Energy Harvesting Mechanisms in Self-Powered Mechanical Systems

Abstract

Energy harvesting in self-powered mechanical systems has emerged as a critical field, enabling sustainable energy solutions in a wide range of applications. This research explores advanced techniques for optimizing energy harvesting mechanisms using a deep learning-based approach. Specifically, a Convolutional Neural Network (CNN) is employed for feature extraction, enabling precise identification of energy-rich mechanical motion patterns. Data preprocessing includes signal normalization to eliminate noise and ensure uniformity in input signals. For classification, a Long Short-Term Memory (LSTM) network is used to categorize harvested energy signals into specific utilization domains, such as kinetic, vibrational, or thermal energy. The proposed methodology demonstrates improved energy capture efficiency and classification accuracy, providing a robust framework for enhancing self-powered mechanical systems. This work lays the foundation for integrating intelligent algorithms in energy harvesting research, ensuring smarter, more adaptive, and efficient systems.