## 1 Introduction  
Severe weather and extreme terrain bring great challenges to long-distance transmission lines, and mechanical failures of transmission towers will lead to serious economic losses and even threaten personal safety. The excessive stress on the towers of transmission lines is the root cause of tower overturning and tower collapse [1]. To obtain the size of the tower strain, [PERSON] et al. of Xi'an Engineering University proposed a method based on a fiber optic grating sensor for tower stress monitoring of transmission lines, which mainly uses a grating fiber sensor attached to the surface of the measured pole and determines the size of the stress change by measuring the grating reflection wavelength or transmission wavelength. This method involves the preparation, laying, and demodulation of grating fiber, which makes its cost too high [2]. [PERSON] et al. of Changsha College used BeiDou differential positioning technology to monitor the size and direction of key pole and node displacements. However, it is difficult to achieve accurate monitoring of small deformations of pylons with the current satellite positioning technology, and the supplementation of other sensors, such as tilt sensors, is also needed [3]. In this paper, we used multisensor fusion technology based on tilt sensors, wind speed sensors, and historical weather data to build a relevant dataset for analyzing tower fault information. The proposed system uses inclination sensors and wind direction sensors to measure tower data and, meanwhile, combines real-time weather data such as real-time temperature, humidity, and current rainfall to construct a dataset and train a support vector machine model to classify faults. In this paper, we also predicted four types of faults: tower icing, conductor galloping, tower base settlement, and water corrosion. These faults are often accompanied by changes in the environmental characteristics around the tower, thus we selected temperature \(x_{1}\), humidity \(x_{2}\), wind direction \(x_{3}\), wind \(x_{4}\), rainfall (snow) \(x_{5}\), water accumulation \(x_{6}\), and tilt size \(x_{7}\) as the fault features and trained a classification model to classify the four faults: tower icing, conductor galloping, tower base settlement, and water corrosion [4]. The sensor is supposed to collect a variety of data during the operation of the tower, and the vast majority of these data are generated during the normal operation of the tower, which will create two problems: first, the fault sample data is too small to completely cover all fault types; second, the amounts of fault data and normal operation data are unbalanced. Either problem will have a large negative impact on the accuracy of the machine learning process. The inadequacy of fault sample data can be compensated by extending the sampling time of the sensors, but this approach will further increase the imbalance of the data. Therefore, a data processing method that can improve the data balance is needed [5]. This article uses the metrological data of imaging in the Jilin area provided by Jilin Provincial Metrological Bureau to build the fault sample. The fault sample dataset has seven features. However, in the image representation, in order to intuitively represent the unbalanced dataset, only two-dimensional images can be drawn here. The two-dimensional characteristics of positive and negative samples in the fault sample data are selected here, and the unbalanced two-dimensional dataset in two-dimensional coordinates is drawn in Figure 1, which shows the representation of an unbalanced 2D dataset in 2D coordinates. For most datasets, there are two general ways to improve data balance: the oversampling method and the undersampling method.