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Sparsity Measures and Their Signal Processing Applications for Machine Condition Monitoring (e-book) (used book) | bookbook.eu
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Description
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. Newly Designed Sparsity Measures and Advanced Signal Processing Technologies 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.
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- Author: Dong Wang
- Publisher: Elsevier
- ISBN-10: 0443334862
- ISBN-13: 9780443334863
- Format: 16.1 x 22.8 x 0.9 cm, minkšti viršeliai
- Language: English English
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. Newly Designed Sparsity Measures and Advanced Signal Processing Technologies 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.
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