基于互信息和MiniRocket网络的CFST脱空识别

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (8) : 202-212.

论文

覃悦1，谢开仲1,2,3，郭晓1,2,3，王红伟4，王秋阳1，彭佳旺1

作者信息 +

Identification of CFST voids based on mutual information and MiniRocket network

QIN Yue1,XIE Kaizhong1,2,3,GUO Xiao1,2,3,WANG Hongwei4,WANG Qiuyang1,PENG Jiawang1

Author information +

文章历史 +

摘要

为提高钢管混凝土（concrete filled steel tube，CFST）脱空检测的效率和精度，本文提出了一种基于快速傅里叶变换（fast fourier transform，FFT）、互信息（mutual information，MI）和MiniRocket神经网络的智能识别方法。首先，采用FFT将待测CFST敲击声波时域信号转换为频域信号；其次，采用MI建立频域信号与脱空状态的相关性，提取相关性最大的前30个特征建立数据集，避免了复杂的数学运算和冗余信息；建立MiniRocket深度学习网络，通过使用更少的参数量和更小的特征尺寸，提高分类的速度和精度。最后，考察了模型的噪音鲁棒性，并与其他算法、特征提取方法和识别方法进行对比。结果表明，在不同脱空深度和脱空宽度下，所提的方法在100次重复实验中获得了100%的平均预测精度。在高信噪比下，该方法受影响较小。此外，与其他算法、特征提取方法和识别方法相比，本方法具有更好的预测性能。因此，所提出的方法在未来实际CFST结构的智能脱空识别中具有较大的应用潜力。

Abstract

In order to improve the efficiency and accuracy of concrete filled steel tube（CFST） void detection， an intelligent recognition method based on FFT (Fast Fourier Transform), MI (Mutual Information) and MiniRocket neural network is proposed in this paper. First, the time domain signal of the CFST percussion wave to be measured is converted to the frequency domain signal using FFT. Secondly, MI is used to establish the correlation between the frequency domain signal and the void state, and the top 30 features with the largest correlation are extracted to establish the data set, which avoids complex mathematical operations and redundant information. A MiniRocket deep learning network is established, and by using fewer parameters and smaller feature sizes improving the speed and accuracy of classification. Finally, the noise robustness of the model is investigated and compared with other algorithms, feature extraction methods and recognition methods. The results show that the proposed method achieves 100 % average prediction accuracy in 100 repetitions of the experiment for different void depths and void widths. At high SNR, this method is less affected. In addition, compared with other algorithms, feature extraction methods and recognition methods, this method has better prediction performance. Therefore, the proposed method has great application potential in the actual intelligent void identification of CFST in the future.

导出引用

覃悦1，谢开仲1,2,3，郭晓1,2,3，王红伟4，王秋阳1，彭佳旺1. 基于互信息和MiniRocket网络的CFST脱空识别[J]. 振动与冲击, 2024, 43(8): 202-212

QIN Yue1,XIE Kaizhong1,2,3,GUO Xiao1,2,3,WANG Hongwei4,WANG Qiuyang1,PENG Jiawang1. Identification of CFST voids based on mutual information and MiniRocket network[J]. Journal of Vibration and Shock, 2024, 43(8): 202-212

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