To address the critical challenges of submerged weak fault signatures and extreme operational variability in aerospace bearing diagnosis, a GADF-powered dual-branch network with KAN-Swin Transformer is proposed for fault diagnosis of aerospace bearing in this study. Firstly, the one-dimensional vibration signal is encoded into a two-dimensional time-frequency image through gramian angular difference field (GADF), employing polar coordinate mapping to preserve temporal dependencies and spectral dynamic characteristics while addressing the noise sensitivity limitations of conventional time-frequency analysis methods. Subsequently, a KAN-Swin Transformer module is developed by replacing traditional multilayer perceptron with B-spline basis functions, which enhances nonlinear mapping capability through dynamic grid adjustment strategy, effectively reducing parameter complexity while improving modeling of transient impacts and periodic patterns. Furthermore, a dual-branch parallel architecture is proposed: The KAN-Swin Transformer branch extracts local structural features through hierarchical window attention mechanisms, while the CNN-GAM branch strengthens global texture perception via multi-scale convolution integrated with channel-spatial attention fusion. Finally, cross-modal feature concatenation is combined with adaptive pooling to achieve synergistic optimization of global-local characteristics, significantly enhancing fault pattern discriminability under complex noise environments. The developed method tested on two different sets of aerospace bearing data, has achieved a classification accuracy of 100%. Meanwhile, the collaborative effect of module integration including KAN, Swin Transformer and CNN-GAM is validated through ablation experiments, showing enhanced cross-speed operational recognition rates compared to baseline models and confirming the robustness of the framework against noise and variable loading conditions. By synergistically integrating mechanism fusion and attention architectures, the proposed framework provides a reliable solution for intelligent health monitoring of aerospace bearings.

The data-driven time–frequency analysis (TFA) method has garnered widespread attention due to its robust feature learning and representation capabilities. However, existing methods still require further development in characterizing nonstationary signals with closely-spaced and crossing frequencies generated from wind turbines , and realizing mechanical fault detection. To this end, a novel method, termed time–frequency self-similarity enhancement network (TFSSEN), is proposed. First, an adaptive time–frequency characterizing module (ATFCM), consisting of the time–frequency convolutional layer and adaptive convolutional pooling unit, is designed to represent random scale vibration signals to an appropriate scale time–frequency representation (TFR). Second, a non-local and global attention residual group (NGARG) is constructed, where a single-scale self-similarity exploitation module is introduced to calculate feature correlations within single-scale TFR, and an improved-global context attention mechanism is developed to explore the most informative components in multi-scale time–frequency features, thereby achieving precise feature reconstruction. Finally, the self-similarity mixed-scale time–frequency enhancement module (SMTEM) is constructed by multiple cascaded NGARGs, and it can extract frequency information from similar time–frequency features and gradually enhance energy concentration. Simulation results show that the TFSSEN can effectively characterize nonstationary signal with closely-spaced and crossing frequencies. The comprehensive experiment analysis on the wind turbine planetary gearbox and bearings further demonstrates that the TFSSEN exhibits superior performance for characterizing nonstationary fault characteristic frequencies (FCFs) compared with advanced TFA methods.

The remaining useful life (RUL) prediction of rolling element bearings is usually subject to the following limitations. First, it is difficult to obtain the massive performance degradation data, which resulting in the insufficient learning of the historical degradation law. Second, the parameters in most of existing models depend heavily on the manual selection, which leads to the poor generalization performance. To address these problems, a novel adaptive sparse graph learning (ASGL) method based on digital twin dictionary (DTD) is proposed in this article. To facilitate the prediction when the data are insufficient, the extended exponential models and the extended linear piecewise models are first established, then a DTD that covers the various degradation behaviors is constructed. Besides, a new objective function of graph learning is designed and the sparse regularization method is introduced to adaptively obtain the topology graph of data. Therefore, the method avoids the wrong adjacency relationship caused by inappropriate parameters. The simulation and experimental results show that the DTD has higher prediction accuracy than the experimental samples, and the ASGL method is easy to implement and has lower dependence on the parameter selections. In addition, compared with some state-of-the-art methods, it can obtain better RUL prediction results.

Deep-learning-based intelligent diagnosis is a popular method to ensure the safe operation of rolling bearings. However, practical diagnostic tasks are often subject to a lack of labeled data, resulting in poor performance in scenarios with insufficient training samples. Moreover, conventional intelligent diagnosis methods suffer from a deficiency in interpretability. In this article, an auto-embedding transformer (AET) method is proposed to implement the interpretable few-shot fault diagnosis of rolling bearings. First, an auto-embedding module is developed to improve the embedding quality of the signal, which is designed based on a novel asymmetric convolutional encoder–decoder architecture. This module can leverage the merits of unsupervised learning in data mining and allow the transformer to learn more diagnostic knowledge from limited data. Second, an attention scoring method is proposed that utilizes positionwise attention to quantify the importance of each signal embedding for diagnosis, thereby interpreting the AET method. Experimental results confirm that, even with limited training samples, the AET method outperforms various comparison methods in terms of recognition accuracy and convergence rate. Furthermore, the attention scores assigned to each embedding facilitate the interpretability of the AET method.

Rotating machinery fault diagnosis under nonstationary conditions still mainly relies on manual analysis of the frequency spectrums or the time-frequency representations of vibration signals. However, not only do the results of those methods depend on heavily the expert experience, but for some samples, the intricate interference frequency components caused by the complex modulation characteristics and the operation conditions also make it difficult to identify the frequency content. In this article, a novel intelligent fault diagnosis method for rolling bearings under nonstationary conditions is investigated. A new flexible generalized demodulation method is first proposed. Different from traditional demodulation methods, it can map the interest time-varying frequencies of extensive samples under different speed conditions to the defined base frequency as well as its multiples, and thus overcomes the effects of operation conditions on demodulation spectrums. Based on the demodulation method, a fault feature extraction method is further proposed to capture the useful fault information in the spectrums. Experiments validate that, for some samples, the health conditions cannot be manually identified by the demodulation spectrums due to the interference frequency components, but can be recognized by the proposed method automatically, and because of the definite physical meaning, the method is more adaptive to new operation conditions.

This paper addresses the issues related to the design of robust controller using genetic algorithms (GA) for lightweight, one-link flexible manipulators working under dynamic environments and other uncertain influences. By selecting sensitivity weight functions properly using the GA method, a mixed sensitivity Hinfincontroller is developed to ensure robustness of manipulator control systems for varying payloads and other modeling uncertainties. Numeric simulation has been conducted and the results have demonstrated the effectiveness of the proposed method