Sparsity Measures And Their Signal Processing Applications For Machine Condition Monitoring
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Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring presents newly designed sparsity measures and their advanced signal processing technologies for machine condition monitoring and fault diagnosis. This book systematically covers new sparsity measures including a quasiarithmetic mean ratio framework for fault signatures quantification, a generalized Gini index, as well as classic sparsity measures based on signal processing technologies and a cycle-embedded sparsity measure based on new impulsive mode decomposition technology. This book additionally includes a sparsity measure data-driven framework–based optimized weights spectrum theory and its relevant advanced signal processing technologies. - Provides the background, roadmaps and detailed discussion of newly designed sparsity measures and their advanced signal processing technologies for machine condition monitoring and fault diagnosis - Covers new theories, advanced technologies, and the latest contributions in the field of machine condition monitoring and fault diagnosis - Particularly focuses on newly advanced sparsity measures for fault signature quantification, classic and advanced sparsity measures–based signal processing technologies and sparsity measures using data-driven framework–based signal processing technologies - Provides experimental and real-world practical validation cases, including newly advanced sparsity measures and their advanced signal processing technologies
Equipment Intelligent Operation and Maintenance
The proceedings of the First International Conference on Equipment Intelligent Operation and Maintenance (ICEIOM 2023) offer invaluable insights into the processes that ensure safe and reliable operation of equipment and guarantee the improvement of product life cycles. The book touches upon a wide array of topics including equipment condition monitoring, fault diagnosis, and remaining useful life prediction. With special emphasis on the integration of big data and machine learning, the papers contained in this publication highlight how these technologies make the equipment operation process highly automated and ingenious. Intelligent operation and maintenance is set to act as the driving force behind a new generation of smart manufacturing and equipment upgradation, and promote demand for intelligent product services and management. This is a highly beneficial guide to students, researchers, working professionals and enthusiasts who wish to stay updated on innovative research contributions and practical applications of state-of-the-art technologies in equipment operation and maintenance.
Structural Health Monitoring
This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.