# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 2 Related Work
Concerning the focus on sensor technology, the research work in [16] proposes a computational algorithm based on least-squares state estimation applied to transmission line sag-tension equations through a geometric transformation to determine conductor sag levels. The estimation of the tower health considers tower tilt, conductor tension, core temperature, the inclination angle of the conductor with the horizon as input parameters. Ref. [17] also presents a similar system for online monitoring based mainly on real-time measurements, such as current through the line, conductor temperature, conductor inclination angle/sag, mechanical tension of the line, conductor oscillations. Differently, the authors in [18] focus on monitoring the transmission tower base's vertical displacement responses, using a hydrostatic leveling system, which works as a slope sensor. The research work shows the importance of making the right choice of sensor technology. Furthermore, the specific sensor is strongly affected by the environmental temperature, which impacts the actual evaluation of tower health. In [19], the authors propose an online monitoring technique applied to power line towers near mining, river, hillside, and other like areas. The system monitors the stress or strain on the tower that can cause foundation deformation. The stress evaluation considers sensing information on foundation settlement, inclination, and side-slip through a finite element model of the tower. The sensing technology used in the actual tower foundation is a fiber Bragg grating. The results show that the tower foundation condition is compromised when sensing data values cross the limits of the ones seen in a tower of reference that it is in good condition. Despite previous sensor technology kinds, it is essential to present research works focusing on vibration approaches. In [20], the system generates an impulse excitation to the transmission tower to learn about the tower's health. First, the monitoring device uses accelerometers to read the impulse excitation. Then, the method allows revealing the tower's natural frequencies that can help evaluate the tower's condition. Ref. [21] also presents a vibration-based transmission tower structural health monitoring system. An analyzer receives data from the acceleration sensors and calculates the tower's natural frequencies. The method for analyzing the natural frequencies at different wind speeds is stochastic subspace identification (SSI), which can reveal the number of natural frequencies of the overall structure. The researchers test the approach by directly interfering (i.e., lifting one of the tower legs) and comparing the natural frequencies of each case. The results show that differences between before and after the interference can be the basis for judging the condition of the tower structure. On the way to modern approaches, machine learning (ML) techniques are relevant in structural monitoring systems for fault, risk detection, and modal parameter estimation. Sensor information, such as vibration and electromagnetic response, may be input to ML models to detect faults or risk situations [22; 23] and, in the case of structures with multiple potential points-of-failure, to pinpoint the location of faults [24; 25]. Modal parameters, such as stiffness and position, may also be estimated through ML models [26]. In [22], the authors develop a Convolutional Neural Network (CNN) for the classification and extraction of features and detecting different damage cases in a structural monitoring system based on vibration data sampled from accelerometers. In [23], the authors apply K-Nearest Neighbors to classify different damage cases in structures having vibration data as input. The authors in [24] propose a method of detection and damage localization in structures based on vibration signals using CNN. In [25], a two-step machine learning method is proposed for the classification and location of faults in structures, using electromagnetic response data.