小波
小波包分解
小波变换
第二代小波变换
平稳小波变换
计算机科学
离散小波变换
模式识别(心理学)
信号(编程语言)
吊装方案
人工智能
语音识别
程序设计语言
作者
Wai Keng Ngui,M. Salman Leong,Lim Meng Hee,Ahmed M. Abdelrhman
出处
期刊:Applied Mechanics and Materials
[Trans Tech Publications, Ltd.]
日期:2013-09-01
卷期号:393: 953-958
被引量:234
标识
DOI:10.4028/www.scientific.net/amm.393.953
摘要
Wavelet analysis, being a popular time-frequency analysis method has been applied in various fields to analyze a wide range of signals covering biological signals, vibration signals, acoustic and ultrasonic signals, to name a few. With the capability to provide both time and frequency domains information, wavelet analysis is mainly for time-frequency analysis of signals, signal compression, signal denoising, singularity analysis and features extraction. The main challenge in using wavelet transform is to select the most optimum mother wavelet for the given tasks, as different mother wavelet applied on to the same signal may produces different results. This paper reviews on the mother wavelet selection methods with particular emphasis on the quantitative approaches. A brief description of the proposed new technique to determine the optimum mother wavelet specifically for machinery faults diagnosis is also presented in this paper.
科研通智能强力驱动
Strongly Powered by AbleSci AI