Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation

Chen, Liang; Li, Xing; Li, Xunbo; Huang, Zhiyong

doi:10.1063/1.3082021

Cited by 28 publications

(9 citation statements)

References 10 publications

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“…EEMD was also widely used for signal processing. For example, reconstruction from selected IMFs was used for the evaluation of pipelines utilizing the magnetic flux leakage (MFL) technique [14]. EEMD was also been used to simulate cardio-respiratory signals in order to measure cardiac stroke volume.…”

Section: Introductionmentioning

confidence: 99%

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Chang

2010

Sensors

172

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A novel noise filtering algorithm based on ensemble empirical mode decomposition (EEMD) is proposed to remove artifacts in electrocardiogram (ECG) traces. Three noise patterns with different power—50 Hz, EMG, and base line wander – were embedded into simulated and real ECG signals. Traditional IIR filter, Wiener filter, empirical mode decomposition (EMD) and EEMD were used to compare filtering performance. Mean square error between clean and filtered ECGs was used as filtering performance indexes. Results showed that high noise reduction is the major advantage of the EEMD based filter, especially on arrhythmia ECGs.

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Section: Introductionmentioning

confidence: 99%

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Chang

2010

Sensors

172

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“…This is different from the work of others [4][5][6][7][8][9], as they focused on applying EMD to distinguish different machinery fault patterns, while the goal of our study is to realize online health description with a probabilistic scheme based on EMD and the Hidden Markov Model (HMM).…”

Section: Introductionmentioning

confidence: 87%

“…What is more, the generated IMFs are stationary. Due to this potential in signal processing, a lot of research has been conducted recently [4][5][6][7][8][9]. For example, Fan and Zuo [4] employed EMD to decompose raw vibration signals into IMFs, which is a selfadaptive program to detect machine faults at the earliest onset of deterioration.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Fan and Zuo [4] employed EMD to decompose raw vibration signals into IMFs, which is a selfadaptive program to detect machine faults at the earliest onset of deterioration. Chen et al [5] presented an ensemble empirical mode decomposition method in pipeline corrosion inspection. Dong et al [6] improved the sifting process of EMD for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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A probabilistic description scheme for rotating machinery health evaluation

2010

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Condition-based maintenance has become more popular in recent years because of its advantages in terms of minimizing downtime, extending lifetime, and reducing cost. This kind of maintenance strategy is based on condition monitoring of machinery in operation. Ccondition monitoring is a key step in maintenance decision analysis. Numerous non-stationary signal processing methods have been developed to reveal fault characteristics in rotating machinery. In this study, an adaptive signal analysis method called empirical mode decomposition is employed for gearbox vibration signal preprocessing. Considering a modulation phenomenon that appeared in a faulty gear, the Hilbert Transform is applied to obtain an envelope signature, which usually contains abundant fault-related signatures. Being different from other failure classification problems, this paper is concerned with determining the probability of normal condition based on current observations describing the condition of a gearbox. Moreover, according to Bayes rule, this problem can be translated to estimate the conditional probability of current observations given normal gearbox condition using a Hidden Markov Model. From this point, a novel probabilistic health description index called Average Probability Index is proposed for gearbox health evaluation. For automatic detection, a semi-dynamic threshold is presented to detect an early fault in a gear. At last, validation and comparative studies are performed using two sets of gearbox lifetime accelerated testing vibration data. The results show the advantages of the proposed method for gearbox condition monitoring.

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“…In various signal processing applications, both EMD and EEMD have been implemented for feature extraction and noise reduction [12,13]. Especially for remote sensing images, 2D-EMD [14][15][16] and MEEMD [11] have been proposed recently for the decomposition of hyperspectral image into IMFs, but they apply to pre-selected two-dimensional image band instead of one-dimensional spectral information.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

et al. 2014

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This study proposed a new approach to measure the similarity between spectra to discriminate materials and evaluate the performance of parameter-selection procedures. Many pure pixel vector-based similarity measurements have been developed in the past to calculate the distance between two pixel vectors. However, those methods may not be effective since they do not take full advantage of the spectral correlation. In this study, we adopt Ensemble Empirical Mode Decomposition (EEMD) to decompose the spectrum into serial components and employ these components to improve the performance of spectral discrimination. Performance evaluation was conducted with several commonly used measurements, and the spectral samples used for experimentation were provided by the spectral library of United States Geological Survey (USGS). The experimental results have demonstrated that EEMD can extract the spectral features more effectively than common spectral similarity measurements, and it better characterizes spectral properties. Our experimental results also suggest general rules for selecting noise standard deviation, the number of iterations for EEMD and the collection of Intrinsic Mode Functions (IMFs) for classification. Finally, since EEMD is a time-consuming algorithm, we also OPEN ACCESS Remote Sens. 2014, 6 2070 implement parallel processing with a Graphics Processing Unit (GPU) to increase the processing speed.

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Signal extraction using ensemble empirical mode decomposition and sparsity in pipeline magnetic flux leakage nondestructive evaluation

Cited by 28 publications

References 10 publications

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

Arrhythmia ECG Noise Reduction by Ensemble Empirical Mode Decomposition

A probabilistic description scheme for rotating machinery health evaluation

Ensemble Empirical Mode Decomposition Parameters Optimization for Spectral Distance Measurement in Hyperspectral Remote Sensing Data

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