In general, deep learning-based fault diagnosis methods need a large number of training samples, which are often not available in real applications. Aiming at this problem, this article develops a new data augmentation method, i.e., randomized wavelet expansion (RWE), to generate a set of synthesis samples that share similar characteristics with the original sample. The first key point is that the amplitudes of wavelet coefficients at a randomly selected frequency band are enlarged through random expansion. Another key point is that the synthesis samples are processed to have the same mean values and standard deviations as their corresponding original sample. Afterward, the synthesis samples are used as the training dataset to train a deep convolutional neural network (CNN) for implementing the few-shot fault diagnosis of aviation hydraulic pumps. Finally, the performance has been validated through a series of experiments.

Aiming at the problem that the sequence of operation in the processing of complex parts directly affects the processing cost, an improved harmony search algorithm with the lowest processing cost as the optimization goal is proposed. The constraint relationship between the operation is represented by a process priority diagram. Then use the topological sorting method on the graph to generate the initial sequence as the initial harmony of the harmony memory. The crossover operator is introduced to ensure the feasibility of the new generation of harmony, and the local search method is used to change the machine and tool resources available for the operation to avoid falling into the local optimum. Experimental results show that the improved harmony search method can effectively solve the cost optimization problem of step sequencing.

For unsupervised anomaly detection in aircraft engines, traditional methods predominantly focus on mining temporal features, often neglecting complex interdependencies among multiple variables. Although existing graph neural networks can model spatial relationships, their purely data-driven graph construction tends to overlook system thermodynamic mechanisms, thereby introducing spurious correlations. To address this, this paper proposes a Physics-Guided Spatio-Temporal Attention Graph Network (PG-STAG). This method fuses aerothermodynamic priors with health-data correlation information to construct a knowledge-data collaborative graph topology with physical consistency. Furthermore, a dual spatio-temporal attention mechanism is utilized to extract multi-dimensional spatio-temporal features, and anomaly identification is achieved based on reconstruction errors. Experiments on the N-CMAPSS dataset and real-world civil aviation QAR data demonstrate that PG-STAG achieves the highest F1-score and ROC-AUC among the compared methods, exhibiting strong robustness and physical interpretability.