In the process of aircraft maintenance and support, a large amount of fault description text data is recorded. However, most of the existing fault diagnosis models are based on structured data, which means they are not suitable for unstructured data such as text. Therefore, a text-driven aircraft fault diagnosis model is proposed in this paper based on Word to Vector (Word2vec) and prior-knowledge Convolutional Neural Network (CNN). The fault text first enters Word2vec to perform text feature extraction, and the extracted text feature vectors are then input into the proposed prior-knowledge CNN to train the fault classifier. The prior-knowledge CNN introduces expert fault knowledge through Cloud Similarity Measurement (CSM) to improve the performance of the fault classifier. Validation experiments on five-year maintenance log data of a civil aircraft were carried out to successfully verify the effectiveness of the proposed model.

This study proposes an improved artificial bee colony algorithm (IABC) for maintaining a balance between the exploratory and exploitative abilities of the algorithm. Therefore, unmodeled dynamic characteristics can be effectively estimated, which can improve the estimation accuracy of the system and create conditions for later system modeling and control. The proposed IABC algorithm uses two new search expressions to generate new alternative solutions and the global optimal solution is considered in the search process. Exploiting the simple structure and fast global convergence of artificial bee colony (ABC) algorithm, an improved clustering algorithm combining IABC and Kernel fuzzy c-means (KFCM) iteration is also proposed. KFCM algorithm is sensitive to the initial clustering center and can easily fall into local optimum; the improved clustering algorithm solves this issue. Compared with other algorithms, IABC algorithm can converge faster and more accurately for a given parameter structure and number of cycles. Three sets of benchmark test functions and six sets of UCI standard datasets were used in the simulation experiments conducted. Experimental results show that KFCM-IABC can aggregate datasets faster than GFCM-IABC because of the use of IABC. The proposed algorithm improves class validity index by 1% to 4%, thereby exhibiting the advantages of strong robustness and high clustering accuracy.

The purpose of this paper is to predict failures based on textual sequence data. The current failure prediction is mainly based on structured data. However, there are many unstructured data in aircraft maintenance. The failure mentioned here refers to failure types, such as transmitter failure and signal failure, which are classified by the clustering algorithm based on the failure text. For the failure text, this paper uses the natural language processing technology. Firstly, segmentation and the removal of stop words for Chinese failure text data is performed. The study applies the word2vec moving distance model to obtain the failure occurrence sequence for failure texts collected in a fixed period of time. According to the distance, a clustering algorithm is used to obtain a typical number of fault types. Secondly, the failure occurrence sequence is mined using sequence mining algorithms, such as-PrefixSpan. Finally, the above failure sequence is used to train the Bayesian failure network model. The final experimental results show that the Bayesian failure network has higher accuracy for failure prediction.

To address the challenges of low diagnostic accuracy and poor interpretability in fault diagnosis under variable conditions with small samples from multiple sensors, this paper proposes a novel multi-sensor fusion attention residual network. First, multi-directional sensor signals are fused using principal component analysis, and 28 time-frequency modal features are innovatively extracted to capture subtle fault signatures across diverse operating conditions. The 1D convolutional neural network is enhanced with residual blocks and a multi-channel attention mechanism, effectively mitigating gradient vanishing and overfitting while emphasizing discriminative features. To overcome the ‘black-box’ nature of deep learning models, the SHapley Additive exPlanations method is incorporated to quantify the contribution of each feature, enabling visualization of the model’s decision logic and identification of critical fault indicators. Experimental results demonstrate that the proposed method achieves superior diagnostic accuracy and generalization performance on small-sample, variable-condition datasets, while offering improved interpretability. These advantages make the approach well-suited for practical bearing condition monitoring and maintenance decision-making.

The paper proposes a mixed integer programming model based on rough set to evaluate the students’ grade with the interactive teaching records. Firstly, data preprocessing is performed by multiple imputation and correlation analysis using principal component analysis (PCA) to dispose data. Secondly, a mixed integer programming model based on rough set is established. Then this paper makes the comparison with the K-means clustering analysis method based on rough set. The empirical research result shows proposed model is feasible.