基于声纹数据标准化的变压器质量缺陷检测研究OA

[Objective]To address the problem of the inconsistent data quality and weak model generalization,this study aims to investigate acoustic data standardization methods and construct a deep learning-based quality detection model to support non-destructive testing and intelligent maintenance of power transformers.[Methods]By analyzing the characteristics of transformer acoustic signals and the bottlenecks in defect detection,a standardized process covering signal acquisition,noise reduction,and feature extraction is established to improve data quality and consistency.A deep learning model based on a CNN-Transformer hybrid architecture is introduced to identify multiple typical defects.[Results]A standardized acoustic characterization system is established,encompassing multi-dimensional features such as sound pressure level,signal-to-noise ratio,odd-even harmonic ratio,high-frequency energy ratio,and spectral entropy,which can effectively enhance model performance,enabling accurate identification of quality defects such as DC bias and partial discharge.[Conclusion]This research provides a standardized processing framework and a high-precision recognition model for transformer acoustic data,contributing significantly to improving the quality of power equipment maintenance.