基于奇异值分解和深度信度网络多分类器的滚动轴承故障诊断方法

An Approach to Fault Diagnosis of Rolling Bearing Using SVD and Multiple DBN Classifiers

提出一种基于奇异值分解(SVD)和深度信度网络(DBN)多分类器的滚动轴承故障诊断方法.对滚动轴承的振动信号进行相空间重构,得到相应的特征矩阵;对特征矩阵进行SVD分解,并用所得整个奇异值序列构造特征向量,建立DBN多分类器模型,以实现滚动轴承的故障诊断;同时,将所提出的方法与DBN、反向传播神经网络、支持向量机等算法进行对比.结果表明,所提出的方法能够更加稳定、可靠地识别滚动轴承的故障类型和故障程度.

A novel approach to fault diagnosis of rolling bearing using singular value decomposition （SVD） and multiple deep belief network （DBN） classifiers was proposed. According to this approach, vibration signals of rolling bearing under different conditions were reconstructed in the phase space and characteristic matrixes were obtained. Then, the characteristic matrixes were decomposed by SVD to get the singular values. After that, all the singular values were used to form a characteristic vector. Finally, a multiple DBN classifiers model was developed to identify the faults of rolling bearing. To confirm the superiority of the proposed approach, it was compared with DBN, BP neural network and SVM. The experimental re- sults indicates that the proposed approach has a better performance in accuracy and efficiency to identify the fault patterns and severity of rolling bearing.