For most rotating mechanical transmission systems, condition monitoring and fault diagnosis of the gearbox are of great significance to avoid accidents and maintain stability in operation. To strengthen the comprehensiveness of feature extraction and improve the utilization rate of fault signals to accurately identify the different operating states of a gearbox, a gearbox fault diagnosis model combining Gramian angular field (GAF) and CSKD-ResNeXt (channel shuffle and kernel decomposed ResNeXt) was proposed. The original one-dimensional vibration signal of the gearbox was converted into a two-dimensional image by GAF transformation, and the image was used as the input of the subsequent diagnosis network. To solve the problem of channel independence and incomplete information caused by group convolution, the idea of channel shuffle is introduced to enable the branches of the group convolution part to establish information exchange. In addition, to improve the semantic expression ability of the model, the convolutional kernel of the network backbone is split and replaced. The model is verified under the different working conditions of the gearbox and compared with other methods. The experimental results show that the diagnostic accuracy of the model is up to 99.75%, and the precise identification of gearbox faults is realized.

As the core equipment in ship power systems, the accurate and real-time diagnosis of ship diesel engine faults directly affects navigation safety and operation efficiency. Existing methods (e.g., expert systems, traditional machine learning) can hardly cope with the complex failure modes and dynamic operation environment due to the problems of relying on artificial features and insufficient generalization ability. In this paper, we propose a BiLSTM-CRF-based knowledge graph construction method for ship diesel engine faults, aiming at integrating multi-source heterogeneous data through deep learning and knowledge graph technology, and mining the deep semantic associations among fault phenomena, causes, and solutions. The research framework covers data acquisition, ontology modeling, and knowledge extraction and storage, and the BiLSTM-CRF model is used to fuse bi-directional contextual features with label transfer probability to achieve high-precision entity recognition and relationship extraction. Finally, a scalable knowledge graph is constructed by Neo4j. Experiments show that the model significantly outperforms baseline methods such as HMM, CRF, and BiLSTM, and the graph visualization clearly presents the fault causality network, which supports knowledge reasoning and decision optimization. For example, “high exhaust temperature” can be related to potential causes such as “turbine failure” and “poor cooling”, and recommended measures can be taken. This method not only improves fault diagnosis accuracy and efficiency but also provides a novel method for intelligent ship health management.

Under different working conditions of gearbox, the feature extraction of fault signals is difficult, and large difference in data distribution affects the fault diagnosis results. Based on the problems, the research proposes a method based on improve...

Aiming at the nonlinear and non-stationarity of gearbox fault signals and the confusion among different fault categories, a gear fault diagnosis method combining variational mode decomposition, reconstruction and ResNeXt is proposed in this paper. In this paper, parameter K of VMD is determined according to the changing trend of sample entropy (SE), K modal components are obtained after decomposition, and the effective modal components are extracted and reconstructed according to Pearson autocorrelation coefficient, so as to remove redundant information from the original signal. Then the reconstructed signal is transformed by time–frequency and output two-dimensional time–frequency information, which is used as the input of ResNeXt model to extract the characteristics of different faults. Moreover, the model performance is improved by changing the learning rate decline rate, and a fault diagnosis model with high precision and good stability is established.