# Damage Diagnosis of Frame Structure Based on Convolutional Neural Network with SE-Res2 Net Module
## 1 Introduction  
With the rapid development of the economy, industry has also entered a new stage of rapid development. Frame structures, with their unique performance characteristics, have been widely applied to mining machinery, construction machinery, bridge engineering and other fields [1, 2]. 'Frame structure' refers to a structure composed of beams and columns which constitutes a load-bearing system that resists the horizontal and vertical loads of a given structure. The transverse part of a steel frame structure mainly bears the constant load of the roof or floor, as well as the service load and the wind load in the horizontal direction. The longitudinal part of the steel frame structure is connected to the frame column by a spring beam, crane beam or wallboard to bear the longitudinal wind load and seismic load as well as to ensure the longitudinal stability of the columns. Steel frame structures have the advantages of being light weight, having flexible collocation and easy standardization and finalization; therefore, they have been widely used in the construction industry. Since the rods of frame structures are connected by bolts or welding, the defects in joints significantly impact the whole structure. Frame structures are mainly used outdoors--mostly at construction sites--which means that the working environments in which frame structures are utilized are harsh and susceptible to vibration, shock, high temperature, corrosion and other factors [3]. If a frame structure is damaged during use, it will threaten the lives and safety of field personnel. Therefore, it is of great significance to study the efficient health monitoring and damage diagnosis technology of frame structures to ensure product safety. The vibration signal based analysis method is one of the most commonly used methods in damage diagnosis [4]. It mainly collects real-time vibration signals at different locationsand then realizes damage diagnosis and real-time monitoring according to signal processing and pattern recognition. Typical methods based on vibration signals include short-time Fourier transform [5], wavelet transform [6] and Wigner-Ville distribution [7]. [PERSON] et al. compressed a spectrum graph into a gray image to obtain information based on the improved short-time Fourier transform method and developed a new prediction and tracking algorithm for fault diagnosis of rotating machinery [8]. [PERSON] et al. proposed an empirical wavelet transform and fuzzy entropy-based damage diagnosis method for motor bearing--which experiments proved to be superior to the empirical mode decomposition method for signal decomposition [9]. [PERSON] et al. analyzed the time and frequency domain of vibration characteristics and conducted damage diagnosis experiments on bearings with Wigner distribution, which effectively predicts the damage locations and degree of bearings [10]. Due to the complex composition of frame structures, the Bayesian analysis and the above time-frequency domain analysis method are less effective for damage diagnosis of frame structures. When the frame structure is massive and heavy, the vibration signal is relatively weak and the vibration signals collected at adjacent locations are similar. Furthermore, actual construction sites contain significant levels of noise, which makes the traditional vibration signal diagnosis method unsatisfactory. In recent years, with the development of machine learning, many shallow machine learning methods, such as artificial neural networks [11], k-nearest neighbor [12], decision tree [13] and support vector machine [14], are widely used in the field of damage diagnosis. [PERSON] et al. used discrete wavelet transform for features extraction and an artificial neural network for classification, which can effectively diagnose different working conditions of the gearbox [15]. [PERSON] et al.