Sliding Empirical Mode Decomposition

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: 91%

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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“…A truly monotonous residue can be obtained through being repeatedly down-sampled and subsequently decomposed by eSLMD. Note that a similar approach was described in [9].…”

Section: Simulation and Analysismentioning

confidence: 99%

On-line Local Mean Decomposition and its application to ECG signal denoising

Hsueh

Chien

2014

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

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Local Mean Decomposition (LMD) has long been proven as an effective method for the analysis of non-linear and non-stationary time series. In this work, an on-line version of LMD, called extended Sliding Local Mean Decomposition (eSLMD), is proposed. The property of eSLMD is examined through numerical simulations, and the performance is evaluated through the ECG noise removal with the test signal obtained from MIT-BIH arrhythmia ECG database. The results show that the proposed eSLMD has better decomposition performance than conventional LMD, and is potentially well suited for on-line and real-time biomedical applications.

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“…This is an especially serious problem with biomedical time series which often are recorded over very long time spans. SEMD [7] offers a robust and easy-to-implement solution to this problem.…”

Section: A Recent Extensions Of Emdmentioning

confidence: 99%

Brain status data analyzed by Empirical Mode Decomposition

Zeiler

Faltermeier

Keck

et al. 2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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Due to external stimuli, biomedical signals are in general non-linear and non-stationary. Intelligent signal processing is crucial to unravel the information content buried in biomedical time series. Empirical Mode Decomposition is ideally suited to extract all pure oscillatory modes which are contained in the signal. These modes, called Intrinsic Mode Functions (IMFs), represent a complete set of locally orthogonal basis functions with time-varying amplitude and frequency. The contribution discusses the application of an online variant, called SEMD, to non-stationary biomedical time series recorded during neuromonitoring.

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Sliding Empirical Mode Decomposition

Cited by 21 publications

References 8 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

On-line Local Mean Decomposition and its application to ECG signal denoising

Brain status data analyzed by Empirical Mode Decomposition

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