Limits...
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 vibration signal waveforms and power spectra from the different fault types: (a,b) Normal bearing vibration waveform/power spectrum; (c,d) Inner race fault vibration waveform/power spectrum; (e,f) Ball fault vibration waveform/power spectrum; (g,h) Outer race fault vibration waveform/power spectrum.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4541876&req=5

sensors-15-16225-f004: The vibration signal waveforms and power spectra from the different fault types: (a,b) Normal bearing vibration waveform/power spectrum; (c,d) Inner race fault vibration waveform/power spectrum; (e,f) Ball fault vibration waveform/power spectrum; (g,h) Outer race fault vibration waveform/power spectrum.

Mentions: The bearing type is SKF6205-2RS JEM, a deep groove ball bearing. Four types of vibration signal datasets (normal, ball fault, inner race fault and outer race fault) are acquired from the bearings with the sampling frequency of 48 kHz during about 10 s by using a 16 channel DAT recorder, and tested under motor loads is 2 HP at the speed of 1750 r/min. A single point fault is introduced to the test bearing inner race and outer race, respectively, using an electro-discharge machining with the fault diameter of 21 mils inches and the fault depth of 11 mils (1 mil = 25.4 um). More detailed information about the test rig can be found in [36]. The length of the signal data in every dataset is about 480,000, we can extract 100 samples for each vibration condition, that is, every sample data includes 4096 points, and thus the overall dataset consists of 400 samples. Figure 4 presents the vibration signal waveforms and power spectra from four signal samples of the different fault types.


Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding.

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

The vibration signal waveforms and power spectra from the different fault types: (a,b) Normal bearing vibration waveform/power spectrum; (c,d) Inner race fault vibration waveform/power spectrum; (e,f) Ball fault vibration waveform/power spectrum; (g,h) Outer race fault vibration waveform/power spectrum.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-16225-f004: The vibration signal waveforms and power spectra from the different fault types: (a,b) Normal bearing vibration waveform/power spectrum; (c,d) Inner race fault vibration waveform/power spectrum; (e,f) Ball fault vibration waveform/power spectrum; (g,h) Outer race fault vibration waveform/power spectrum.
Mentions: The bearing type is SKF6205-2RS JEM, a deep groove ball bearing. Four types of vibration signal datasets (normal, ball fault, inner race fault and outer race fault) are acquired from the bearings with the sampling frequency of 48 kHz during about 10 s by using a 16 channel DAT recorder, and tested under motor loads is 2 HP at the speed of 1750 r/min. A single point fault is introduced to the test bearing inner race and outer race, respectively, using an electro-discharge machining with the fault diameter of 21 mils inches and the fault depth of 11 mils (1 mil = 25.4 um). More detailed information about the test rig can be found in [36]. The length of the signal data in every dataset is about 480,000, we can extract 100 samples for each vibration condition, that is, every sample data includes 4096 points, and thus the overall dataset consists of 400 samples. Figure 4 presents the vibration signal waveforms and power spectra from four signal samples of the different fault types.

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.