小波
相似性(几何)
失真(音乐)
人工智能
断层(地质)
计算机科学
模式识别(心理学)
小波包分解
小波变换
电信
图像(数学)
放大器
带宽(计算)
地震学
地质学
作者
Song Fu,Lin Lin,Yue Wang,Minghang Zhao,Feng Guo,Shisheng Zhong,Yikun Liu
标识
DOI:10.1016/j.ymssp.2024.111115
摘要
Data augmentation methods, such as SMOTE and deep generative networks, have the potential to solve class-imbalance issue in aviation hydraulic pump fault diagnosis. Nevertheless, the scarcity of faulty samples often leads augmented samples generated using such methods suffer from blurred category boundaries, limited diversity, and mode collapse. To avoid these issues, a new data augmentation method namely Local Wavelet Similarity Fusion (LWSF) is proposed, which augments faulty samples by preserving and distorting the wavelet packet coefficients (WPCs) of the original faulty samples. An important property is that LWSF can generate high-quality synthesis samples without complex model training. First, faulty samples are decomposed into a series of frequency bands using wavelet packet decomposition. Second, the amplitudes of WPCs at a few randomly selected frequency bands are distorted, which is accomplished through a similarity-weighted fusion of the target WPCs with the closest related WPCs matched from reference samples, with the goal of keeping distortion within an appropriate range. Third, these WPCs are used to reconstruct the samples, which serve as the augmented samples. Besides, to enhance diversity, normal samples are also employed to assist in the distortion. Finally, the effectiveness of LWSF is validated by a series of experimental comparisons. In an imbalanced fault diagnosis task, the accuracy and F1-score obtained by LWSF are 97.16 % and 97.35 %, respectively, which are 3.59 % and 7.30 % higher than the best one among the five compared methods.
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