To improve the precision of quantifying the damage caused by broken wires of passenger ropeways load carrying rope,this paper presents a novel approach for the quantitative identification of such damage,utilizing convolutional neural networks.Through the selection and optimization of the structure and training parameters of the quantitative identification model of convolutional neural network,the obtained target model was tested for quantitative identification of broken wire damage with different diameters,different number of broken wires,different break widths and internal and external.The results show that the model can accurately identify various types of broken wire damage,and the classification accuracy is more than 99%.Finally,the validity,accuracy and applicability of the proposed method are further verified by field application.
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