Fault diagnosis method based on a new manifold learning framework
Fault diagnosis method based on a new manifold learning framework
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基于新型流形学习框架的故障诊断方法
DOI：
10.3233/jifs-169522
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发表时间：
2018-06
影响因子：
2
通讯作者：
Zhang Yi
中科院分区：
文献类型：
--
作者：
Su Zuqiang;Xu Haitao;Luo Jiufei;Zheng Kai;Zhang Yi
提出了一种新的流形学习框架用于机械故障诊断，以进一步提高故障诊断的准确率。新的流形学习框架包括两个阶段：用于非线性去噪的无监督流形学习和用于特征提取的有监督流形学习。首先介绍了基于非监督流形学习的非线性去噪方法，该方法结合了流形学习在揭示非线性流形结构方面的优势和相空间重构在表征信号和噪声空间分布方面的优势。然后根据去噪后的振动信号频谱进行故障特征提取。为了降低频谱的高维数，去除冗余信息，提出了一种改进的有监督局部切空间对齐（ISLTSA）方法，进一步扩大故障样本的多样性，提高了故障样本的可分性。最后，将提取的低维故障特征输入到模式识别方法中进行故障识别。通过对轴承故障诊断的研究，验证了该方法的有效性。
This study presents a new manifold learning framework for machinery fault diagnosis, in order to further improve fault diagnosis accuracy. The new manifold learning framework contains two stages: unsupervised manifold learning for nonlinear denoising and supervised manifold learning for feature extraction. Firstly, the nonlinear denoising method with unsupervised manifold learning was introduced, which combined advantages of manifold learning in revealing nonlinear manifold structure as well as advantages of phase space reconstruction in representing spatial distribution of signal and noise. Then, fault feature extraction was carried out according to the frequency spectrum of vibration signals after denoising. In order to reduce the high dimension and remove redundant information of frequency spectrum, an improved supervised local tangent space alignment (ISLTSA) was proposed to further enlarge diversity of the fault samples and thus increase separability. Finally, the extracted low-dimensional fault features were inputted into a pattern recognition method for fault identification. The effectiveness of the proposed method was verified by studying the fault diagnosis of bearings.
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