Affiliations: [1]
School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing, P.R. China
Correspondence:
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Corresponding author. Jiufei Luo, School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, P.R. China. E-mail: [email protected].
Abstract: 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.
