Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method

Abdelkader, Rabah; Kaddour, Abdelhafid; Derouiche, Z.

doi:10.1007/s00170-018-2167-7

Cited by 61 publications

(27 citation statements)

References 17 publications

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“…It shows that the application of the improved AP method is effective for high-precision and high-demand equipment fault diagnosis in helicopter transmission systems. This can solve the difficult problem of precise equipment fault diagnosis in the helicopter transmission system, and this application can also be extended to the fault diagnosis of other mechanical equipment transmission systems [10][11][12][13][14].…”

Section: Figure 8: Clustering Effect Of Multi-fault Diagnosis Based Omentioning

confidence: 99%

Application of Data Mining-Based Affinity Propagation Clustering Algorithm for Diagnosis of Mechanical Equipment Transmission System

2020

IJOMAM

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Section: Figure 8: Clustering Effect Of Multi-fault Diagnosis Based Omentioning

confidence: 99%

Application of Data Mining-Based Affinity Propagation Clustering Algorithm for Diagnosis of Mechanical Equipment Transmission System

2020

IJOMAM

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“…Of the 4 approximations obtained with Equation (4), the one containing the useful information is selected according to the principle of partial reconstruction. There are several options, such as energy-based methods [44], correlation-based methods [45][46][47], probability density function based methods [48], entropy [49], higher order statistics [50], mutual information [51], and mutual information entropy [52]. In this paper, the method described in [47] has been used, selecting as the filtered signal the one among the 4 approximations obtained in Equation (4) that has the highest Pearson correlation coefficient with the signal from the same sector of the normative database.…”

Section: Adaptive Emd Filtermentioning

confidence: 99%

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Santiago

Castillo

García‐Martín

et al. 2019

Sensors

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As multiple sclerosis (MS) usually affects the visual pathway, visual electrophysiological tests can be used to diagnose it. The objective of this paper is to research methods for processing multifocal electroretinogram (mfERG) recordings to improve the capacity to diagnose MS. MfERG recordings from 15 early-stage MS patients without a history of optic neuritis and from 6 control subjects were examined. A normative database was built from the control subject signals. The mfERG recordings were filtered using empirical mode decomposition (EMD). The correlation with the signals in a normative database was used as the classification feature. Using EMD-based filtering and performance correlation, the mean area under the curve (AUC) value was 0.90. The greatest discriminant capacity was obtained in ring 4 and in the inferior nasal quadrant (AUC values of 0.96 and 0.94, respectively). Our results suggest that the combination of filtering mfERG recordings using EMD and calculating the correlation with a normative database would make mfERG waveform analysis applicable to assessment of multiple sclerosis in early-stage patients. Author Contributions: Conceptualization, E.G.-M., M.J.R., E.M.S.M., and L.B.; methodology, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; software, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; validation, L.d.S., M.O.d.C., C.C., J.M.M., and A.L.; formal Analysis, M.O.d.C. and J.M.M.; investigation, E.G.-M., M.J.R., E.M.S.M., and L.B.; resources, L.B. and E.G.-M.; data Curation, L.d.S., M.O.d.C., C.C., J.M.M., A.L., and B.C.; writing-original draft preparation, L.B., E.G.-M., and M.J.R.; writing-review and editing, L.B., E.G.-M., and M.J.R.; visualization, L.B. and E.G.-M.; supervision, L.B. and E.G.-M.; project administration, L.B. and E.G.-M.; funding acquisition, L.B. and E.G.-M. All authors have read and agreed to the published version of the manuscript.

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“…With timefrequency domain analysis methods, the evolvement of the local frequency components can be obtained [18] [19]. There are many time-frequency domain analysis methods, such as wavelet analysis [20] [21] [22], empirical mode decomposition [23] [24], ReliefF algorithm [25] and other methods. Fault identification is also called fault pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

2020

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Enhancement of rolling bearing fault diagnosis based on improvement of empirical mode decomposition denoising method

Cited by 61 publications

References 17 publications

Application of Data Mining-Based Affinity Propagation Clustering Algorithm for Diagnosis of Mechanical Equipment Transmission System

Application of Data Mining-Based Affinity Propagation Clustering Algorithm for Diagnosis of Mechanical Equipment Transmission System

Empirical Mode Decomposition-Based Filter Applied to Multifocal Electroretinograms in Multiple Sclerosis Diagnosis

Rolling Bearing Fault Diagnosis Based on the Coherent Demodulation Model

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