残余物
计算机科学
方位(导航)
断层(地质)
卷积神经网络
块(置换群论)
一般化
模式识别(心理学)
深度学习
人工智能
数据挖掘
人工神经网络
算法
数学
数学分析
几何学
地震学
地质学
作者
Huaqing Wang,Wenbo Zhang,Changkun Han,Zhenbao Fu,Liuyang Song
标识
DOI:10.1088/1361-6501/ad1708
摘要
Abstract As a vital constituent of rotating machinery, rolling bearings assume a pivotal function in ensuring the stable operation of equipment. Recently, deep learning (DL)-based methods have been able to diagnose bearing faults accurately. However, in practical applications, the severe data imbalance caused by the limited availability of fault data compared to the abundance of healthy data poses challenges to the effective training of DL models, leading to a decrease in diagnostic accuracy. In this paper, a bearing fault diagnosis method with the improved residual Unet diffusion model (IResUnet-DM) based on a data generation strategy is proposed to solve the extreme data imbalance. Initially, a deep feature extraction network named improved residual Unet is built, which effectively enhances the information learning ability from vibration signals of the Unet network by one-dimensional residual block and self-attention block. Furthermore, the IResUnet-DM is constructed, which generates vibration signals under extreme data imbalance based on a probability model. The variational bound on the negative log-likelihood of the distribution of generated data was optimized to make the generated data similar to the real data distribution. Finally, wide deep convolutional neural network and one-dimensional ResNet classification networks were used for fault identification to verify the validity and generalization of the IResUnet-DM. Experiment results at different data imbalance rates on two bearing datasets demonstrate that the proposed method can effectively improve fault diagnosis accuracy under extreme data imbalances and outperform the comparison method.
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