SVMs (support vector machines) is a new artificial intelligence methodology derived from Vapnik's statistical learning theory, which has better generalization than artificial neural network. A C-support vector classifiers Based Fault Diagnostic Model (CBFDM) which gives the 3 most possible fault causes is constructed in this paper. Fivefold cross validation is chosen as the method of model selection for CBFDM. The simulated data are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of CBFDM is over 93% even when the standard deviation of noise is 3 times larger than the normal. This model can also be used for other diagnostic problems.

To address the limitations of CNNs and RNNs in handling complex operating conditions, multi-scale degradation patterns, and long-term dependencies—with attention mechanisms often failing to highlight key degradation features—this paper proposes a remaining useful life (RUL) prediction framework based on a multi-scale dilated fusion attention (MDFA) module. The MDFA leverages parallel dilated convolutions with varying dilation rates to expand receptive fields, while a global-pooling branch captures sequence-level degradation trends. Additionally, integrated channel and spatial attention mechanisms enhance the model’s ability to emphasize informative features and suppress noise, thereby improving overall prediction robustness. The proposed method is evaluated on NASA’s C-MAPSS and N-CMAPSS datasets, achieving MAE values of 0.018–0.026, RMSE values of 0.021–0.032, and R² scores above 0.987, demonstrating superior accuracy and stability compared to existing baselines. Furthermore, to verify generalization across domains, experiments on the PHM2012 bearing dataset show similar performance (MAE: 0.023–0.026, RMSE: 0.031–0.032, R²: 0.987–0.995), confirming the model’s effectiveness under diverse operating conditions and its adaptability to different degradation behaviors. This study provides a practical and interpretable deep-learning solution for RUL prediction, with broad applicability to aero-engine prognostics and other industrial health-monitoring tasks.

The 5G frequency band is extremely close to the operating frequency band of radio altimeters (RAs), so an in-depth study of the possible interference of 5G’s key parameters on RAs is especially necessary to ensure aviation safety. In this paper, the interference magnitude of 5G waveforms on an altimeter was measured by simulating the Adjacent Channel Leakage Power Ratio (ACLR) values for different sub-carrier spacing (SCS) and channel bandwidth configurations. Furthermore, interference injection experiments on simulated 5G signals and the interference thresholds of a frequency-modulated continuous wave (FMCW) altimeter were compared to experiments on the effects of the different configurations of 5G SCSs, channel bandwidths, and center frequency points. The interference thresholds of this FMCW altimeter were found to be in the range of 1 dBm to 6 dBm and −4 dBm to 0 dBm under the interference of 5G signals at the center frequency points of 3.7 GHz and 3.9 GHz. These results provided a certain reference for the engineering judgement margin of the interference thresholds.

In this manuscript, we present a novel deployment protection method aimed at safeguarding aeronautical radio altimeters (RAs) from interference caused by fifth-generation (5G) telecommunication base stations (BSs). Our methodology involves an integrated interference model for defining prohibited zones and utilizes power control and angle shutoff methods to mitigate interference. First, to ensure reliable protection, we define both horizontal and vertical prohibited zones and investigate their variations to immunize RA against 5G interference. Second, we validate the effectiveness of the model in various operational scenarios, analyzing the influence of factors such as base station types, antenna parameters, flight altitude, and aircraft attitudes to cover a wide range of real-world scenarios. Third, to mitigate interference, we propose and analyze the power control and angle shutoff methods through simulation for the RMa prohibited zone. Our results demonstrate the efficacy of the deployment protection method in safeguarding RAs from 5G interference, providing guidance for interference protection during civil aviation operations and base station deployment near airports.

The failure mode and effect analysis (FMEA) method, which estimates the risk levels of systems or components solely based on the multiplication of simple risk rating indices, faces several limitations. These include the risk of inaccurate risk level judgment and the potential for misjudgments due to human factors, both of which pose significant threats to the safe operation of aircraft. Therefore, a Probabilistic Language based on a cumulative prospect theory (Probabilistic Language, PL) risk assessment strategy was proposed, combining the technique for order preference with similarity to an ideal solution (TOPSIS). The probabilistic language term value and probability value were fused in the method through the cumulative prospect theory, and a new PL measure function was introduced. The comprehensive weights of evaluation strategies were determined by calculating the relevant weights of various indicators through the subjective expert weight and objective entropy weight synthesis. So, a weighted decision matrix was constructed to determine the ranking order close to the ideal scheme. Finally, the risk level of each failure mode was ranked according to its close degree to the ideal situation. Through case validation, the consistency of risk ranking was improved by 23.95% compared to the traditional FMEA method. The rationality of weight allocation was increased by 18.2%. Robustness was also enhanced to some extent. Compared with the traditional FMEA method, the proposed method has better rationality, application, and effectiveness. It can provide technical support for formulating a new generation of airworthiness documents for the risk level assessment of civil aircraft and its subsystem components.

This study addresses threat scenario identification and quantitative evaluation in the context of the information security risk assessment process for airborne networks. A method integrating an improved system-theoretic process analysis (STPA) and the technique for order preference by similarity to an ideal solution (TOPSIS) is proposed. A safety control interaction structure is first established based on the system-level loss definition to identify unsafe control actions and derive STPA-Sec threat conditions. Then, the opinion aggregation method based on the weakest t-norm is introduced to address the uncertainty from insufficient expert consensus and opinion deviation. To address the intrinsic correlations among evaluation indicators, the criteria importance through intercriteria correlation (CRITIC) method is applied to determine indicator weights. These weights are subsequently incorporated into the TOPSIS framework to assess the identified threat scenarios. Comparative analysis confirms the effectiveness of the proposed approach. The results show that the improved STPA-TOPSIS method enables the reliable identification of security threats in airborne networks and supports the prioritization of threat scenarios by severity, facilitating the implementation of targeted mitigation strategies.