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.
Table of Contents
1. Introduction and background2. Basic signal processing transforms and analysis
3. Newly advanced sparsity measures for fault signature quantification
4. Classic and advanced sparsity measures-based signal processing technologies
5. Sparsity measures data-driven framework based signal processing technologies
6. Outlook References