# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 1 Introduction
proposed an enhanced k-nearest neighbor algorithm, which can obtain the correlation vector between training and test dataset samples and adaptively select the nearest neighbor to obtain the corresponding label [16]. [PERSON] et al. implemented the support vector machine model based on particle swarm optimization to realize fault identification of rolling bearings under different working conditions [17]. However, with the intellectualization and modernization of mechanical equipment, the operation of mechanical equipment produces a large amount of data. It is challenging to obtain damage features by using the above shallow machine learning methods. With the development of artificial intelligence technology [18], deep learning theory has been widely applied in signal classification, intelligent fault diagnosis, image recognition, analog data generation, target detection and other fields [19; 20; 21; 22; 23; 24]; and a variety of neural networks have been derived. In these neural networks, the one-dimensional convolutional neural network (1 DCNN) [25], VGG-Net [26], AlexNet [27], ZFNet [28] and other neural networks have been used by many researchers for mechanical damage diagnosis by transforming dimensions and changing depth and convolution kernel size. However, long short-term memory [29], recurrent neural networks [30] and faster convolutional neural networks [31] have been improved by many scholars by changing the number of feature extraction layers and using the residual neural network, so as to improve the feature extraction capability of the neural network--and have then been applied to real-time detection in the fields of mechanical vibration and unmanned aerial vehicle vision. In the field of structural health monitoring and fault diagnosis, deep learning has also been widely studied and applied. [PERSON] et al. proposed an improved multi-scale convolutional neural network by extracting multi-scale features from vibration signals, and improved the fault diagnosis ability of rolling bearings under strong noise and variable loads [32]. [PERSON] et al. used a deep belief network to build a health assessment system based on multiple sensors which was verified by experiments [33]. [PERSON] et al. proposed an intelligent fault diagnosis method for aero-engine high-speed bearings based on an enhanced convolutional neural network; the proposed method was experimentally studied and the high diagnosis accuracy of bearing faults were obtained [34]. [PERSON] et al. realized a four-classification fault diagnosis of rotating machinery by using a two-dimensional convolutional neural network [35]. [PERSON] et al. proposed an enhanced convolutional neural network model for mechanical fault diagnosis; firstly, the Morlet wavelet decomposition method was used to preprocess the vibration signal to obtain the wavelet scale image; then, the wavelet scale image was scaled onto a gray image with a size of 32 by bilinear interpolation; and finally, the newly obtained gray image was used as the model input [36]. [PERSON] et al. used a one-dimensional convolutional neural network to diagnose the damage of a four-layer frame structure [37]. However, due to the inferior accuracy of the neural network model used under a strong noise environment, it is necessary to propose a new model with more significant anti-noise performance for the damage diagnosis of frame structures. The damage diagnosis research object in this paper is a four-layer frame structure model built by the University of British Columbia [38]. Based on convolutional neural networks with training interference (TICNN) [39], this paper proposes a new model for damage diagnosis of frame structures by modifying the parameters and configuration of TICNN and implanting a squeeze-and-excitation neural network (SENet) [40] and Res2 Net [41] modules into it.