Li Pengbo, Tian Jie
School of Mechanical, Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Key Laboratory of Intelligent Mining and Robotics, Ministry of Emergency Management, Beijing 100083, China.
Entropy (Basel). 2024 Jun 21;26(7):531. doi: 10.3390/e26070531.
In order to solve the problem of great difficulty in detecting the internal damage of wire rope, this paper proposes a method to improve the VGG model to identify the internal damage of wire rope. The short-time Fourier transform method is used to transform the wire rope damage signal into a time-frequency spectrogram as the model input, and then the traditional VGG model is improved from three aspects: firstly, the attention mechanism module is introduced to increase the effective feature weights, which effectively improves the recognition accuracy; and then, the batch normalization layer is added to carry out a uniform normalization of the data, so as to make the model easier to converge. At the same time, the pooling layer and the fully connected layer are improved to solve the redundancy problem of the traditional VGG network model, which makes the model structure more lightweight, greatly saves the computational cost, shortens the training time, and finally adopts the joint-sample uniformly distributed cross-entropy as the loss function to solve the overfitting problem and further improve the recognition rate. The experimental results show that the improved VGG model has an identification accuracy of up to 98.84% for the internal damage spectrogram of the wire rope, which shows a good identification ability. Not only that, but the model is also superior, with less time-consuming training and stronger generalization ability.
为了解决钢丝绳内部损伤检测难度大的问题，本文提出一种改进VGG模型以识别钢丝绳内部损伤的方法。采用短时傅里叶变换方法将钢丝绳损伤信号转换为时频频谱图作为模型输入，然后从三个方面对传统VGG模型进行改进：一是引入注意力机制模块增加有效特征权重，有效提高识别准确率；二是添加批归一化层对数据进行统一归一化，使模型更易收敛；同时，对池化层和全连接层进行改进以解决传统VGG网络模型的冗余问题，使模型结构更轻量化，大幅节省计算成本，缩短训练时间，最后采用联合样本均匀分布交叉熵作为损失函数解决过拟合问题并进一步提高识别率。实验结果表明，改进后的VGG模型对钢丝绳内部损伤频谱图的识别准确率高达98.84%，显示出良好的识别能力。不仅如此，该模型还具有优势，训练耗时少且泛化能力更强。
