According to the problems of low maintenance efficiency and high cost for regular maintenance and after-the-fact maintenance methods of the automatic magazine transport system of a naval gun, an expert system for fault diagnosis based on fault tree is proposed to improve the success rate of the system operation, extend the service life, and reduce maintenance difficulty and cost. According to the characteristics of the magazine system to build the fault tree model, combined with rules to build the diagnostic rule base of expert system, using mixed reasoning strategy to build the inference engine, and the human-machine interface for fault diagnosis is designed and built. Finally, the diagnosis effects are analyzed by examples. The results show that this fault diagnosis expert system has a good diagnostic effect and applicability. This kind of method has a certain versatility and provides reference for fault diagnosis of other systems
With the development of hyperspectral technology and the increase of hyperspectral dimension, single model is difficult to apply to the process of feature selection, feature extraction and feature integration for hyperspectral image, causing the undesirable hyperspectral classification effect. In order to improve the classification accuracy, a kind of algorithm of uniting convolutional neural network and multihead attention is proposed. Firstly, PCA algorithm is used for dimensionality reduction of hyperspectral data; Then, excavation feature of multi-scale convolutional neural network is utilized; Finally, residual layer and classification layer are utilized for the integration of convolution results and the classification of hyperspectral image. open-sourcing hyperspectral dataset Piavia, Salinas and Inida are verified, and the algorithm in this paper can improve the hyperspectral classification accuracy efficiently.
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