Extending the scope of empirical mode decomposition by smoothing

Ultrasonic guided waves have been extensively applied for non-destructive testing of plate-like structures particularly pipes in past two decades. In this regard, if a structure has a simple geometry, obtained guided waves' signals are easy to explain. However, any small degree of complexity in the geometry such as contacting with other materials may cause an extra amount of complication in the interpretation of guided wave signals. The problem deepens if defects have irregular shapes such as natural corrosion. Signal processing techniques that have been proposed for guided wave signals' analysis are generally good for simple signals obtained in a highly controlled experimental environment. In fact, guided wave signals in a real situation such as the existence of natural corrosion in wall-covered pipes are much more complicated. Considering pipes in residential buildings that pass through concrete walls, in this paper we introduced Smooth Empirical Mode Decomposition (SEMD) to efficiently separate overlapped guided waves. As empirical mode decomposition (EMD) which is a good candidate for analyzing non-stationary signals, suffers from some shortcomings, wavelet transform was adopted in the sifting stage of EMD to improve its outcome in SEMD. However, selection of mother wavelet that suits best for our purpose plays an important role. Since in guided wave inspection, the incident waves are well known and are usually tone-burst signals, we tailored a complex tone-burst signal to be used as our mother wavelet. In the sifting stage of EMD, wavelet de-noising was applied to eliminate unwanted frequency components from each IMF. SEMD greatly enhances the performance of EMD in guided wave analysis for highly contaminated signals. In our experiment on concrete covered pipes with natural corrosion, this method not only separates the concrete wall indication clearly in time domain signal, a natural corrosion with complex geometry that was hidden and located inside the concrete section was successfully exposed.

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“…It is worthy of mention that this method is different than some works with a similar names reported in [44,45,46,47]. …”

Section: Smooth Empirical Mode Decomposition (Semd)mentioning

confidence: 90%

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes

Rostami

Chen

Tse

2017

Sensors

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“…Wu and Huang [18] developed the ensemble EMD (EEMD) by averaging out the noise from the simulated signals. Kim et al [19] introduced the statistical EMD that uses smoothing of local extrema instead of interpolation. More recently, Komaty et al [20] suggested a signal-filtering approach based on a combination of EMD and a similarity measure for noise removal.…”

Section: Outlinementioning

confidence: 99%

Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals

Park

Kim

2015

IEEE Trans. Instrum. Meas.

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The main goal of this paper is to propose a new approach of empirical mode decomposition (EMD) that analyzes noisy signals efficiently. The EMD has been widely used to decompose nonlinear and nonstationary signals into some components according to intrinsic frequency called intrinsic mode functions. However, the conventional EMD may not be efficient in decomposing signals that are contaminated by noninformative noises or outliers. This paper presents a new EMD procedure that analyzes noisy signals effectively and is robust to outliers with holding the merits of the conventional EMD. The key ingredient of the proposed method is to apply a quantile smoothing method to a noisy signal itself instead of interpolating local extrema of the signal when constructing its mean envelope. Through simulation studies and texture image analysis, it is demonstrated that the proposed method produces substantially effective results.Index Terms-Empirical mode decomposition (EMD), intrinsic mode functions (IMFs), mean envelope, noisy signals, outliers, quantile smoothing.

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“…It represents a signal by a linear combination of wavelet basis functions. Its decomposition results for nonlinear data can be misleading (Huang and Wu, 2008;Kim et al, 2012). Furthermore, wavelet analysis suffers from its nonadaptive nature as it applies the same type of basis functions to the entire range of data.…”

Section: Introductionmentioning

confidence: 99%

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

Duan

Huang

2016

JZUS-A

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Abstract:Accurate wave forecasting with a couple of hours of warning time offers improvements in safety for maritime operation-related activities. Autoregressive (AR) model is an efficient and highly adaptive approach for wave forecasting. However, it is based on linear and stationary theory and hence has limitations in forecasting nonlinear and non-stationary waves. Inspired by the capability of empirical mode decomposition (EMD) technique in handling nonlinear and non-stationary signals, this paper describes the development of a hybrid EMD-AR model for nonlinear and non-stationary wave forecasting. The EMD-AR model was developed by coupling an AR model with the EMD technique. Nonlinearity and non-stationarity were overcome by decomposing the wave time series into several simple components for which the AR model is suitable. The EMD-AR model was implemented using measured significant wave height data from the National Data Buoy Center, USA. Prediction results from various locations consistently show that the hybrid EMD-AR model is superior to the AR model. This demonstrates that the EMD technique is effective in processing nonlinear and non-stationary waves.

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Extending the scope of empirical mode decomposition by smoothing

Cited by 31 publications

References 22 publications

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes

A Signal Processing Approach with a Smooth Empirical Mode Decomposition to Reveal Hidden Trace of Corrosion in Highly Contaminated Guided Wave Signals for Concrete-Covered Pipes

Quantile-Based Empirical Mode Decomposition: An Efficient Way to Decompose Noisy Signals

A hybrid EMD-AR model for nonlinear and non-stationary wave forecasting

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