A Criterion for Selecting Relevant Intrinsic Mode Functions in Empirical Mode Decomposition

Ayenu-Prah, Albert Y.; Attoh-Okine, Nii

doi:10.1142/s1793536910000367

Cited by 116 publications

(55 citation statements)

References 12 publications

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“…According to improved CEEMDAN and the correlation coefficient, the signal reconstitution method can be concluded by employing the following steps.

The original signal is decomposed into IMF i ( i = 1,2,…, n ) by using the improved CEEMDAN algorithm, and n is the number of IMFs.

All the correlative coefficient value between IMF i and the original signal is calculated using formula (9). The sensitive IMFs are selected according to the correlative coefficient threshold [30], which is shown in formula (10). $\begin{matrix} T1 \\ μ_{h} = \frac{\max (μ_{i})}{10 \times \max (μ_{i}) - 3} (i = 1,2, \dots, n) . \end{matrix}$

In the formula above, μ i represents the correlative coefficient between IMF i and the original signal, and the maximum number of correlative coefficient is denoted by max⁡( μ i ).

If the correlative coefficient value between IMF i and the original signal is larger than μ h , then the relevant IMF is maintained as the sensitive mode.

…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

2016

Computational and Mathematical Methods in Medicine

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Diabetes is a serious threat to human health. Thus, research on noninvasive blood glucose detection has become crucial locally and abroad. Near-infrared transmission spectroscopy has important applications in noninvasive glucose detection. Extracting useful information and selecting appropriate modeling methods can improve the robustness and accuracy of models for predicting blood glucose concentrations. Therefore, an improved signal reconstruction and calibration modeling method is proposed in this study. On the basis of improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and correlative coefficient, the sensitive intrinsic mode functions are selected to reconstruct spectroscopy signals for developing the calibration model using the support vector regression (SVR) method. The radial basis function kernel is selected for SVR, and three parameters, namely, insensitive loss coefficient ε, penalty parameter C, and width coefficient γ, are identified beforehand for the corresponding model. Particle swarm optimization (PSO) is employed to optimize the simultaneous selection of the three parameters. Results of the comparison experiments using PSO-SVR and partial least squares show that the proposed signal reconstitution method is feasible and can eliminate noise in spectroscopy signals. The prediction accuracy of model using PSO-SVR method is also found to be better than that of other methods for near-infrared noninvasive glucose detection.

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“…According to improved CEEMDAN and the correlation coefficient, the signal reconstitution method can be concluded by employing the following steps.

The original signal is decomposed into IMF i ( i = 1,2,…, n ) by using the improved CEEMDAN algorithm, and n is the number of IMFs.

In the formula above, μ i represents the correlative coefficient between IMF i and the original signal, and the maximum number of correlative coefficient is denoted by max⁡( μ i ).

If the correlative coefficient value between IMF i and the original signal is larger than μ h , then the relevant IMF is maintained as the sensitive mode.

…”

Section: Methodsmentioning

confidence: 99%

“…The sensitive IMFs are selected according to the correlative coefficient threshold [30], which is shown in formula (10).

\begin{matrix} T1 \\ μ_{h} = \frac{\max (μ_{i})}{10 \times \max (μ_{i}) - 3} (i = 1,2, \dots, n) . \end{matrix}

…”

Section: Methodsmentioning

confidence: 99%

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

2016

Computational and Mathematical Methods in Medicine

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“…Thus, the original signal is decomposed into multiple IMFs, and a single residue which is monotonously increasing or decreasing. Reference [45] mentions that the original signal is expected to have better correlation with relevant IMFs than with the irrelevant IMFs. Generally, these irrelevant IMFs are formed due to interpolation related numerical errors.…”

Section: Algorithm 1: Sifting Process For Emdmentioning

confidence: 99%

Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

et al. 2016

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Intelligent fault diagnosis of machines for early recognition of faults saves industry from heavy losses occurring due to machine breakdowns. This paper proposes a process with a generic data mining model that can be used for developing acoustic signal-based fault diagnosis systems for reciprocating air compressors. The process includes details of data acquisition, sensitive position analysis for deciding suitable sensor locations, signal pre-processing, feature extraction, feature selection, and a classification approach. This process was validated by developing a real time fault diagnosis system on a reciprocating type air compressor having 8 designated states, including one healthy state, and 7 faulty states. The system was able to accurately detect all the faults by analyzing acoustic recordings taken from just a single position. Additionally, thorough analysis has been presented where performance of the system is compared while varying feature selection techniques, the number of selected features, and multiclass decomposition algorithms meant for binary classifiers.Index Terms-Acoustic fault diagnosis, air compressor, fault diagnosis, feature extraction, feature selection.

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“…In this figure, the abscissa mark Ai (i = 1, 2, …, 7) indicates IMFi (i = 1, 2, …, 7) and the original signal. The normal and damage MI thresholds are calculated according to literature [19], with threshold values of 0.1452 and 0.0711, respectively. In addition, Figure 7 shows that the MI value between IMF1 and IMF4 and the original signal under normal state is higher than the threshold of 0.1452, hence, IMF1 and IMF4 are determined as sensitive IMFs.…”

Section: Feature Extraction Of Two Kinds Of Signalsmentioning

confidence: 99%

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

Long

Liu

2015

Entropy

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Abstract:The randomness and fuzziness that exist in rolling bearings when faults occur result in uncertainty in acquisition signals and reduce the accuracy of signal feature extraction. To solve this problem, this study proposes a new method in which cloud model characteristic entropy (CMCE) is set as the signal characteristic eigenvalue. This approach can overcome the disadvantages of traditional entropy complexity in parameter selection when solving uncertainty problems. First, the acoustic emission signals under normal and damage rolling bearing states collected from the experiments are decomposed via ensemble empirical mode decomposition. The mutual information method is then used to select the sensitive intrinsic mode functions that can reflect signal characteristics to reconstruct the signal and eliminate noise interference. Subsequently, CMCE is set as the eigenvalue of the reconstructed signal. Finally, through the comparison of experiments between sample entropy, root mean square and CMCE, the results show that CMCE can better represent the characteristic information of the fault signal.

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A Criterion for Selecting Relevant Intrinsic Mode Functions in Empirical Mode Decomposition

Cited by 116 publications

References 12 publications

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

Intelligent Condition Based Monitoring Using Acoustic Signals for Air Compressors

Feature Extraction Method of Rolling Bearing Fault Signal Based on EEMD and Cloud Model Characteristic Entropy

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