Ye Yuan – författare

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning.

Features:

This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.

This book unifies existing and emerging concepts concerning state estimation, fault detection, fault isolation and fault estimation on industrial systems with an emphasis on a variety of network-induced phenomena, fault diagnosis and remaining useful life for industrial equipment. It covers state estimation/monitor, fault diagnosis and remaining useful life prediction by drawing on the conventional theories of systems science, signal processing and machine learning.

Features:

This book aims at graduate students, professionals and researchers in control science and application, system analysis, artificial intelligence, and fault diagnosis.

The 58 revised full papers and 27 short papers were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: knowledge graph; knowledge representation; knowledge management for education; knowledge-based systems; and data processing and mining. The papers of the second volume are organized in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning.

The 58 revised full papers and 27 short papers were carefully reviewed and selected from 291 submissions. The papers of the first volume are organized in the following topical sections: knowledge graph; knowledge representation; knowledge management for education; knowledge-based systems; and data processing and mining. The papers of the second volume are organized in the following topical sections: machine learning; recommendation algorithms and systems; social knowledge analysis and management; text mining and document analysis; and deep learning.

This book focuses on pulverized coal particle devolatilization, ignition, alkali metal release behavior, and burnout temperature using several novel optic diagnostic methods on a Hencken multi-flat flame burner. Firstly, it presents a novel multi-filter technique to detect the CH* signal during coal ignition, which can be used to characterize the volatile release and reaction process. It then offers observations on the prevalent transition from heterogeneous ignition to hetero-homogeneous ignition due to ambient temperature based on visible light signal diagnostics. By utilizing the gap between the excitation energies of the gas and particle phases, a new low-intensity laser-induced breakdown spectroscopy (PS-LIBS) is developed to identify the presence of sodium in the particle or gas phase along the combustion process. For the first time, the in-situ verification of the gas phase Na release accompanying coal devolatilization is fulfilled when the ambient temperature is high enough.In fact, particle temperature plays a vital role in the coal burnout process and ash particle formation. The last part of the book uses RGB color pyrometry and the CBK model to study the char particle temperature on a Hencken burner. It offers readers valuable information on the technique of coal ignition and combustion diagnostics as well as coal combustion characteristics.

This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications.

The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed.