 Modelling and diagnosis method of power transformer sound characteristics by integrating sparse representation and deep autoencoder networkDong Li, Kuo Liu, Congcong Guo and Qingqing Gong International Journal of Innovation and Sustainable Development, 2026, vol. 20, issue 7, 18-35 Abstract: 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. Keywords: deep learning; sparse representation; DAE; deep autoencoder network; power transformer; sound characteristics; fault diagnosis. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisde:v:20:y:2026:i:7:p:18-35 Access Statistics for this article More articles in International Journal of Innovation and Sustainable Development from Inderscience Enterprises Ltd |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.