Improved Vision-Transformer based fault diagnosis in unmanned boat power system
In order to meet the development demands for high reliability and intelligent maintenance support of un-manned boats,a fault diagnosis method of unmanned boat power systems based on an improved VIT neural network model is proposed.By employing an enhanced attention mechanism to determine model parameters,the algorithmic space complex-ity is reduced,and the model parameters are prevented from being confined to local optima.Through short-circuit fault simu-lations of AC low-voltage unmanned boat power systems,a fault dataset is established.Continuous wavelet transform is util-ized to extract features from fault voltage sequence data.These feature data are used to train the improved VIT model to achieve fault diagnosis for unmanned boat power systems.The paper conducts a comparative simulation study on the fault recognition performance of the improved VIT model,convolutional neural networks(CNN),deep residual shrinking net-works(DRSN),and the standard VIT model.The results indicate that the improved VIT model exhibits higher fault diagnos-is accuracy and is less affected of changes in short-circuit fault resistance,demonstrating stronger adaptability.
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