As a popular clustering algorithms, fuzzy c-means (FCM) algorithm has been used in various fields, including fault diagnosis, machine learning. To overcome the sensitivity to outliers problem and the local minimum problem of the fuzzy c-means new algorithm is proposed based on the simulated annealing (SA) algorithm and the genetic algorithm (GA). The combined algorithm utilizes the simulated annealing algorithm due to its local search abilities. Thereby, problems associated with the genetic algorithm, such as its tendency to prematurely select optimal values, can be overcome, and genetic algorithm can be applied in fuzzy clustering analysis. Moreover, the new algorithm can solve other problems associated with the fuzzy clustering algorithm, which include initial clustering center value sensitivity and convergence to a local minimum. Furthermore, the simulation results can be used as classification criteria for identifying several types of bearing faults. Compare with the dimensionless indexes, it shows that the mutual dimensionless indexes are more suitable for clustering algorithms. Finally, the experimental results show that the method adopted in this paper can improve the accuracy of clustering and accurately classify the bearing faults of rotating machinery.

In compound fault diagnosis, the scarcity of samples leads to a low fault diagnosis rate. Existing zero-shot compound fault diagnosis methods lack the ability to simultaneously recognize both seen and unseen fault classes, especially when aligning attributes of unseen faults, where a dimensionality explosion occurs. To counter the deficiencies of traditional zero-shot compound fault diagnosis methods, this article introduces an innovative generalized zero-shot compound fault diagnosis approach. This approach pioneers the use of an attribute transformation strategy, establishing a knowledge bridge between single and compound faults through a semantic label definition module, thereby creating a fault attribute set. The fault generation module is then utilized to ingeniously convert the attribute set into training samples that are rich in fault characteristics. These samples are employed to train a multilabel classification module, enabling the model to identify compound faults, including those of unseen classes. Experiments conducted on two real-world bearing datasets have validated the effectiveness and strong generalization capabilities of this method, offering a novel solution and technical support for current zero-shot rotating machinery fault diagnosis.

The existing deep learning models can achieve a high level of fault diagnosis accuracy in the case of a large number of samples. However, in actual production, data is often limited due to the difficulty of data collection and labeling. For small sample fault diagnosis, a fault diagnosis method called diffusion model-overlapping-patch vision transformer (DM-OVT) is proposed in this paper. The method adds coordinate attention to the DM, so that it can consider both channel information and spatial information. In the patch embedding part of Vision Transformer, features are first extracted using convolutional layers, and then overlapping patch divisions are used to improve the correlation between each patch. To be specific, DM-OVT first uses short-time Fourier transform to convert the one-dimensional signals into the time–frequency maps. And then inputs them into the DM to generate different classes of fault data according to labels. Finally, OVT is used to classify the expanded data. The effectiveness of the proposed method was tested on data sets from laboratory multistage centrifugal fans and Case Western Reserve University, and the highest accuracy was achieved in the comparison experiments.

Deep learning (DL) is widely used in ship detection, but there are still some problems in the effective classification, such as inaccurate object feature extraction and inconspicuous feature information in deep layers. To address these problems, we propose a YOLOv7-residual convolutional block attention module (YOLOv7-RCBAM) by combining the convolutional attention mechanism and residual connections to the YOLOv7. First, to accelerate the training speed, the parameters in the backbone network of the pretrained model are frozen by using transfer learning, and the model is fine-tuned for training. Second, to enhance the information relevance of channel dimensional features, an attention mechanism with residual connectivity is adopted. Finally, a feature fusion attention mechanism is introduced to improve the effective feature extraction. The effectiveness of the proposed method is fully validated on the SeaShips dataset. The results show that the YOLOv7-RCBAM model achieves better performance with a 97.59% in and effectively extracts object feature in deep layers. Meanwhile, the YOLOv7-RCBAM model can accurately locate ship in complex environments with darkness and noise with the reaching 96.13% to achieve effective ship classification detection.