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Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

Wang X, Zheng Y, Zhao Z, Wang J - Sensors (Basel) (2015)

Bottom Line: 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.Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly.The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

View Article: PubMed Central - PubMed

Affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China. wangxiang@njit.edu.cn.

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.

No MeSH data available.


Illustration of LLE algorithm: (a) Select neighbors; (b) Reconstruct with embedded linear weights; (c) Map to coordinates.
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sensors-15-16225-f001: Illustration of LLE algorithm: (a) Select neighbors; (b) Reconstruct with embedded linear weights; (c) Map to coordinates.

Mentions: Given a set of data, is in a high-dimensional input data space . The data points are assumed to lie on or near a nonlinear manifold of intrinsic dimensionality . The goal of LLE is to find a low-dimensional embedding of dataset by mapping the D-dimensional data into a single global coordinate system in Euclidean distance . The LLE algorithm can be generalized to three steps: select neighbors, reconstruct with linear weights and map to embedded coordinates. The steps of the LLE algorithm are illustrated in Figure 1.


Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

Wang X, Zheng Y, Zhao Z, Wang J - Sensors (Basel) (2015)

Illustration of LLE algorithm: (a) Select neighbors; (b) Reconstruct with embedded linear weights; (c) Map to coordinates.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541876&req=5

sensors-15-16225-f001: Illustration of LLE algorithm: (a) Select neighbors; (b) Reconstruct with embedded linear weights; (c) Map to coordinates.
Mentions: Given a set of data, is in a high-dimensional input data space . The data points are assumed to lie on or near a nonlinear manifold of intrinsic dimensionality . The goal of LLE is to find a low-dimensional embedding of dataset by mapping the D-dimensional data into a single global coordinate system in Euclidean distance . The LLE algorithm can be generalized to three steps: select neighbors, reconstruct with linear weights and map to embedded coordinates. The steps of the LLE algorithm are illustrated in Figure 1.

Bottom Line: 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.Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly.The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches.

View Article: PubMed Central - PubMed

Affiliation: College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China. wangxiang@njit.edu.cn.

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.

No MeSH data available.