A Deep Learning Framework for Noise Elimination of Partial Discharge Signals-Reference-Cited by-同舟云学术

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A Deep Learning Framework for Noise Elimination of Partial Discharge Signals

Published:2023-09-29 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 2nd International Conference on Industrial Electronics: Developments & Applications (ICIDeA)
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Author:

Kumar Chandan¹,Ganguly Biswarup²,Dey Debangshu³,Chatterjee Saibal¹

Affiliation:

1. National Institute of Technology Mizoram,Electrical Engineering Department,Mizoram,India

2. Meghnad Saha Institute of Technology,Electrical Engineering Department,Kolkata,India

3. Jadavpur University,Electrical Engineering Department,Kolkata,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10294327/10295052/10295214.pdf?arnumber=10295214

Reference25 articles.

1. Optimized Denoising Method for Weak Acoustic Emission Signal in Partial Discharge Detection

2. Novel Wavelet Threshold Denoising Method to Highlight the First Break of Noisy Microseismic Recordings

3. Research on Partial Discharge Signal Denoising Based on Improved Variational Mode Decomposition

4. An Automatic Tool for Partial Discharge De-Noising via Short-Time Fourier Transform and Matrix Factorization

5. Intelligent Fault Diagnosis of Rotating Machines Based on Wavelet Time-Frequency Diagram and Optimized Stacked Denoising Auto-Encoder

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Wavelet-Based Convolutional Neural Network for Denoising Partial Discharge Signals Extracted via Acoustic Emission Sensors;IEEE Sensors Letters;2024-07

2. A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques;Energies;2024-02-28

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