摘要
针对矿山充填管道磨损缺陷检测存在的人工检测困难和检测成本高等问题,提出了一种融合SENet的密集连接卷积神经网络模型(SE_DenseNet),可实现充填管道不同磨损程度的远程快速识别。首先通过完全集合经验模态分解(CEEMDAN),对与原信号相关性较高的分量进行重构;之后,使用短时傅里叶变换,形成声谱图;将声信号识别问题转化为图像识别问题;并将声谱图输入到DenseNet网络模型,通过特征重用,融合通道注意力机制SENet,增强特征信息,实现对充填管道磨损声信号的准确声音识别。结果表明:SE_DenseNet的识别准确率可达到97.368%。相比同类深层基线网络模型ResNet101和基线DenseNet121而言,该网络模型泛化能力及识别准确率有所提升,在模型参数数量上有所下降,实现更快收敛。SE_DenseNet的上述优势可被应用于类似的固液两相流输送管道无损检测领域。
Aiming at the difficulty of manual detection and high costs in the detection of wear defects in mine filling pipelines,a densely connected convolutional neural network model(SE_DenseNet)integrated with SENet is proposed,which can realize remote and rapid identification of filling pipeline wear.The method firstly reconstructs the components with high correlation with the original signal through complete ensemble empirical mode decomposition(CEEMDAN).After that,the short-time Fourier transform is used to transform the acoustic signal recognition problem into an image recognition problem.And finally,the spectrogram is input into the DenseNet network model,and through feature reuse,the channel attention mechanism SENet is fused to enhance the feature information,and the accurate voiceprint recognition of the filling pipeline wear sound signal is realized.The results show that the accuracy of SE_DenseNet recognition can reach 97.368%.Compared with the similar deep baseline network models ResNet101 and DenseNet121,the generalization ability and recognition accuracy of the network model have been improved,the number of model parameters has decreased,so the model can converge faster.The above advantages of SE_DenseNet can be applied to similar non-destructive testing of solid-liquid two-phase flow pipelines.
作者
邢怡君
杨鹏
吕文生
贯怀光
王璟
XING Yijun;YANG Peng;LYU Wensheng;GUAN Huaiguang;WANG Jing(Beijing Key Laboratory of Information Service Engineering,Beijing Union University,Beijing 100101,China;Urban Rail Transit and Logistics College,Beijing Union University,Beijing 100101,China;School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing 100083,China;CATARC(Tianjin)Automotive Engineering Research Institute Co.,Ltd.,Tianjin 300300,China)
出处
《有色金属工程》
CAS
北大核心
2023年第8期102-109,共8页
Nonferrous Metals Engineering
基金
国家“十四五”重点研发计划(2021YFC3001302)
国家自然科学基金资助项目(51774045)。
