This paper presents a Gaidai multivariate reliability method for evaluating the operational safety of energy harvesters (EHs) with manufacturing imperfections. The method is particularly suitable for non-stationary, imperfect, or damaged multi-dimensional energy harvesting systems. It uses analog observations from representative timelapse to produce quasi-ergodic system dynamic records. The study demonstrates that this technique can be used to evaluate the risk of damage or failure in dynamic systems. The method is designed to handle high-dimensionality, deterioration, and nonlinear cross-correlations between key components of dynamic systems, which are challenging for standard reliability approaches. The goal of this study was to benchmark the novel Gaidai multivariate reliability approach, which allows for effective processing of statistical data even from limited, multivariate non-stationary datasets. The method aims to assist designers in evaluating the risks of failure and hazards for nonlinear multidimensional dynamic energy harvesting systems when initial manufacturing imperfections are present. Energy harvesters have gained popularity due to the promotion of wind energy as a green and renewable resource. Piezoelectric, triboelectric, and electrostatic EHs are used to collect wind energy on small scales, while electromagnetic EHs are used on larger scales. Recent developments have shown that low-frequency EH technology is beneficial. These advancements have helped design inexpensive, low-power devices such as MEMS and WNS. PVEHs can convert mechanical vibrational energy into piezoelectric energy. Due to their irregular and sporadic nature, mechanical vibrations can cause aero-instability in the form of resonant vibrations. Using FIVs to create energy is feasible, with examples including VIV, wake galloping, and similar phenomena. Energy collection systems are important for both onshore and offshore applications. As maritime business expands, the risks of ocean contamination increase. To address these issues, water monitoring devices such as various types of sensors have been developed. Sensors can collect data from remote locations and transmit it to nearby stations. However, battery replacement or recharging is costly, time-consuming, and labor-intensive. Additionally, the development of lightweight, compact electronic devices has been hindered by battery weight and size. There is also concern about the potential leakage of hazardous chemicals from batteries that could harm the environment. Therefore, it is essential to develop battery-free, self-powered EHs. The adoption of technologies like MEMS has led to the inclusion of several low-energy consumption sensors. Miniaturization of power consumption and mobility are the most pervasive themes in newly created sensors and EHs. These EH devices have frequently been powered by chemical batteries, but battery life is often shorter than EH sensor life. Studying critical EH dynamics is important to reduce its potentially harmful impacts on the environment.This paper presents a Gaidai multivariate reliability method for evaluating the operational safety of energy harvesters (EHs) with manufacturing imperfections. The method is particularly suitable for non-stationary, imperfect, or damaged multi-dimensional energy harvesting systems. It uses analog observations from representative timelapse to produce quasi-ergodic system dynamic records. The study demonstrates that this technique can be used to evaluate the risk of damage or failure in dynamic systems. The method is designed to handle high-dimensionality, deterioration, and nonlinear cross-correlations between key components of dynamic systems, which are challenging for standard reliability approaches. The goal of this study was to benchmark the novel Gaidai multivariate reliability approach, which allows for effective processing of statistical data even from limited, multivariate non-stationary datasets. The method aims to assist designers in evaluating the risks of failure and hazards for nonlinear multidimensional dynamic energy harvesting systems when initial manufacturing imperfections are present. Energy harvesters have gained popularity due to the promotion of wind energy as a green and renewable resource. Piezoelectric, triboelectric, and electrostatic EHs are used to collect wind energy on small scales, while electromagnetic EHs are used on larger scales. Recent developments have shown that low-frequency EH technology is beneficial. These advancements have helped design inexpensive, low-power devices such as MEMS and WNS. PVEHs can convert mechanical vibrational energy into piezoelectric energy. Due to their irregular and sporadic nature, mechanical vibrations can cause aero-instability in the form of resonant vibrations. Using FIVs to create energy is feasible, with examples including VIV, wake galloping, and similar phenomena. Energy collection systems are important for both onshore and offshore applications. As maritime business expands, the risks of ocean contamination increase. To address these issues, water monitoring devices such as various types of sensors have been developed. Sensors can collect data from remote locations and transmit it to nearby stations. However, battery replacement or recharging is costly, time-consuming, and labor-intensive. Additionally, the development of lightweight, compact electronic devices has been hindered by battery weight and size. There is also concern about the potential leakage of hazardous chemicals from batteries that could harm the environment. Therefore, it is essential to develop battery-free, self-powered EHs. The adoption of technologies like MEMS has led to the inclusion of several low-energy consumption sensors. Miniaturization of power consumption and mobility are the most pervasive themes in newly created sensors and EHs. These EH devices have frequently been powered by chemical batteries, but battery life is often shorter than EH sensor life. Studying critical EH dynamics is important to reduce its potentially harmful impacts on the environment.