Advanced Signal Processing: Decomposition, Entropy, and Machine Learning (SpringerBriefs in Energy) - Tapa blanda

Acerca del autor

Yuning Zhang is mainly engaged in the research of fault diagnosis, intelligent operation and maintenance of hydropower units, big data analysis, and health status management of wind turbines. He has published over 100 journal papers as leading author or corresponding author and 2 Springer briefs. He serves as an editorial board member of the IET Renewable Power Generation and the Journal of Hydrodynamics. He has hosted 3 conferences as chairman and delivered 27 invited talks. 

Chenxin Yang research is mainly focused on signal analysis of pump turbines and wind turbines. He has published 5 journal papers. He has received the first prize of Science and Technology Innovation from the China Electricity Council in 2024.

Peng Luo conducts research on electricity production and renewable energy. He has participated in and completed 15 research projects. He has delivered 37 patents. He has received many awards from State Grid Corporation of China, China Enterprise Confederation, Gansu Province, etc.

Heng Zhang is mainly engaged in the research of signal analysis of energy systems. As a first/corresponding author, he has published more than 50 SCI research papers. He has received many prizes from the China Petrochemical Association and the Science and Technology Award of the National Film Industry Association. One of his papers was selected as an excellent paper by the China Society of Electrical Engineering. 

De la contraportada

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.