Symmetry and asymmetry appear together in aero-engine prognostics and health management. A monitoring system should preserve a common decision structure across operating scenarios, while sensor evidence becomes asymmetric under changing operating conditions and fault modes. This study formulates the National Aeronautics and Space Administration (NASA) Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan benchmark as a four-level health-state assessment problem using remaining useful life (RUL) thresholds of 100, 50 and 15 cycles, and proposes a neural evidential reasoning (ER) framework with decision-structure symmetry. FD001–FD004 share the same health-state space, threshold map, reliability-discounted ER operator and final argmax rule, whereas feature selection, evidence transformation and reliability parameters are estimated independently for each subset. Sliding statistical descriptors and training-fold-only minimum-redundancy maximum-relevance (mRMR) selection retain six compact sensor-derived features. FD001 and FD003 therefore use six inputs; FD002 and FD004 additionally retain the same three operating-setting variables and use nine inputs. Boundary soft labels and auxiliary ordinal/RUL supervision exploit ordered degradation information near adjacent-state boundaries. Evaluation uses engine-disjoint validation, three random seeds, identical subset-specific inputs for every comparator, ensemble/neural/ordinal baselines, component ablation and calibration/error-detection analyses. Across the four subsets, the proposed model achieves the highest mean three-metric average (Avg3), 0.664 (standard deviation 0.007), with Accuracy 0.816 (0.006), Macro-F1 0.582 (0.010) and Balanced Accuracy 0.594 (0.005). It is statistically comparable to histogram gradient boosting, random forest and a plain multilayer perceptron, and significantly exceeds the tested compact-input cumulative ordinal, Feature Transformer and correlation-graph attention controls after Holm correction. Fixed-probe diagnostics identify the five-cycle window as the best overall short-history setting. The mRMR top-6 interface reduces the sensor-feature dimension by 97.1% while retaining 98.2% and 97.0% of full-pool Avg3 on FD002 and FD004, respectively. Unknown mass ranks erroneous predictions above correct predictions on every subset (area under the receiver operating characteristic curve (ROC-AUC) 0.749–0.810), including the highest value on the most heterogeneous FD004 subset. These results support a compact and auditable evidence interface that combines competitive classification with source-wise reliability and uncertainty information.

To address the inefficiencies of traditional numerical simulations and the high cost of experimental validation in the aerodynamic–stealth integrated design of S-shaped inlets for aero-engines, this study proposes a novel parameter prediction model based on a fuzzy C-means (FCM) clustering and nonlinear dynamic adaptive particle swarm optimization-enhanced radial basis function neural network (NDAPSO-RBFNN). The FCM algorithm is applied to reduce the feature dimensionality of aerodynamic parameters and determine the optimal hidden layer structure of the RBF network using clustering validity indices. Meanwhile, the NDAPSO algorithm introduces a three-stage adaptive inertia weight mechanism to balance global exploration and local exploitation effectively. Simulation results demonstrate that the proposed model significantly improves training efficiency and generalization capability. Specifically, the model achieves a root mean square error (RMSE) of $3.81\times {10}^{-8}$ on the training set and $8.26\times {10}^{-8}$ on the test set, demonstrating robust predictive accuracy. Furthermore, $98.3\%$ of the predicted values fall within the $y=x\pm 3\beta$ confidence interval ($\beta =1.2\times {10}^{-7}$). Compared with traditional PSO-RBF models, the number of iterations of NDAPSO-RBF network is lower, the single prediction time of NDAPSO-RBF network is shorter, and the number of calls to the standard deviation of the NDAPSO-RBF network is lower. These results indicate that the proposed model not only provides a reliable and efficient surrogate modeling method for complex inlet flow fields but also offers a promising approach for real-time multi-objective aerodynamic–stealth optimization in aerospace applications.

Aiming at the problem of multiple faults in the electro-hydraulic servo actuator system (EHSA), this paper proposes a novel fault diagnosis method based on a nonsingular terminal sliding mode observer (NTSMO). Three typical faults, namely servo valve stuck, hydraulic cylinder leakage, and open circuit, are modeled and injected into the EHSA based on their fault mechanisms and corresponding mathematical descriptions. The main focus of the study lies in the design of a group of NTSMOs specifically developed for fault diagnosis, supported by a more accurate AMESim model to enhance simulation reliability. Fault decision-making is further achieved through a dedicated observer strategy (DOS) combined with an adaptive threshold mechanism. Theoretical analysis verifies the stability and accuracy of the proposed observer as well as the rationality of the adaptive threshold design. Co-simulation results using AMESim/MATLAB demonstrate that the proposed method exhibits strong applicability and high diagnostic accuracy for the three typical types of faults.