断层(地质)
加权
计算机科学
人工智能
模式(计算机接口)
残余物
点(几何)
功能(生物学)
钥匙(锁)
深度学习
算法
模式识别(心理学)
数学
地质学
医学
几何学
进化生物学
地震学
生物
放射科
操作系统
计算机安全
作者
He Li,Zhijin Zhang,Chunlei Zhang
出处
期刊:Measurement
[Elsevier]
日期:2023-05-24
卷期号:217: 113062-113062
被引量:9
标识
DOI:10.1016/j.measurement.2023.113062
摘要
Currently, the popular fault diagnosis methods based on deep learning encounter a common limitation in that their accuracy heavily relies on an adequate number of training samples. However, collecting fault samples in real-world scenarios is often challenging. To overcome this challenge, this paper develops a novel data augmentation method named variational mode reconstruction (VMR) to generate augmented samples with similar features to the original samples. The first key point is the random weighting of a certain randomly chosen intrinsic mode function (IMF). Another key point is that the mean values and standard deviations of the augmented samples remain consistent with the original samples. Next, the augmented balanced dataset is utilized to train a deep residual shrinkage network (DRSN), which is then employed for the classification of test samples. Finally, the effectiveness and superiority of the developed VMR in few-shot fault diagnosis are verified through a series of experiments.
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