Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections

Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety, Given Manufacturing Imperfections

29 February 2024 | Oleg Gaidai, Vladimir Yakimov, Fang Wang, Yu Cao
This study focuses on the reliability and safety of energy harvesters, particularly piezoelectric energy harvesters (PVEH), in the context of wind energy. The authors utilize extensive wind-tunnel tests under realistic wind speeds to evaluate the dynamic performance of galloping energy harvesters. They introduce the Gaidai multivariate reliability method, which is designed to handle non-stationary, imperfect, or damaged multi-dimensional systems. This method is particularly useful for assessing the risk of damage or failure in dynamic systems, especially those with high dimensionality, deterioration, and nonlinear cross-correlations. The study aims to benchmark the Gaidai multivariate reliability approach, which can effectively process statistical data from limited, non-stationary datasets. The research highlights the importance of developing battery-free, self-powered energy harvesters to reduce environmental impact and improve sustainability. The experimental setup and results are detailed, showing the dynamic performance of the GPEH under various wind speeds and conditions.This study focuses on the reliability and safety of energy harvesters, particularly piezoelectric energy harvesters (PVEH), in the context of wind energy. The authors utilize extensive wind-tunnel tests under realistic wind speeds to evaluate the dynamic performance of galloping energy harvesters. They introduce the Gaidai multivariate reliability method, which is designed to handle non-stationary, imperfect, or damaged multi-dimensional systems. This method is particularly useful for assessing the risk of damage or failure in dynamic systems, especially those with high dimensionality, deterioration, and nonlinear cross-correlations. The study aims to benchmark the Gaidai multivariate reliability approach, which can effectively process statistical data from limited, non-stationary datasets. The research highlights the importance of developing battery-free, self-powered energy harvesters to reduce environmental impact and improve sustainability. The experimental setup and results are detailed, showing the dynamic performance of the GPEH under various wind speeds and conditions.
Reach us at info@study.space
Understanding Gaidai Multivariate Reliability Method for Energy Harvester Operational Safety%2C Given Manufacturing Imperfections