SD2SDFNet: A novel deep fusion network based on statistical denoising and dual-dimension self-attention model for power transformer hybrid-space fault prognosis

Author:

Liu XiaoyanORCID,He Yigang

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference43 articles.

1. A new testing method for the diagnosis of winding faults in transformer;Wu;IEEE Trans. Instrum. Meas.,2020

2. Calculation and analysis of mechanical characteristics of transformer windings under short-circuit condition;Wang;IEEE Trans. Magn.,2019

3. Classification and discrimination among winding mechanical defects, internal and external electrical faults, and inrush current of transformer;Bagheri;IEEE Trans. Ind. Inform.,2017

4. Transformer fault prognosis using deep recurrent neural network over vibration signals;Zollanvari;IEEE Trans. Instrum. Meas.,2020

5. Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals;Jiang;Measurement,2022

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