## 1 Introduction  
Solar energy has become one of the most promising alternatives in the fight against climate change and high energy demand. Particularly, photovoltaic technology has had accelerated growth due to its low environmental impact and the recent reduction in technology costs [1]. In recent years, research groups have focused on increasing low conversion efficiency through the development of new materials [2; 3; 4], multi-junction configurations in solar cells [5; 6], development of concentrating solar technology [7], and control strategies for generation capacity improvement [8; 9], among others. The efficiency and generation capacity are related to the position of the solar collector; Solar Tracking Systems (STS) allow solar technology to follow the solar trajectory throughout the day [10], where the aim is to increase the amount of solar radiation received by the conversion technology. One classification of STS is according to the axes of movement into single-axis and dual-axis trackers. Studies have shown that dual-axis systems in an azimuth-elevation configuration are the most efficient and can increase the transformed energy by 67.65% compared to a fixed PV system [11], with the active tracker being the most common drive type with 76.42% [12], which determines the position of the solar path with pointing sensors. However, tracking systems have disadvantages such as complexity and size, which increase costs and complexity of maintenance, requiring special installations. In addition, the probability of failures increases due to the number of moving components required, which causes losses of precision and profitability. These movements cause wear and tear on the physical components of the tracker, reducing the tracking accuracy or even stopping it from operating, which causes losses in energy production capacity and economics, affecting their performance and increasing energy consumption and tracking errors over a lifetime. Additionally, the complexity of tracking systems increases the probability of failures because they integrate electrical-electronic, electromechanical, and mechanical components. These devices are subject to drastic changes in weather conditions, mainly temperature ranges, rainfall, high humidity, and solar irradiance. Then, the tracking systems are subject to static and dynamic loads due to environmental conditions, and it is necessary to ensure continuous operation to harvest the greatest amount of solar energy. Therefore, it is essential to increase the reliability of the systems without affecting energy gain or increasing costs for additional devices. Consequently, much of the efforts of the scientific and industrial community are focused on improving the efficiency, reliability, and lifespan of solar harvesting systems to increase the energy gain during a period of 20 to 30 years and guarantee the best performance over time. There are multiple investigations focused on diagnosing the status of the photovoltaic system, which require additional devices such as DC-DC converters, charge controllers, power electronics, and processing devices that allow the implementation of algorithms. By estimating the current and voltage of the network [13], embedded devices detect faults by assessing and identifying, at the software level, the currents of the PV modules [14]. In [15], an approach is presented to detect faults in PV systems based on the processing of thermal images using a Support Vector Machine (SVM). The authors in [16] performed an analysis of the performance of a PV array by processing thermal images to detect faults. Finally, in [17], an extensive study of the various methods and techniques used for fault detection in PV systems is presented, including a classification and an approach for efficient maintenance. On the other hand, there is little to no research evidence regarding the lifetime of solar tracking systems, which considers the physical effects on the components due to the tracking action.