An enhanced intelligent diagnosis method is proposed based on a joint pairwise graph embedded sparse deep belief network with partial least square fine-tuning (J-PDBN). In this novel framework, the joint pairwise graph embedded sparse deep belief network (DBN) is considered as an unsupervised learning method to realize fast parameters initialization and to extract data features. It combines the advantages of both the pairwise graph and sparse representation, which can preserve the manifold structure of the original data and generate discriminant features. The partial least square (PLS) is used to optimize the parameters to eliminate the gradient diffusion in the supervised learning process. The J-PDBN-based fault diagnosis is implemented by both the unsupervised learning method and PLS fine-tuning, which contributes to better classification capabilities. Finally, gearbox and bearing fault diagnosis experiments are conducted. The results show that the total recognition rates of the proposed method are 93.65% in the gearbox case and 95.96% in the bearing case, which are higher than those of other methods. Specifically, the testing accuracy is approximately 10% higher than those of the DBN network for both cases. This validates the effectiveness and superiority of the proposed method.

提出了一种基于联合成对图嵌入稀疏深度置信网络的偏最小二乘微调增强智能诊断方法（J-PDBN）。在这种新颖的框架中，联合成对的图嵌入稀疏深度信念网络（DBN）被视为实现快速参数初始化和提取数据特征的无监督学习方法。它结合了成对图和稀疏表示的优点，可以保留原始数据的流形结构并生成判别特征。偏最小二乘（PLS）用于优化参数，以消除监督学习过程中的梯度扩散。基于J-PDBN的故障诊断是通过无监督学习方法和PLS微调实现的，这有助于更好的分类能力。最后，进行了变速箱和轴承故障诊断实验。结果表明，该方法在齿轮箱中的总识别率为93.65％，在轴承箱中的总识别率为95.96％，高于其他方法。具体而言，在两种情况下，测试精度均比DBN网络的测试精度高约10％。这验证了所提出方法的有效性和优越性。在这两种情况下，测试准确性均比DBN网络的准确性高出约10％。这验证了所提出方法的有效性和优越性。在这两种情况下，测试准确性均比DBN网络的准确性高出约10％。这验证了所提出方法的有效性和优越性。