## 1 Introduction
In [18], a unified metric system is presented to evaluate the reliability of solar tracking systems in the software domain, estimating the convergence of faults of syntax errors, structural errors, and stuck-at-faults. However, structural and electromechanical faults are not considered, being elements subject to continuous wear due to the tracking action. [PERSON] and their team present in [19] a mathematical approach to estimate and improve the solar reliability factor (SRF), showing that factors such as prolonged exposure to sunlight and varying weather conditions significantly affect the failure rate. This research focuses on data errors in electronic components, not estimating the lifetime of the solar tracking system per se. In [20], the design of real-time test hardware for solar tracking is presented, considering electronic hardware failures of the open and short circuits without regarding failures in the moving components of the tracker. Experimental tests were performed, where the PV system generated a total output power of 10.76 W, and the tracker and monitoring hardware consumed 6.13 W, representing low energy gain. In [21], a technique is presented to test the software code of a solar tracking device, implementing a white-box testing approach through Wi-Fi communication. As a result, a coverage of 70.12% of the errors related to loopholes and possible breakpoints in the tracker software was achieved. In the structural domain, [PERSON] et al. [22] presented an investigation of failures in a single-axis solar tracking system, which concludes that the tilt angle increases the probability of structural failures due to wind loads, generating a dynamic phenomenon called torsional galloping. They proposed a solution design of trackers with higher torsional resistance or the implementation of control of the tilt angle to move a safe position in dangerous situations. Additionally, recent research has indicated that monitoring systems are required to identify potential faults or existing damage as early as possible so that they can be corrected or prevented in advance [11]. This involves developing intelligent control strategies that allow decisions to be made to improve performance by recording extensive data on weather conditions and tracker characteristics [23; 24; 25]. In consequence, monitoring and online supervision are required and have a fundamental role in the solar tracking system's lifetime improvement, allowing the decision-making process in real time through the implementation of strategies that increase global energy gain, characterizing the impact of solar and environmental parameters on energy generation with tracking, and diagnosis and prediction of hardware and software failures. Hence, the possibility of remote monitoring helps to control generating systems in distant locations through the transmission of data in real time for the detection and prevention of possible failures in photovoltaic technology or tracking systems, allowing the estimation of the levels of dirt due to dust and the detection of abrupt changes in energy generation, making it easier to implement preventive actions, reducing repair times and maintenance costs [26]. In summary, the literature on solar tracking systems mentions that there is still a need to develop trackers with high precision, low maintenance and low cost, and high reliability and robustness. Tests must be carried out outside the laboratory under realistic operating conditions, using methodologies for overall improvement [27]. Therefore, in two-axis solar tracking systems, it is essential to consider real-time monitoring parameters and energy consumption, and component lifetime estimation in the optimization strategies to increase the viability of these systems. The present research seeks to solve the challenges above across the development of a novel tracking strategy based on real-time measurements to increase the lifetime without reducing the energy productivity of the tracking systems, considering the tracking error and the energy consumption in the decisions.