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.
1. Chapter 1. Introduction and background - p. 1 2. Chapter 2. Basic signal processing transforms and analysis - p. 15 3. Chapter 3. Research on sparsity measures for fault signature quantification - p. 25 4. 3.1 Chapter introduction - p. 26 5. 3.2 Sparsity measures and their six properties - p. 26 6. 3.3 New unified framework for sparsity measures - p. 29 7. 3.4 Construction of new sparsity measures - p. 36 8. 3.5 Exploration of new statistical indices based on the ratio of different quasiarithmetic means - p. 45 9. 3.6 New cycle-embedded sparsity measures for the quantification of repetitive fault transients - p. 56 10. 3.7 Other sparsity-informed measures - p. 68 11. 3.8 Summary - p. 69 12. Chapter 4. Classic and advanced sparsity-measures-based signal processing methods - p. 73 13. Chapter 5. Sparsity-measures-inspired data-driven signal processing techniques - p. 105 14. Chapter 6. Two practical application case studies - p. 137 15. Appendices - p. 153 16. A1 Descriptions of experimental datasets - p. 153 17. A2 Proofs of the six sparsity properties of new sparsity measures - p. 156 18. A3 Appendices for CESM - p. 166 19. Index - p. 173
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.
1. Chapter 1. Introduction and background - p. 1 2. Chapter 2. Basic signal processing transforms and analysis - p. 15 3. Chapter 3. Research on sparsity measures for fault signature quantification - p. 25 4. 3.1 Chapter introduction - p. 26 5. 3.2 Sparsity measures and their six properties - p. 26 6. 3.3 New unified framework for sparsity measures - p. 29 7. 3.4 Construction of new sparsity measures - p. 36 8. 3.5 Exploration of new statistical indices based on the ratio of different quasiarithmetic means - p. 45 9. 3.6 New cycle-embedded sparsity measures for the quantification of repetitive fault transients - p. 56 10. 3.7 Other sparsity-informed measures - p. 68 11. 3.8 Summary - p. 69 12. Chapter 4. Classic and advanced sparsity-measures-based signal processing methods - p. 73 13. Chapter 5. Sparsity-measures-inspired data-driven signal processing techniques - p. 105 14. Chapter 6. Two practical application case studies - p. 137 15. Appendices - p. 153 16. A1 Descriptions of experimental datasets - p. 153 17. A2 Proofs of the six sparsity properties of new sparsity measures - p. 156 18. A3 Appendices for CESM - p. 166 19. Index - p. 173
