Bingchang Hou – författare

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.- 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- Particularly focuses on 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