Empirical mode decomposition: an analytical approach for sifting process

Deléchelle, E.; Lemoine, Jacques; Niang, Oumar

doi:10.1109/lsp.2005.856878

Cited by 154 publications

(98 citation statements)

References 11 publications

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“…Considering that the EMD and the LMD are data-driven analysis methods, they are essentially algorithmic in nature and, hence, suffer from the drawback that there is no well-established analytical formulation on the basis of theoretical analysis and performance evaluation [28]. Accordingly, relevant modifications mainly come from case-by-case comparisons conducted empirically.…”

Section: Connection Between Local Extremamentioning

confidence: 99%

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Guo¹,

Zuo²

2017

Fault Diagnosis and Detection

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Vibration-based condition monitoring and fault diagnosis are becoming more common in the industry to increase machine availability and reliability. Considerable research efforts have recently been directed towards the development of adaptive signal processing methods for fault diagnosis. Two adaptive signal decomposition methods, i.e. the empirical mode decomposition (EMD) and the local mean decomposition (LMD), are widely used. This chapter is intended to summarize the recent developments mostly based on the authors' works. It aims to provide a valuable reference for readers on the processing and analysis of vibration signals collected from rotating machinery.

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Section: Connection Between Local Extremamentioning

confidence: 99%

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Guo¹,

Zuo²

2017

Fault Diagnosis and Detection

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“…Typically, the nonsmooth part is computed as the first IMF with the help of masking and mirror-image signals [27]. The characteristics of the nonsmooth IMF were explored in previous works by relating them to Fourier series expansions of saw-tooth wave signals [41] and also by a partial-differential-equation-based sifting process [40] noting that EMD acts, in essence, as a dyadic filter bank. Figure 7b depicts such a nonsmooth IMF for the acceleration signal in Fig.…”

Section: 13 Imply the Impact Instants Identified From The Impact mentioning

confidence: 99%

Nonlinear system identification of the dynamics of a vibro-impact beam: numerical results

et al. 2012

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We study the dynamics of a cantilever beam with two rigid stops of certain clearances by performing nonlinear system identification (NSI) based on the correspondence between analytical and empirical slow-flow dynamics. The NSI method in this work can proceed in two directions: One for the numerical data obtained from a reduced-order model by means of the assumed-mode method, and the other for the experimental data measured at the same positions as in numerical simulations. This paper focuses on the analysis of the numerical data, providing qualitative comparison with some experimental results; the latter task will be discussed in detail in a companion paper. First, we perform empirical mode decomposition (EMD) on the acceleration responses measured at ten, almost evenly-spaced, spanwise positions along the beam leading to sets of intrinsic modal oscillators governing the vibro-impact dynamics at different time scales. In particular, the EMD analysis can separate any nonsmooth effects caused by vibro-impacts of the beam and the rigid stops from the smooth (elastodynamic) response, so that nonlinear modal interactions caused by vibro-impacts can be explored only with the remaining smooth components. Then, we establish nonlinear interaction models (NIMs) for the respective intrinsic modal oscillators, where the NIMs invoke slowly-varying forcing amplitudes that can be computed from empirical slow-flows. By comparing the spatio-temporal variations of the nonlinear modal interactions for the vibro-impact beam and those of the underlying linear model (i.e., the beam with no rigid constraints), we demonstrate that vibro-impacts significantly influence the lower-frequency modes, introducing spatial modal distortions, whereas the higher frequency modes tend to retain their linear dynamics between impacts. We introduce a linear correlation coefficient as a measure for studying the linear dependency between the slowlyvarying complex forcing amplitudes for the linear and vibro-impact beams and demonstrate that only a set of lower-frequency modes are strongly influenced by vibro-impacts, capturing most of the essential nonlinear dynamics. These results demonstrate the efficacy of the proposed approach to analyze strongly nonlinear measured time series and provide physical insight for strong nonlinear dynamical interactions.

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“…A difference from the Fourier-based signal processing methods is that the IMF is not restricted to be single banded and can be nonstationary. Several EMD algorithms have been developed using the so-called sifting process [104,105].…”

Section: Provides the Best Local Fit Of X(t) Using-time Dependent Funmentioning

confidence: 99%

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

Yun

Reddi

2011

Advances in Civil Engineering

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A nonparametric, data-driven methodology of monitoring for geotechnical structures subject to long-term environmental change is discussed. Avoiding physical assumptions or excessive simplification of the monitored structures, the nonparametric monitoring methodology presented in this paper provides reliable performance-related information particularly when the collection of sensor data is limited. For the validation of the nonparametric methodology, a field case study was performed using a full-scale retaining wall, which had been monitored for three years using three tilt gauges. Using the very limited sensor data, it is demonstrated that important performance-related information, such as drainage performance and sensor damage, could be disentangled from significant daily, seasonal and multiyear environmental variations. Extensive literature review on recent developments of parametric and nonparametric data processing techniques for geotechnical applications is also presented.

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Empirical mode decomposition: an analytical approach for sifting process

Cited by 154 publications

References 11 publications

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Adaptive Signal Decomposition Methods for Vibration Signals of Rotating Machinery

Nonlinear system identification of the dynamics of a vibro-impact beam: numerical results

Nonparametric Monitoring for Geotechnical Structures Subject to Long-Term Environmental Change

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