As a typical data-driven fault detection approach, the moving window kernel principal component analysis (MWKPCA) method has attracted attention for fault detection of turbofan engines considering the presence of component degradation, but the conventional MWKPCA method uses a fixed step to update the KPCA model periodically until anomaly data is detected, this will increase the amount of calculation. To address this weakness, a modified MWKPCA method is proposed based on an adaptive updating mechanism for the KPCA model in this study. To realize the capability of updating KPCA model adaptively, k-means clustering method is utilized to divide a certain amount of newly acquired sampling data and the same amount of oldest data in the current time window into two categories, and calculates the Mahalanobis distance of the two clustering centers. Then the distance is compared with a prescribed threshold to determine whether to update the KPCA model. The proposed method is applied to an illustrative case, and the fault detection results under normal condition and sensor faults condition show that compared with the conventional MWKPCA algorithm, the modified MWKPCA method does not weaken the ability of fault detection and has better performances in terms of computation efficiency than the conventional MWKPCA algorithm with a fixed moving step.

This paper is concerned with designing a networked controller for a mixed flow two‐spool turbofan aeroengine with aging and deterioration. Firstly, the state‐space representation of the aeroengine considering aging and deterioration is identified, by which the engine system with aging and deterioration is modeled as an uncertain linear system. Then based on this uncertain linear system, theoretical results from the networked control systems and the regional pole assignment are introduced to formulate the networked engine control design in the form of linear matrix inequalities (LMIs). By solving these LMIs simultaneously, a networked engine controller is obtained which guarantees both the robustness against delay/dropout and the satisfactory dynamic performance. Finally, the proposed method is applied to an aerothermodynamic component‐level engine simulator to demonstrate its validity and applicability. The corresponding delay/dropout margin is also calculated, which provides reference for the future development of the distributed engine control system.

Predicting the remaining useful life (RUL) of aircraft engines is crucial for ensuring flight safety, optimizing maintenance, and reducing operational costs. This paper introduces a novel hybrid deep learning model, Transformer–KAN–BiLSTM, for aero-engine RUL prediction. The model is designed to leverage the complementary strengths of its components: the Transformer architecture effectively captures long-range temporal dependencies in sensor data, the emerging Kolmogorov–Arnold Network (KAN) provides superior approximation flexibility and a unique degree of interpretability through its spline-based activation functions, and the Bidirectional LSTM (BiLSTM) extracts nuanced local temporal patterns. Evaluated on the benchmark NASA C-MAPSS dataset, the proposed fusion framework demonstrates exceptional performance, achieving remarkably low Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values that significantly surpass existing benchmarks. These results validate the model’s robustness and its high potential for practical deployment in prognostics and health management systems.

Based on the thermohydraulic calculation model verified in this study and Non-dominated Sorted Genetic Algorithm-II (NSGA-II), a multi-objective configuration optimization method is proposed, and the performances of shell-and-tube heat exchanger with disc-and-doughnut baffles (STHX-DDB) and shell-and-tube heat exchanger with segmental baffles (STHX-SB) are compared after optimization. The results show that, except in the high range of heat transfer capacity of 16.5–17 kW, the thermohydraulic performance of STHX-DDB is better. Tube bundle diameter, inside tube bundle diameter, number of baffles of STHX-DDB and tube bundle diameter, baffle cut, number of baffles of STHX-SB are chosen as design parameters, and heat transfer capacity maximization and shell-side pressure drop minimization are considered as common optimization objectives. Three optimal configurations are obtained for STHX-DDB and another three are obtained for STHX-SB. The optimal results show that all the six selected optimal configurations are better than the original configurations. For STHX-DDB and STHX-SB, compared with the original configurations, the heat transfer capacity of optimal configurations increases by 6.26% on average and 5.16%, respectively, while the shell-side pressure drop decreases by 44.33% and 19.16% on average, respectively. It indicates that the optimization method is valid and feasible and can provide a significant reference for shell-and-tube heat exchanger design.

In order to calculate heat transfer capacity and air-side pressure drop of an annular radiator (AR), one performance calculation method was proposed combining heat transfer unit (HTU) simulation and plate-and-fin heat exchanger (PFHX) performance calculation formulas. This method can obtain performance data with no need for meshing AR as a whole, which can be convenient and time-saving, as grid number is reduced in this way. It demonstrates the feasibility of this performance calculation method for engineering applications. In addition, based on the performance calculation method, one configuration optimization method for AR using nondominated sorted genetic algorithm-II (NSGA-II) was also proposed. Fin height (FH) and number of fins in circumferential direction (NFCD) were optimized to maximize heat transfer capacity and minimize air-side pressure drop. Three optimal configurations were obtained from the Pareto optimal points. The heat transfer capacity of the optimal configurations increased by 22.65% on average compared with the original configuration, while the air-side pressure drop decreased by 33.99% on average. It indicates that this configuration optimization method is valid and can provide a significant guidance for AR design.