Classification of partial discharge EMI conditions using permutation entropy-based features

Mitiche, Imene; Morison, Gordon; Nesbitt, A.; Boreham, Philip; Stewart, Brian G.

doi:10.23919/eusipco.2017.8081434

Cited by 7 publications

(4 citation statements)

References 23 publications

(22 reference statements)

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“…As DispEn needs to neither sort the amplitude values of each embedding vector nor calculate every distance between any two composite delay vectors with embedding dimensions m and

, it is fast [ 9 ]. The good performance of DispEn to distinguish different dynamics of real-time series was also shown in [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 82%

Amplitude- and Fluctuation-Based Dispersion Entropy

Azami

Escudero

2018

Entropy

150

160

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Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel-Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.

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“…As DispEn needs to neither sort the amplitude values of each embedding vector nor calculate every distance between any two composite delay vectors with embedding dimensions m and

, it is fast [ 9 ]. The good performance of DispEn to distinguish different dynamics of real-time series was also shown in [ 22 , 23 , 24 ].…”

Section: Introductionmentioning

confidence: 82%

Amplitude- and Fluctuation-Based Dispersion Entropy

Azami

Escudero

2018

Entropy

150

160

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“…The current literature lacks studies on classification of EMI discharge sources, yet it has been widely exploited for PD measurement since 1980 to the present [1]. Authors in [8] and [9] attempted for the first time feature extraction and machine learning application to EMI signals in order to develop an EMI intelligent system based on experts system. The idea was to classify different EMI discharge sources, using relevant features and Multi-Class Support Vector Machine (MCSVM).…”

Section: Introductionmentioning

confidence: 99%

Deep Residual Neural Network for EMI Event Classification Using Bispectrum Representations

Mitiche

Jenkins

Boreham

et al. 2018

2018 26th European Signal Processing Conference (EUSIPCO)

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This paper presents a novel method for condition monitoring of High Voltage (HV) power plant equipment through analysis of discharge signals. These discharge signals are measured using the Electromagnetic Interference (EMI) method and processed using third order Higher-Order Statistics (HOS) to obtain a Bispectrum representation. By mapping the time-domain signal to a Bispectrum image representations the problem can be approached as an image classification task. This allows for the novel application of a Deep Residual Neural Network (ResNet) to the classification of HV discharge signals. The network is trained on signals into 9 classes and achieves high classification accuracy in each category, improving upon our previous work on this task.

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“…There have been studies on the properties of permutation entropy, including extensions to higher regular domains [6] and irregular domains or graphs [7]. Some modifications of PE consider nonlinear mappings to deal with the differences between the amplitude values [8], [9], or weights in permutation patterns [10]. Previous research also has extended PE to different scales [11], [12], and studied its dependencies with respect to random signals [13] or autoregressive processes [14].…”

Section: Introductionmentioning

confidence: 99%

Multivariate permutation entropy, a Cartesian graph product approach

Fabila-Carrasco

Tan

Escudero

2022

2022 30th European Signal Processing Conference (EUSIPCO)

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Entropy metrics are nonlinear measures to quantify the complexity of time series. Among them, permutation entropy is a commonly used metric due to its robustness and fast computation. Multivariate entropy metrics techniques are needed to analyse data consisting of more than one time series. To this end, we present a multivariate permutation entropy, MPEG, using a graph-based approach. Given a multivariate signal, the algorithm to compute MPEG involves two main steps: 1) we construct an underlying graph G as the Cartesian product of two graphs G1 and G2, where G1 preserves temporal information of each times series and G2 models the relations between different channels; and 2) we consider the multivariate signal as samples defined on the regular graph G and apply the recently introduced permutation entropy for graphs. Our graph-based approach gives the flexibility to consider diverse types of cross channel relationships and signals, and it overcomes with the limitations of current multivariate permutation entropy.

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Classification of partial discharge EMI conditions using permutation entropy-based features

Cited by 7 publications

References 23 publications

Amplitude- and Fluctuation-Based Dispersion Entropy

Amplitude- and Fluctuation-Based Dispersion Entropy

Deep Residual Neural Network for EMI Event Classification Using Bispectrum Representations

Multivariate permutation entropy, a Cartesian graph product approach

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