计算机科学
自编码
频域
快速傅里叶变换
时频分析
人工神经网络
试验数据
时域
断层(地质)
人工智能
算法
短时傅里叶变换
模式识别(心理学)
傅里叶变换
滤波器(信号处理)
傅里叶分析
数学
计算机视觉
数学分析
地质学
地震学
程序设计语言
作者
Haoyu Wang,Peng Li,Xun Lang,Dapeng Tao,Jun Ma,Xiang Li
出处
期刊:IEEE Transactions on Instrumentation and Measurement
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:72: 1-14
被引量:16
标识
DOI:10.1109/tim.2023.3234095
摘要
For imbalanced bearing fault diagnosis, generative adversarial networks (GANs) are a common data augmentation (DA) approach. Nevertheless, current GAN-based methods cannot update the generator from time–frequency domain simultaneously, downgrading the authenticity of signal time–frequency character. In this article, Fourier-like transform GAN (FTGAN), a novel GAN method, is proposed by introducing a Fourier-like transformer (FLT) based on autoencoder (AE) to improve synthetic data quality. FLT approximates the discrete Fourier transform (DFT) by the neural network, learning a universal map from time to frequency domain during training. FTGAN with FLT can decouple input into a time–frequency domain, fitting the distribution of time and frequency of data simultaneously. Multidomain distribution is manipulated in FTGAN without introducing additional signal transformation means. Furthermore, train on real, test on synthetic (TRTS) and train on synthetic, test on real (TSTR) analyses of 1-D data are introduced to evaluate data quality. Real and synthetic data are applied as training or test sets of diagnostic classifiers by turns so that data quality can be analyzed through diagnosis results. Experiment results show that the proposed method can generate bearing fault signals closer to real data in the time and frequency domains, effectively improving the performance under an imbalanced dataset.
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