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Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network

Dong Li et al.

International Journal of Innovation and Sustainable Development2026https://doi.org/10.1504/ijisd.2026.152281article
AJG 1
Weight
0.50

What the paper says

This paper combines sparse representation with deep autoencoder (DAE) to propose a method for power transformer sound modelling and fault diagnosis.When a power transformer malfunctions, it is usually accompanied by abnormal sounds.During the operation of transformers, the sound signals generated by structural vibrations such as iron cores and windings contain rich equipment status information.Accurate detection and analysis of sound signals can achieve mechanical fault diagnosis of transformers.Traditional detection methods are no longer able to meet the constantly changing needs of the electrical power system (EPS).This paper constructs a fault sound recognition model that combines sparse representation and DAE.This model optimises the encoding process by introducing sparse constraints, thereby enhancing the ability to extract key fault signal features.The results indicate that the model has advantages in effective feature extraction, diagnostic speed, and fault diagnosis accuracy.

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https://doi.org/https://doi.org/10.1504/ijisd.2026.152281

Or copy a formatted citation

@article{dong2026,
  title        = {{Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network}},
  author       = {Dong Li et al.},
  journal      = {International Journal of Innovation and Sustainable Development},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijisd.2026.152281},
}

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Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder network

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.