# Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network
## 1 Introduction  
With the continuous development of China's machinery and construction industry, the frame structure is used in various construction sites with large equipment. The safety and stability of the frame structure will have a direct impact on the safety of personnel on the construction site and the stable operation of equipment. The failure of the frame structure is often caused by corrosion or external force impacting the bolts and beams in a certain part. Therefore, it is necessary to place multiple vibration sensors on the frame structure to collect the vibration data of specific parts of frame structure in real time. When the damage occurs in some parts of the frame structure, the damage location can be diagnosed by signal processing based on the collected vibration data. For structural health monitoring and fault diagnosis problems, common methods before the emergence of deep learning include Fourier transform [1, 2], wavelet analysis [3, 4], principal component analysis (PCA) [5, 6], support vector machine (SVM) [7, 8], artificial neural network (ANN) [9, 10], etc. Although the above traditional fault diagnosis methods have been widely used in different fields due to their intuitive operation, with the arrival of the big data era, facing the problem of fault diagnosis under the condition of massive data, diverse features, and strong noise, traditional methods are difficult to ensure the efficiencyand accuracy of fault diagnosis. The composition of the frame structure is complex, so there are many kinds of data to be used in fault diagnosis of the frame structure. In addition, due to the large amount of noise brought in by the complex and changeable environment of the construction site, and the heavy and huge frame structure, the overall vibration caused by the structural damage is often weak. When using traditional signal processing methods to diagnose the fault of the frame structure, it is difficult to separate the effective information features. In recent years, with the rapid development of artificial intelligence, due to the learning characteristics of autonomous mining, the inherent hidden features of data from massive data, deep learning has attracted in-depth research from researchers in various countries and has made fruitful achievements in natural language processing [11; 12; 13], text information retrieval [14; 15; 16], image processing [17; 18; 19], computer vision [20; 21; 22], and other fields. With the maturity of deep learning theory, it has also been widely used in the field of structural health monitoring. [PERSON] et al. used the improved bird swarm algorithm to optimize the two-dimensional convolutional neural network to evaluate the torsional capacity of RC beams [23]. In order to overcome the influence of measurement noise and incomplete data on damage feature extraction, [PERSON] et al. proposed a new method based on depth learning [24]. In order to solve the inaccuracy caused by data anomaly in structural health monitoring, [PERSON] et al. proposed a data anomaly detection method based on computer vision and deep learning [25]. In order to overcome the shortcomings of the existing technology of surface crack diagnosis of the concrete structure and improve the detection accuracy, [PERSON] et al. proposed an automatic recognition method for surface condition identification of concrete structures based on vision by using depth learning theory [26]. To solve the problem of data anomaly in the data preprocessing stage of structural health monitoring, [PERSON] et al. proposed a data anomaly detection method based on the convolutional neural network [27]. [PERSON] et al. adopted a two classification method with multiple 1 DCNNs (one-dimensional continuous neural network) to study the fault diagnosis of the grandstand model and the four floor structure model, respectively [28].