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A pairwise graph regularized constraint based on deep belief network for fault diagnosis
Digital Signal Processing ( IF 2.9 ) Pub Date : 2020-10-08 , DOI: 10.1016/j.dsp.2020.102868
Jie Yang , Weimin Bao , Yanming Liu , Xiaoping Li , Junjie Wang , Yue Niu

An enhanced intelligent fault diagnosis method is proposed based on pairwise graph regularized deep belief network (PG-DBN) model. In this novel framework, two different graph constraints are imposed on hidden layer of the Restricted Boltzmann Machine (RBM). The first graph constraint defines the representation of preserving the feature manifold structure in same class of the data and the second graph constraint defines the representation of the penalty of the feature manifold structure in different class of the data. The two graph constraints introduce feature manifold structure to RBM and make the extracted features contain more intrinsic information, which contributes to a better classification result. Meanwhile, the convergence and stability analysis of the proposed method is presented. Finally, the advantages of the proposed fault diagnosis model are evaluated by Tennessee Eastman process.



中文翻译:

基于深度置信网络的成对图正则化约束用于故障诊断

提出了一种基于成对图正则化深度信念网络(PG-DBN)模型的增强型智能故障诊断方法。在这个新颖的框架中,两个不同的图约束被施加到受限玻尔兹曼机(RBM)的隐藏层上。第一图形约束定义在相同数据类别中保留特征流形结构的表示,第二图形约束定义在不同数据类别中保留特征流形结构的惩罚的表示。这两个图约束将特征流形结构引入到RBM中,并使提取的特征包含更多的固有信息,从而有助于获得更好的分类结果。同时,提出了该方法的收敛性和稳定性分析。最后,

更新日期:2020-11-13
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