Wavelet-Based Multifractal Analysis to Periodic Time Series

被引：2

作者：

Chen, Changzheng ^{[1
]}

Wang, Zhong ^{[1
]}

Gou, Yi ^{[1
]}

Zhao, Xinguang ^{[1
]}

Miao, Hailing ^{[2
,3
]}

机构：

[1] Shenyang Univ Technol, Sch Mech Engn, Shenyang 110023, Peoples R China

[2] Shenyang Univ Technol, Sch Sci, Shenyang 110023, Peoples R China

[3] Xiamen XGMA Machinery Co Ltd, Xiamen 361023, Peoples R China

来源：

JOURNAL OF COMPUTATIONAL AND NONLINEAR DYNAMICS | 2015年 / 10卷 / 01期

基金：

中国国家自然科学基金;

关键词：

periodic time series; multifractal analysis; wavelet transform modulus maxima; singularity spectrum; CRACKED ROTOR; FORMALISM; SIGNALS; TURBULENCE;

D O I：

10.1115/1.4027470

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Many processes are characterized by their oscillating or cyclic time behavior. This holds for rotating machines or alternating currents. The resulting signals are then periodic signals or contain periodic parts. It can be used for fault detection of rotating machines. In this paper, we studied the periodic time series of the superposition of two oscillations from the multifractal point of view. The wavelet transform modulus maxima method was used for the singularity spectrum computations. The results show that the width and the peak position of the singularity spectrum changed significantly when the amplitude, frequency, or the phase difference changed. So, the width and the peak position of the singularity spectrum can be used as a new measure for periodic signals.

引用

页数：4

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