计算机科学
卷积神经网络
噪音(视频)
方位(导航)
域适应
断层(地质)
人工智能
降噪
深度学习
适应(眼睛)
模式识别(心理学)
人工神经网络
机器学习
试验数据
面子(社会学概念)
数据挖掘
社会学
地质学
物理
地震学
光学
程序设计语言
图像(数学)
分类器(UML)
社会科学
作者
Mohammadreza Ghorvei,mohammadreza kavianpor,Mohammad Taghi Hamidi Beheshti,Amin Ramezani
标识
DOI:10.1088/1361-6501/ac3a31
摘要
Abstract Deep learning-based approaches for diagnosing bearing faults have attracted considerable attention in the last years. However, in real-world applications, these methods face challenges. For proper training of these models, a considerable amount of labeled data are necessary, and due to limitations in industry, obtaining this amount of data may not be possible. Because of load variations, the distribution of training and test data may vary, which reduces the accuracy of the trained model for various working conditions. Furthermore, noise has a significant impact on bearing fault diagnosis performance in real-world industrial applications. This study introduced the deep subdomain adaptation convolutional neural network (DSACNN) method to overcome these challenges in real scenarios. The local maximum mean discrepancy (LMMD) method reduces the difference between each class distribution in the source and target domains. We validated our proposed method by CWRU bearing dataset under various loads and noise with different SNRs. The results show that DSACNN outperforms other comparative methods in anti-noise performance and reduction of domain distribution discrepancies.
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