Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Voltammetric Techniques: Pulse Voltammetry01:17

Voltammetric Techniques: Pulse Voltammetry

1.3K
Differential-pulse voltammetry (DPV) is a type of voltammetry that involves applying a series of voltage pulses to an electrochemical cell while measuring the resulting current. In DPV, the differential pulse or small potential pulses are superimposed on a linear potential sweep. The magnitude of these pulses is typically small, often in the millivolt range. Each voltage pulse lasts a short duration, usually in the order of a few milliseconds, and is applied at regular intervals along the...
1.3K
Double Resonance Techniques: Overview01:12

Double Resonance Techniques: Overview

665
Double resonance techniques in Nuclear Magnetic Resonance (NMR) spectroscopy involve the simultaneous application of two different frequencies or radiofrequency pulses to manipulate and observe two distinct nuclear spins. One important application of double resonance is spin decoupling, which selectively suppresses coupling with one type of nucleus while observing the NMR signal from another nucleus, simplifying the spectrum and enhancing resolution.
Spin decoupling is usually achieved by...
665

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Correction: SIRT7 activates p53 by enhancing PCAF-mediated MDM2 degradation to arrest the cell cycle.

Oncogene·2026
Same author

PHF20 stabilizes the GAS7-F-actin axis to drive DNA damage repair and chemoresistance in cutaneous squamous cell carcinoma.

Cell death & disease·2026
Same author

CT-based radiomics to predict peri-device leakage after left atrial appendage closure.

BMC medical imaging·2026
Same author

High-fidelity image transmission and reconstruction through multimode fiber using OAM modes and deep learning.

Optics express·2026
Same author

Improving the measurement resolution in BOTDR sensor with optimized wavelet denoising strategy.

PloS one·2026
Same author

Preliminary Developmental Safety Assessment of Allylestrenol in Pregnant SD Rats: Evaluation of F0 and Early F1 Generational Endpoints.

Drug design, development and therapy·2026

Related Experiment Video

Updated: Jan 9, 2026

Studying Cavitation Enhanced Therapy
07:36

Studying Cavitation Enhanced Therapy

Published on: April 9, 2021

5.7K

Advanced Signal Processing Methods for Partial Discharge Analysis: A Review.

He Wen1, Mohamad Sofian Abu Talip1, Mohamadariff Othman1

  • 1Department of Electrical Engineering, Faculty of Engineering, Universiti Malaya, Kuala Lumpur 50603, Malaysia.

Sensors (Basel, Switzerland)
|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This review covers advanced signal processing for partial discharge (PD) analysis, comparing traditional and AI methods. Future research needs standardized, explainable AI for accurate, real-time PD classification.

Keywords:
artificial intelligencefault diagnosispartial dischargesignal processingtime-frequency analysis

More Related Videos

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

596
Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces
07:51

Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces

Published on: February 24, 2012

25.1K

Related Experiment Videos

Last Updated: Jan 9, 2026

Studying Cavitation Enhanced Therapy
07:36

Studying Cavitation Enhanced Therapy

Published on: April 9, 2021

5.7K
High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition
05:11

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

Published on: June 27, 2025

596
Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces
07:51

Voltage Biasing, Cyclic Voltammetry, & Electrical Impedance Spectroscopy for Neural Interfaces

Published on: February 24, 2012

25.1K

Area of Science:

  • Electrical Engineering
  • Signal Processing
  • Artificial Intelligence

Background:

  • Partial discharge (PD) analysis is crucial for electrical insulation monitoring.
  • Traditional and advanced signal processing techniques are used for PD detection and classification.
  • Existing methods face challenges with non-stationary and noisy PD signals.

Purpose of the Study:

  • To comprehensively review and compare advanced signal processing methods for PD analysis.
  • To identify the principles, advantages, limitations, and applications of various PD analysis techniques.
  • To highlight the evolution and complementary roles of different methods in PD signal processing.

Main Methods:

  • Review of traditional time-frequency techniques.
  • Analysis of wavelet transform and Hilbert-Huang transform.
  • Examination of artificial intelligence-based methods, including machine learning and deep learning.

Main Results:

  • Systematic comparison of signal processing methods for PD analysis.
  • Evaluation of the strengths and weaknesses of each technique in handling complex PD signals.
  • Identification of a research gap in standardized, explainable, and embeddable AI for real-time PD classification.

Conclusions:

  • Advanced signal processing methods offer improved PD analysis capabilities.
  • Hybrid approaches and edge AI show promise for future PD diagnostic systems.
  • Further development is needed for robust, real-time AI-driven PD classification systems.