Dispersion Entropy: A Measure for Time-Series Analysis

Rostaghi, Mostafa; Azami, Hamed

doi:10.1109/lsp.2016.2542881

Cited by 558 publications

(511 citation statements)

References 22 publications

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“…This can be observed by listing the new entropybased algorithms that have recently been proposed to improve and extend one of the most well-known irregularity measures, sample entropy (SampEn 1D ) [1]: see, e.g., [2]- [6]. This growing interest is probably due to the ability of the entropy-based algorithms to analyze large sets of signals [3] and also to their ability -when associated with a multiscale approach -to give information on the system's complexity [3], [7].…”

Section: Introductionmentioning

confidence: 99%

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Azami

Escudero

Humeau-Heurtier

2017

IEEE Signal Process. Lett.

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Abstract-Two-dimensional sample entropy (SampEn 2D ) has been recently proposed to quantify the irregularity of textures. However, when dealing with small-sized textures, SampEn 2D may lead to either undefined or unreliable values. Moreover, SampEn 2D is too slow for most real-time applications. To alleviate these deficiencies, we introduce bidimensional distribution entropy (DistrEn 2D ). We evaluate DistrEn 2D on both synthetic and real texture datasets. The results indicate that DistrEn 2D can detect different amounts of white Gaussian and salt and pepper noise, and discriminate periodic from synthesized textures. The results also show that DistrEn 2D distinguishes different kinds of textured surfaces. In addition, DistrEn 2D , unlike SampEn 2D , does not lead to undefined values. Moreover, DistrEn 2D is noticeably faster than SampEn 2D . Overall, DistrEn 2D -as an insensitive feature extraction method to rotation -is expected to be very useful for the analysis of real image textures.

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Section: Introductionmentioning

confidence: 99%

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Azami

Escudero

Humeau-Heurtier

2017

IEEE Signal Process. Lett.

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“…DE was introduced in [19] to overcome (PE) and Sample Entropy (SE) limitations. SE is slow in computation particularly for long time series, whereas PE disregards information of the amplitude values mean and amplitude variations [29].…”

Section: Classification Theorymentioning

confidence: 99%

“…This type of signals exhibit the same behaviour as some of the captured EMI discharge signals, also referred to as events, and this is the main motivation for using the PE-based measure. The solution in this paper introduces a new feature extraction technique called Dispersion Entropy (DE), which is a modified and improved version of PE [19]. Both PE and DE are implemented to extract the features of each IMF signal, which are subsequently fed into a Multi-Class Support Vector Machine (MCSVM) classifier to distinguish between the different EMI events.…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

Mitiche

Morison

Nesbitt

et al. 2018

Sensors

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Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.

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“…DE was developed in [22] to overcome (PE) and Sample Entropy (SE) limitations. SE is claimed to be slow in computation particularly for long time series and PE neglects information of the mean of amplitude values and their variations [23], this PE limitation has also been addressed by considering WPE.…”

Section: B Dispersion Entropymentioning

confidence: 99%

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

Mitiche

Morison

Nesbitt

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we investigate the application of feature extraction and machine learning techniques to fault identification in power systems. Specifically we implement the novel application of Permutation Entropy-based measures known as Weighted Permutation and Dispersion Entropy to field ElectroMagnetic Interference (EMI) signals for classification of discharge sources, also called conditions, such as partial discharge, arcing and corona which arise from various assets of different power sites. This work introduces two main contributions: the application of entropy measures in condition monitoring and the classification of real field EMI captured signals. The two simple and low dimension features are fed to a MultiClass Support Vector Machine for the classification of different discharge sources contained in the EMI signals. Classification was performed to distinguish between the conditions observed within each site and between all sites. Results demonstrate that the proposed approach separated and identified the discharge sources successfully.

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Dispersion Entropy: A Measure for Time-Series Analysis

Cited by 558 publications

References 22 publications

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

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

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