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首页> 外文期刊>IEEE transactions on industrial informatics >Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data
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Semisupervised Graph Convolution Deep Belief Network for Fault Diagnosis of Electormechanical System With Limited Labeled Data

机译：半质化图卷积了Liment Liment Liment Liment的Electorm机械系统故障诊断的深度信仰网络

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摘要

The labeled monitoring data collected from the electromechanical system is limited in the real industries; traditional intelligent fault diagnosis methods cannot achieve satisfactory accurate diagnosis results. To deal with this problem, an intelligent fault diagnosis method for electromechanical system based on a new semisupervised graph convolution deep belief network algorithm is proposed in this article. Specifically, the labeled and unlabeled samples are first employed to design a new adaptive local graph learning method for constructing the graph neighbor relationship. Meanwhile, the labeled samples are applied to describe the discriminative structure information of data via the latest circle loss. Finally, the local and discriminative objective functions are reconstructed under the semisupervised learning framework. The experimental results from the motor-bearing system demonstrate that the method can achieve 98.66% accuracy with only 10% of training labeled data, which indicates that it is a promising semisupervised intelligent fault diagnosis method.
机译：从机电系统收集的标记监测数据在真实行业中受到限制;传统的智能故障诊断方法无法实现令人满意的准确诊断结果。为了解决这个问题，在本文中提出了一种基于新的半体验图卷积的机电系统的智能故障诊断方法。具体地，首先使用标记和未标记的样本来设计一种用于构建图形邻居关系的新的自适应局部图学习方法。同时，施加标记的样本以通过最新的圆圈损失来描述数据的鉴别性结构信息。最后，在半体育学习框架下重建了本地和歧视目标职能。电动机系统的实验结果表明，该方法可达到98.66％的精度，只有10％的培训标记数据，这表明它是一个有前途的半体验智能故障诊断方法。

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