title: Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.
abstract: Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.
authors.affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China. wangxiang@njit.edu.cn.|School of Energy and Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China. wangxiang@njit.edu.cn.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wang
authors.affiliation: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China. zhengyuan@hhu.edu.cn.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zheng
authors.affiliation: College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China. zhaozhzh_2008@hhu.edu.cn.
authors.forename: Zhenzhou
authors.initials: Z
authors.lastname: Zhao
authors.affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China. wangjinping@njit.edu.cn.
authors.forename: Jinping
authors.initials: J
authors.lastname: Wang
keywords: dimensionality reduction; fault diagnosis; feature extraction; high-dimensional data; manifold learning; statistical locally linear embedding