Wavelets : theory and applications for manufacturing
著者
書誌事項
Wavelets : theory and applications for manufacturing
Springer, c2011
- : hbk
- : pbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Wavelets: Theory and Applications for Manufacturing presents a systematic description of the fundamentals of wavelet transform and its applications. Given the widespread utilization of rotating machines in modern manufacturing and the increasing need for condition-based, as opposed to fix-interval, intelligent maintenance to minimize machine down time and ensure reliable production, it is of critical importance to advance the science base of signal processing in manufacturing. This volume also deals with condition monitoring and health diagnosis of rotating machine components and systems, such as bearings, spindles, and gearboxes, while also:
-Providing a comprehensive survey on wavelets specifically related to problems encountered in manufacturing
-Discussing the integration of wavelet transforms with other soft computing techniques such as fuzzy logic, for machine defect and severity classification
-Showing how to custom design wavelets for improved performance in signal analysis
Focusing on wavelet transform as a tool specifically applied and designed for applications in manufacturing, Wavelets: Theory and Applications for Manufacturing presents material appropriate for both academic researchers and practicing engineers working in the field of manufacturing.
目次
Why wavelets.- From fourier transform to wavelet transform.- Wavelet integrated with fourier transform: a spectral post-processing technique.- Wavelet-based multi-sensor data fusion.- Integration of wavelet with fuzzy logic for machine defect severity classification.- Wavelet-based multi-fractal singularity spectrum.- Wavelet-based ultrasonic pulse detection and differentiation.- Wavelet-based multi-scale enveloping spectrogram.- Wavelet packet decomposition for cross-term interference suppression in wigner-ville distribution.- Optimal wavelet packet transform for discriminable feature extraction.- Wavelet selection criteria.- Customized wavelet design.
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