Signal Nonstationary Degree Evaluation Method Based on Moving Statistics Theory

被引：6

作者：

He, Haoxiang ^{[1
]}

Cheng, Shitao ^{[1
]}

Zhang, Xiaofu ^{[2
]}

机构：

[1] Beijing Univ Technol, Beijing Key Lab Earthquake Engn & Struct Retrofit, Beijing 100124, Peoples R China

[2] China Acad Bldg Res, Beijing 100013, Peoples R China

来源：

SHOCK AND VIBRATION | 2021年 / 2021卷 / 2021期

关键词：

EMPIRICAL MODE DECOMPOSITION; TIME-SERIES; FEATURE-EXTRACTION; SPECTRAL-ANALYSIS;

D O I：

10.1155/2021/5562110

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Nonstationary signal refers to the signal whose statistics change with time, and its nonstationary degree evaluation can provide effective support for the evaluation of the operating state of the signal source. This paper introduced a variety of typical signal global and local nonstationary degree evaluation methods and compared the applicable scope of different evaluation methods. In view of the limitations of the existing evaluation methods in the scope of application, considering the influence of adjacent signal points, this paper proposed the concepts and calculation methods of the moving mean, moving standard deviation, moving variation coefficient, and moving Hurst exponent based on the theory of moving statistics. According to different nonstationary degree evaluation methods, three different fields of signals (sinusoidal signal, mechanical fault signal, and ECG signal) are analyzed. The results show that, compared with the existing nonstationary degree evaluation methods, the signal nonstationary degree evaluation method proposed in this paper can reveal the time-varying details of the nonstationary signals, with high precision and strong stability, and has unique advantages in nonstationary signal processing.

引用

页数：18

共 37 条

[1] A spectral estimation method for nonstationary signals analysis with application to power systems [J].

Adamo, F. ;

Attivissimo, F. ;

Di Nisio, A. ;

Savino, M. ;

Spadavecchia, M. .

MEASUREMENT, 2015, 73 :247-261

[2] Increased nonstationarity of neonatal heart rate before the clinical diagnosis of sepsis [J].

Cao, HQ ;

Lake, DE ;

Griffin, MP ;

Moorman, JR .

ANNALS OF BIOMEDICAL ENGINEERING, 2004, 32 (02) :233-244

[3] Time-dependent Hurst exponent in financial time series [J].

Carbone, A ;

Castelli, G ;

Stanley, HE .

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2004, 344 (1-2) :267-271

[4] FPGA-Based Broken Bars Detection on Induction Motors Under Different Load Using Motor Current Signature Analysis and Mathematical Morphology [J].

de Jesus Rangel-Magdaleno, Jose ;

Peregrina-Barreto, Hayde ;

Manuel Ramirez-Cortes, Juan ;

Gomez-Gil, Pilar ;

Morales-Caporal, Roberto .

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2014, 63 (05) :1032-1040

[5] DISTRIBUTION OF THE ESTIMATORS FOR AUTOREGRESSIVE TIME-SERIES WITH A UNIT ROOT [J].

DICKEY, DA ;

FULLER, WA .

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1979, 74 (366) :427-431

[6] Smooth Break Detection and De-Trending in Unit Root Testing [J].

Emirmahmutoglu, Furkan ;

Omay, Tolga ;

Shahzad, Syed Jawad Hussain ;

Nor, Safwan Mohd .

MATHEMATICS, 2021, 9 (04) :1-25

[7] Acoustic fault analysis of three commutator motors [J].

Glowacz, Adam .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 133

[8] RECOGNITION OF ACOUSTIC SIGNALS OF INDUCTION MOTOR USING FFT, SMOFS-10 AND LSVM [J].

Glowacz, Adam .

EKSPLOATACJA I NIEZAWODNOSC-MAINTENANCE AND RELIABILITY, 2015, 17 (04) :569-574

[9] Efficient Bayesian inference for natural time series using ARFIMA processes [J].

Graves, T. ;

Gramacy, R. B. ;

Franzke, C. L. E. ;

Watkins, N. W. .

NONLINEAR PROCESSES IN GEOPHYSICS, 2015, 22 (06) :679-700

[10] TIME-DEPENDENT ARMA MODELING OF NONSTATIONARY SIGNALS [J].

GRENIER, Y .

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING, 1983, 31 (04) :899-911

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