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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.


The normalized amplitude energy features value of the first six IMFs by EMD method (Note: sample data No. 1–100, 101–200, 201–300, 301–400 represent normal, inner race fault, ball fault and outer race faults, respectively).
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sensors-15-16225-f009: The normalized amplitude energy features value of the first six IMFs by EMD method (Note: sample data No. 1–100, 101–200, 201–300, 301–400 represent normal, inner race fault, ball fault and outer race faults, respectively).

Mentions: The normalized IMFs energy was analyzed after EMD, and the results are shown in Figure 9, and the energy distributions are mutually different. From Figure 9, we note that there is a relatively large difference between the normal bearing and the faulty bearing signals.


Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

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

The normalized amplitude energy features value of the first six IMFs by EMD method (Note: sample data No. 1–100, 101–200, 201–300, 301–400 represent normal, inner race fault, ball fault and outer race faults, respectively).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16225-f009: The normalized amplitude energy features value of the first six IMFs by EMD method (Note: sample data No. 1–100, 101–200, 201–300, 301–400 represent normal, inner race fault, ball fault and outer race faults, respectively).
Mentions: The normalized IMFs energy was analyzed after EMD, and the results are shown in Figure 9, and the energy distributions are mutually different. From Figure 9, we note that there is a relatively large difference between the normal bearing and the faulty bearing signals.

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.