Sparsity Measures and their Signal Processing Applications for Machine Condition Monitoring

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.

• 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: especially in 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