Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

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...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 15; H. 7; S. 16225 - 16247
Hauptverfasser: Wang, Xiang, Zheng, Yuan, Zhao, Zhenzhou, Wang, Jinping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 06.07.2015
MDPI
Schlagworte:
Algorithms
Classification
Datasets
dimensionality reduction
Engineering
Fault diagnosis
feature extraction
high-dimensional data
manifold learning
Pattern recognition
Sensors
statistical locally linear embedding
Teaching methods
China
statistical locally linear embedding
dimensionality reduction
fault diagnosis
feature extraction
high-dimensional data
manifold learning
ISSN:1424-8220, 1424-8220
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Zusammenfassung: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s150716225