Cointegration and the Empirical Mode Decomposition for the Analysis of Diagnostic Data

2013 ◽  
Vol 569-570 ◽  
pp. 884-891 ◽  
Author(s):  
Ifigeneia Antoniadou ◽  
Elizabeth J. Cross ◽  
Keith Worden
Keyword(s):  
Empirical Mode Decomposition ◽  
Time Scales ◽  
Energy Operator ◽  
Frequency Separation ◽  
Operational Conditions ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Detection Approach ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

The use of cointegration has been proposed recently as a potentially powerful means of removing confounding influences from structural health monitoring (SHM) data. On the other hand the Empirical Mode Decomposition method is a recent multi-scale decomposition technique with the ability to decompose a signal into meaningful signal components. In this paper the EMD method will be used to highlight the dominant time-scales that have been affected by varying environmental and operational conditions and the time-scales that are related to damage. Then cointegration will be used to remove the nonstationary effects not associated with damage at the time-scales of interest in the data. The final step of the damage detection approach proposed, will be the use of two different amplitude-frequency separation methods, the Hilbert Transform and the more recent Teager Kaiser energy operator approach in order to compare the merits of both, concerning the estimation of the instantaneous characteristics of the signals.

Application of the Empirical Mode Decomposition Method to the Identification of Disc Brake Squeal

2002 ◽  
Author(s):  
Fulun Yang ◽  
Chin An Tan ◽  
Frank Chen
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Disc Brake ◽  
Intrinsic Mode Functions ◽  
Brake Squeal ◽  
Mode Decomposition ◽  
Out Of Plane ◽  
Empirical Mode Decomposition Method ◽  
Original Application ◽  
The Hilbert Transform

This paper investigates the identification of mechanisms of disc brake squeal by the application of a recently developed Empirical Mode Decomposition method (EMD). A known strength of the EMD is its adaptive nature in analyzing nonstationary data, with success in its original application to ocean mechanics. The EMD decomposes an original signal into a number of intrinsic mode functions (IMFs), with each IMF often containing distinct physical significance. Several sets of disc brake squeal data were obtained and processed by EMD. A typical set data is presented in this paper for discussion. Employing a sifting process in the EMD, four prominent squeal-related IMFs are identified in this set of data. The Hilbert transform is then used to analyze the frequency and amplitude contents of the four IMFs, and it is shown that the first IMF is dominant. The spectrogram method is applied to analyze the time-evolution of the frequency components of the IMFs in the squeal process. This analysis procedure confirms an important squeal mechanism, i.e., the squeal condition is governed by the coupling of in-plane and out-of-plane vibration modes of the rotor and the coalescence of their natural frequencies. The inverse approach outlined in this paper is shown to be useful for providing new insights and confirming established hypotheses of disc brake squeal.

scholarly journals A VARIANT OF THE EMD METHOD FOR MULTI-SCALE DATA

2009 ◽  
Vol 01 (04) ◽  
pp. 483-516 ◽  
Author(s):  
THOMAS Y. HOU ◽  
MIKE P. YAN ◽  
ZHAOHUA WU
Keyword(s):  
Empirical Mode Decomposition ◽  
Decomposition Method ◽  
Separation Property ◽  
Intrinsic Mode Functions ◽  
Scale Separation ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Emd Method ◽  
Scale Data

In this paper, we propose a variant of the Empirical Mode Decomposition method to decompose multiscale data into their intrinsic mode functions. Under the assumption that the multiscale data satisfy certain scale separation property, we show that the proposed method can extract the intrinsic mode functions accurately and uniquely.

Identification of Linear Time-Varying Dynamical Systems Using Hilbert Transform and Empirical Mode Decomposition Method

2006 ◽  
Vol 74 (2) ◽  
pp. 223-230 ◽  
Author(s):  
Z. Y. Shi ◽  
S. S. Law
Keyword(s):  
Dynamical Systems ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Decomposition Method ◽  
Degrees Of Freedom ◽  
Linear Time ◽  
Time Varying ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
The Hilbert Transform

This paper addresses the identification of linear time-varying multi-degrees-of-freedom systems. The identification approach is based on the Hilbert transform and the empirical mode decomposition method with free vibration response signals. Three-different types of time-varying systems, i.e., smoothly varying, periodically varying, and abruptly varying stiffness and damping of a linear time-varying system, are studied. Numerical simulations demonstrate the effectiveness and accuracy of the proposed method with single- and multi-degrees-of-freedom dynamical systems.

A Method of Fault Detection and Diagnosis Based on Time-Frequency Analysis

2012 ◽  
Vol 490-495 ◽  
pp. 1407-1410
Author(s):  
Ying Bo Liang ◽  
Li Hong Zhang ◽  
Jin Li
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Maximum Modulus ◽  
Fault Detection And Diagnosis ◽  
Time Frequency ◽  
Multi Scale ◽  
Mode Function ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Detection And Diagnosis

In the paper the authors propose a combination of the EMD (empirical mode decomposition)method and the wavelet analysis to suppress the noise and fault detection and diagnosis, It adopts empirical mode decomposition to current signal ,obtained a series of IMFs(Intrinsic Mode Function),removing the first IMF component to denosing,and then analyzed multi-scale ,using signal become mutated have the maximum modulus determine the time that the failure appeared ,the results show that this method determine the time that the failure appeared.

Engineering Surface Analysis by Bidimensional Empirical Mode Decomposition

2013 ◽  
Vol 694-697 ◽  
pp. 2823-2828 ◽  
Author(s):  
Zheng Kai Zhang ◽  
Li Chen Gu ◽  
Yong Sheng Zhu
Keyword(s):  
Empirical Mode Decomposition ◽  
Friction And Wear ◽  
Intrinsic Mode Functions ◽  
Wear Properties ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Empirical Mode Decomposition Method ◽  
Friction And Wear Properties ◽  
Bidimensional Empirical Mode Decomposition

It is well known that an engineering surface is composed of a large number of wavelengths of roughness that are superimposed on each other. Because these multi-scale features are related to different aspects of the processes the surface has undergone and closely related to the friction and wear properties of a surface, the analysis and characterization of these features becomes an important aspect of manufacture. The challenge is how to use them for acquiring knowledge and for aid to analysis. In this paper, a method for surface topography analysis is proposed based on bidimensional empirical mode decomposition (BEMD), which can provide good adaptive separation of surface texture into multiple hierarchical components known as bidimensional intrinsic mode functions (BIMFs). Applications are conducted by using a simulated surfaces to demonstrate the feasibility and applicability of using the bidimensional empirical mode decomposition method in the analysis of engineering surfaces.

scholarly journals Scale-dependent intrinsic entropies of complex time series

2016 ◽  
Vol 374 (2065) ◽  
pp. 20150204 ◽  
Author(s):  
Jia-Rong Yeh ◽  
Chung-Kang Peng ◽  
Norden E. Huang
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Spatial Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Complex Time ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Simulated Time ◽  
Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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