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Implementing condition monitoring and fault diagnosis of aero-engine bearing is crucial for ensuring aircraft operate safely and reliably. In engineering practice, the fault data for aero-engine bearings is extremely limited. However, the traditional fault diagnosis methods have two shortcomings under extremely small sample conditions: (1) they have limited diagnostic performance and generalization ability; (2) they do not mine fault information sufficiently and efficiently. This article proposes a siamese multiscale residual feature fusion network (SMSRFFN) for aero-engine bearings fault diagnosis under small sample conditions to overcome the weaknesses above. In the proposed SMSRFFN, the training samples are first paired in pairs according to the matching rules to realize the expansion of the sample size. Secondly, a multiscale residual feature extraction network (MSRFEN) is constructed to excavate the fault features of different scales and speed up the convergence speed of the network. Then, a multiscale attention mechanism feature fusion module (MSAMFFM) is designed to achieve efficient fusion of fault features at different scales. Finally, the distance of the input sample is measured based on the fused deep feature representation to identify the fault state of the aero-engine bearing. The proposed SMSRFFN is evaluated using three bearing fault data and also compared with some state-of-the-art small sample diagnostic methods. Experimental results demonstrate the effectiveness and superiority of the proposed SMSRFFN in mining fault information and improving diagnosis accuracy under extremely small sample conditions.

Aviation unsafe events often lead to major casualties and property losses. Aviation safety risk intelligent early warning is an important means to ensure the safe and reliable operation of aircraft. Therefore, an intelligent early warning model is urgently needed to quickly predict the risk level and identify potential risks to take targeted measures to realize the active management of safety. To realize the above process, the text mining method is used to extract key risk information from unsafe event reports and input it into the intelligent early warning model to predict its risk level, further constructing the priority processing index to achieve a rapid decision, and finally realize the intelligent safety management process of features extraction to early warning levels identification and then to priority processing. First, domain dictionary and Chinese stop word list are constructed to process the massive text data in the unsafe event’s report. Further, TF-IDF and TextRank are fused to extract key risk information and convert it into feature vectors. Second, the IHT algorithm is used to alleviate the sample class imbalance problem. After that, input the balanced risk information into an improved stacking multi-model fusion algorithm to accurately identify the early warning level and improve the level of active management and control via priority processing index ranking. The effectiveness and feasibility of the proposed method are demonstrated by testing the unsafe event text data of some aircraft maintenance companies and airlines, which promotes the practical application value of text mining technology in the aviation field.

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The exact radar cross-section (RCS) measurement is difficult when the scattering of targets is low. Full polarimetric calibration is one technique that offers the potential for improving the accuracy of RCS measurements. There are numerous polarimetric calibration algorithms. Some complex expressions in these algorithms cannot be easily used in an engineering practice. A radar polarimetric coefficients matrix (RPCM) with a simpler expression is presented for the monostatic radar polarization scattering matrix (PSM) measurement. Using a rhombic dihedral corner reflector and a metallic sphere, the RPCM can be obtained by solving a set of equations, which can be used to find the true PSM for any target. An example for the PSM of a metallic dish shows that the proposed method obviously improves the accuracy of crosspolarized RCS measurements.