# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 6 Conclusions  
We have seen so far that the collapse of overhead power line guyed towers is one of the leading causes of power grid failures, subjecting electricity companies to pay considerable, high-value fines. Additionally, city blackouts resulting from the collapse of cable-stayed structures cause financial losses of millions of dollars for industries and threaten sensitive sectors, such as hospitals that depend on an uninterrupted energy supply. We pointed out that traditional solutions, such as ground inspection by foot, seem ineffective since in-site periodic inspections are sometimes insufficient to detect hidden defects in such structures. Furthermore, the interval between visits and expensive personnel allocation makes monitoring/predicting defects difficult. Other monitoring methods, such as wired communication and video monitoring, are costly and hard to scale. Henceforth, we proposed a novel and complete framework for remotely monitoring mechanical stresses and deformation of guyed towers. The proposed framework considers wireless monitoring of environmental threats in two ways. First, the analysis of remotely recorded data allows finding evidence of risk in the guyed tower structure, due to environment-critical events and, subsequently, a correlation between the guyed tower structural failure and harsh environment degrading factors. Second, the constant online sampling of data in the cable traction allows real-time monitoring of the guyed tower health, which helps to prompt personnel to act immediately. Furthermore, the reduction of in-field inspections and the adoption of ubiquitous monitoring increase the productivity of the asset management wellness and ensure the quality of service for the consumer in the power transmission segment of the electric sector. Therefore, we expect the risk of collapse of guyed cable towers to reduce when the proposed framework is applied. In addition, we hope to facilitate inspection and optimize maintenance since remote monitoring allows to switch from periodic to on-demand maintenance visits. We also showed that it is possible to estimate tower parameters using a neural network and sensor fusion from multiple sensors. The proposed method uses multiple input variable fusion, such as accelerometers and tension sensors, to estimate the tower's displacement. These estimations help address the structural health of the tower against failures (i.e., loosening of the stay cables, displacement, and vibrations) that can cause catastrophic events, such as tower collapse or even cable rupture. Finally, we hope to facilitate inspections, increasing the productivity and quality in asset management since remote monitoring allows to switch from periodic to on-demand maintenance visits. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **X** & & & **Y** & \\ \hline
**Features** & **NRMSE** & **MAPE** & **R\({}^{2}\)** & **NRMSE** & **MAPE** & **R\({}^{2}\)** \\ \hline A1, A2, A3, A4 & 0.1078 & 1.8595 & 0.9884 & 0.3335 & 6.7182 & 0.8888 \\ \hline X1, Y1, Z1, X2, Y2, \(\ldots\), \(Z4\) & 0.0696 & 1.4419 & 0.9952 & 0.1369 & 2.6887 & 0.9813 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Estimation of X and Y using different features. **Author Contributions:** [PERSON] participated in conceptualization, formal analysis, research, software, visualization and writing--original draft; [PERSON] participated in concept design and writing--review; [PERSON] participated in concept design and writing--review; [PERSON] participated in conceptualization, funding acquisition, project administration and review;. [PERSON] participated in conceptualization, funding acquisition, project administration and review;. [PERSON] participated in visualization, technical support and review. All authors have read and agreed to the published version of the manuscript.