Vibration of hydrogen fuel cell vehicles is greatly influenced by road conditions during driving,and the resulting vibration load may cause surface damage of the on-board gas cylinders,which directly affects their safety and efficiency.To solve the issue of poor load identification due to the unbalanced distribution of road load data for hydrogen storage gas cylinders,an improved deep convolutional generative adversarial networks(DCGAN)model combined with convolutional neural networks(CNN)is proposed for road condition vibration load identification.The DCGAN can realize sample expansion and improve the recognition performance of the model.Meanwhile,aiming at the problem that the convolutional calculation of DCGAN can only deal with the characteristics of adjacent data,the self-attention mechanism(SA)is introduced into the DCGAN to calculate the relationship between feature points of the sample and help the generator of DGCAN to fully learn the global features of the sample and enhance the generalization of the model.Finally,load recognition is realized through CNN.The proposed model is experimentally tested and the results are compared with those of various models.It is found that the accuracy rate of the proposed model for road condition vibration load recognition can reach 96.3%,and the proposed model exhibits better performance than the other models.
关键词
振动与波/路况识别/数据增强/生成对抗网络/自注意力机制/模式识别
Key words
vibration and wave/road condition recognition/data enhancement/generative adversarial network/self-attention/pattern recognition
维普
万方数据