SpringerBriefs in Energy: Decomposition, Entropy, and Machine Learning

Zhang, Yuning; Yang, Chenxin; Luo, Peng; Zhang, Heng

This brief explores advanced signal processing techniques, focusing on signal decomposition, entropy analysis, and machine learning, with applications in energy-related fields such as hydroturbines, wind turbines, and power grids. It provides a detailed overview of methods for signal denoising and pattern recognition, covering techniques like wavelet transform, empirical mode decomposition, permutation entropy, and deep learning models. Through real-world engineering case studies, the book demonstrates how these methods enhance data analysis, improve fault detection, and optimize system performance, making it a valuable resource for researchers, engineers, and students in signal processing and mechanical engineering.

Details

Published by: Springer

Publication Date: 2026-01-03

Format: Paperback

ISBN-13: 9783032118530

DOI: 10.1007/978-3-032-11854-7

Dimensions: 235cm x155cm

Pages: 83