Wavelets: Theory and Applications for Manufacturing presents a systematic yet easily accessible description of the fundamentals of wavelet transform and its applications in manufacturing. Given the widespread utilization of machine tools in modern manufacturing and the increasing need for minimizing unexpected machine down-time to ensure reliable, economical, and high quality production, it is of critical importance to continually advance the science base for machine condition monitoring, fault diagnosis, and remaining service life prognosis. The adaptive, multi-resolution capability of the wavelet transform has made it a powerful mathematical tool for accomplishing such goals. In addition, this volume also:.Provides a historical overview of the evolution of signal processing techniques, from the Fourier transform to wavelet transform.Introduces the fundamental mathematics for understanding what wavelet transform is and does, and how to apply it to problems typically encountered in manufacturing.Discusses the integration of wavelet transforms with other techniques, such as signal enveloping and neural networks, for enhanced machine defect detection and severity classification.Demonstrates how to select an appropriate base wavelet or custom design a wavelet for optimal performance in signal analysisFocusing on wavelet transform as a tool specifically applied to and designed for manufacturing, Wavelets: Theory and Applications for Manufacturing presents material appropriate for both academic researchers and practicing engineers working in the field of manufacturing.

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.