerprint is used to correct gas path parameter deviation values, eliminating parameter shifts caused by non-component performance degradation. Secondly, coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding window. Thirdly, an Ensemble Empirical Mode Decomposition and Non-Local Means (EEMD-NLM) filtering method is designed to “clean” data noise, completing the preprocessing for gas path parameter deviation values. Afterward, based on the characteristics of gas path parameter deviation values, a Dynamic Temporary Blended Network (DTBN) model is built to extract its temporal features, cascaded with Multi-Layer Perceptron (MLP), and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder (DTB-AutoEncoder). Eventually, by training this improved autoencoder, the aeroengine gas path multi-component performance assessment model is formed, which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values, thereby achieving the performance assessment of engine gas path components. Through practical application cases, the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.