Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

Mitiche, Imene; Morison, Gordon; Hughes-Narborough, Michael; Nesbitt, A.; Boreham, Philip; Stewart, Brian G.

doi:10.1109/ceidp.2017.8257520

Cited by 9 publications

(8 citation statements)

References 28 publications

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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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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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“…Lower classification accuracy is observed in site 1. However, it is clear that an improvement in accuracy is achieved for each case, compared to [2]. An improvement of 8% is achieved for site 1 and 3 and an improvement of 14% and 4% is achieved for sites 2 and the common condition case respectively.…”

Section: Resultsmentioning

confidence: 95%

“…The downsides of an expert’s analysis are the high costs, human time and impracticability for continuous monitoring. In [2] the authors introduced, for the first time, an automatic and continuous condition monitoring solution, based on a pattern recognition approach. The developed model is seen as a transfer of expert knowledge to a software model.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we improve the performance of previous EMI discharge sources classification [2,12,13,14,15], which aims to distinguish between a number of events including PD, Corona (C), Noise (N), Process Noise (PN) etc. Our previous method involves a state-of-the-art signal decomposition technique combined with an entropy measurement technique.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Dispersion entropy (DE) is proposed by Rostaghi and Azami in 2016, which is a nonlinear dynamics method to characterize the complexity and irregularity of the time-series (Rostaghi and Azami 2016;Mitiche et al 2018).…”

Section: Dispersion Entropy (De)mentioning

confidence: 99%

Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

Zhang

Li²,

et al. 2021

Preprint

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Magnetotelluric (MT) data processing can increase the reliability of measured data. Traditional MT de-noising methods are usually filtered in entire MT time-series sequence, which result in losing of useful MT signals and the decrease of imaging accuracy of electromagnetic inversion. However, targeted MT noise separation can retain the part of data not affected by strong noise, and enhance the quality of MT data. Thus, we proposed a novel method for MT noise separation, which using refined composite multiscale dispersion entropy (RCMDE) and orthogonal matching pursuit (OMP). Firstly, the RCMDE characteristic parameters are extracted from each segment of the MT time-series. Then, the characteristic parameters are input to the fuzzy c-mean (FCM) clustering for automatic identification of MT signal and noise. Next, OMP method is utilized to remove the identified noise segments independently. Finally, the reconstructed signal consists of the denoised data segments and the identified useful signal segments. We conducted the simulation experiments and algorithm evaluation on the EMTF data, simulated data and measured sites. The results indicate that the RCMDE can improve the stability of multiscale dispersion entropy (MDE) and multiscale entropy (MSE) by analyzing the characteristics of the signal samples library, effectively dividing MT signals and noise. Compared with the existing techniques of the entire time domain de-noising and signal-noise identification, the proposed method used RCMDE and OMP as characteristic parameter and noise separation, simplified the multi-features fusion, and improved the accuracy of signal-noise identification. Moreover, the de-noising efficiency has accelerated, and the MT data quality of low-frequency band has improved greatly.

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Classification of partial discharge signals by combining adaptive local iterative filtering and entropy features

Cited by 9 publications

References 28 publications

Deep Residual Neural Network for EMI Event Classification Using Bispectrum Representations

Deep Residual Neural Network for EMI Event Classification Using Bispectrum Representations

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

Separation of magnetotelluric signals based on refined composite multiscale dispersion entropy and orthogonal matching pursuit

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