Power grid networks eventually require guyed towers as support structures for transmission lines. In these cases, the cables that support the structure can experience long-term degradation as a result of environmental forces. Long-term degradation can result in tower collapse leading to power outages in essential public services. We propose a Structure Health Monitor (SHM) system to improve transmission line reliability. It is based on data fusion using machine learning algorithms that monitor acceleration signals collected from multiple locations of the guyed tower that can allow the identification of loose cables and the amount of tightness. We select the most relevant individual measurable properties as a set of uncorrelated input sources, which can be in the time or frequency domain. The overall results for loose cable estimation of all investigated methods show a balanced accuracy in the range of 85% up to 96% and identification of tightness shows values between 91% and 96% for respectively inference using selected features set by investigated methods and all features available.