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Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring Wang, Dong (Shanghai Jiao Tong University, China)
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Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring
Wang, Dong (Shanghai Jiao Tong University, China)
Sparsity measures are effective indicators for quantifying the sparsity of data sequences. They are often used for fault feature characterization in condition monitoring and fault diagnosis of rotating machinery. Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring introduces newly designed sparsity measures and their advanced signal processing technologies for machine condition monitoring and fault diagnosis.
The book systematically introduces: (1) new sparsity measures such as quasi-arithmetic mean ratio framework for fault signatures quantification, generalized Gini index, etc.; (2) classic sparsity measures based on signal processing technologies and cycle-embedded sparsity measure based on new impulsive mode decomposition technology; and (3) a sparsity measure data-driven framework based optimized weights spectrum theory and its relevant advanced signal processing technologies.
300 pages
| Mediji | Grāmatas Paperback Book (Grāmata ar mīksto vāku un līmēto muguru) |
| Izlaists | 2025. gada 22. maijs |
| ISBN13 | 9780443334863 |
| Izdevēji | Elsevier - Health Sciences Division |
| Lapas | 184 |
| Izmēri | 150 × 220 × 10 mm · 303 g |
