Enhanced Partial Discharge Signal Denoising Using Dispersion Entropy Optimized Variational Mode Decomposition.

Ragavesh Dhandapani, Imene Mitiche, Scott McMeekin, Venkateswara Sarma Mallela, Gordon Morison

Abstract read

In one paragraph

Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it

0cells of the map it votes in

3citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

Advanced Signal Processing Methods for Partial Discharge Analysis: A Review.Sensors (Basel, Switzerland) · 2025
Review
Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024
Review
A Dual-Optimization Fault Diagnosis Method for Rolling Bearings Based on Hierarchical Slope Entropy and SVM Synergized with Shark Optimization Algorithm.Sensors (Basel, Switzerland) · 2023
Article

4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Ragavesh DhandapaniDepartment of Electrical and Communication Engineering, College of Engineering, National University of Science & Technology, Seeb P.O. Box 2322, Oman.ORCID 0000-0003-1526-4765

Imene MiticheDepartment of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.ORCID 0000-0001-9169-835X

Scott McMeekinSchool of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.ORCID 0000-0002-1221-1193

Venkateswara Sarma MallelaDepartment of Electrical and Communication Engineering, College of Engineering, National University of Science & Technology, Seeb P.O. Box 2322, Oman.

Gordon MorisonDepartment of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK.ORCID 0000-0002-3282-5248

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a new approach for denoising Partial Discharge (PD) signals using a hybrid algorithm combining the adaptive decomposition technique with Entropy measures and Group-Sparse Total Variation (GSTV). Initially, the Empirical Mode Decomposition (EMD) technique is applied to decompose a noisy sensor data into the Intrinsic Mode Functions (IMFs), Mutual Information (MI) analysis between IMFs is carried out to set the mode length

Indexed as

dispersion entropygroup-sparse total variationmutual information entropypartial discharge denoisingvariational mode decomposition

Identifiers

PMID34945873

DOI10.3390/e23121567

PMCPMC8700104

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.