Application of Incremental Local Tangent Space Alignment Algorithm to Rolling Bearings Fault Diagnosis

2012 ◽  
Vol 48 (05) ◽  
pp. 81 ◽  
Author(s):  
Qing YANG
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Alignment Algorithm ◽  
Rolling Bearings ◽  
Local Tangent

Fault Diagnosis Method Based on Supervised Manifold Learning and SVM

2011 ◽  
Vol 216 ◽  
pp. 223-227 ◽  
Author(s):  
Guang Bin Wang ◽  
Xue Jun Li ◽  
Zhi Cheng He ◽  
Y.Q. Kong
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Roller Bearing ◽  
Support Vector ◽  
Alignment Algorithm ◽  
Bearing Fault Diagnosis ◽  
Svm Algorithm ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Local Tangent

In order to better identify the fault of bearing,one new fualt diagnosis method based on supervised Linear local tangent space alignment (SLLTSA) and support vector machine (SVM) is proposed..In this methd, the supervised learning is embedded into the linear local tangent space alignment algorithm,making full use of experience category information for fault feature extraction, and then using linear transformation matrix to fast process the new monitoring data, finally distinguishing fault status of the incremental data by nonlinear SVM algorithm. The experiment result for roller bearing fault diagnosis shows that SLLTSA-SVM method has better diagnosis effect than related unsupervised methods.

Fault Diagnosis Method Based on Supervised Incremental Local Tangent Space Alignment and SVM

2010 ◽  
Vol 34-35 ◽  
pp. 1233-1237 ◽  
Author(s):  
Guang Bin Wang ◽  
Xian Qiong Zhao ◽  
Yu Hui He
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Roller Bearing ◽  
Support Vector ◽  
Alignment Algorithm ◽  
Bearing Fault Diagnosis ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Fault Feature Extraction ◽  
Local Tangent

To enhance the effect of fault diagnosis, a new fualt diagnosis method based on supervised incremental local tangent space alignment (SILTSA) and support vector machine (SVM) is proposed. The supervised learning approach is embedded into the incremental local tangent space alignment algorithm, to realize fault feature extraction and new data processing for equipment fault signal, and then correctly classify the faults by non-linear support vector machines. The experiment result for roller bearing fault diagnosis shows that SILTSA-SVM method has better diagnosis effect to related methods

Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment

2015 ◽  
Vol 764-765 ◽  
pp. 274-279
Author(s):  
Zhi Wen ◽  
Chen Lu ◽  
Hong Mei Liu
Keyword(s):  
Tangent Space ◽  
Feature Vector ◽  
Wavelet Packet ◽  
Linear Structure ◽  
Extraction Technology ◽  
Rolling Bearings ◽  
Time Frequency ◽  
Non Linear ◽  
Non Stationary Signal ◽  
Local Tangent

Health assessment and fault diagnosis for rolling bearings mostly adopt traditional methods, such as time-frequency, spectral, and wavelet packet analyses, to extract the feature vector. These methods are suitable for processing data with a linear structure. However, for the non-linear and non-stationary signal, the result of these methods is not ideal. Thus, this study proposes a suitable method to extract the feature vector in nonlinear signals. Local tangent space alignment of a manifold algorithm is employed to extract the feature vector from the rolling bearings. Results verify the advantage of the manifold algorithm for non-linear and non-stationary signals.

scholarly journals Rotating Machine Fault Diagnosis Based on Optimal Morphological Filter and Local Tangent Space Alignment

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Shaojiang Dong ◽  
Lili Chen ◽  
Baoping Tang ◽  
Xiangyang Xu ◽  
Zhengyuan Gao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Tangent Space ◽  
Learning Algorithm ◽  
Support Vector ◽  
Morphological Filter ◽  
Rotating Machine ◽  
Machine Fault Diagnosis ◽  
Mode Decomposition ◽  
Machine Fault ◽  
Local Tangent

In order to identify the fault of rotating machine effectively, a new method based on the morphological filter optimized by particle swarm optimization algorithm (PSO) and the nonlinear manifold learning algorithm local tangent space alignment (LTSA) is proposed. Firstly, the signal is purified by the morphological filter; the filter’s structure element (SE) is selected by PSO method. Then the filtered signals are decomposed by the empirical mode decomposition (EMD) method, and the extract features are mapped into the LTSA to extract the character features; then the support vector machine (SVM) model is used to achieve the rotating machine fault diagnosis. The proposed method is evaluated by vibration signals measured from bearings with faults. Results show that the method can effectively remove the noise and extract the fault features, so the rotating machine fault diagnosis can be achieved effectively.

Adaptive Discriminant Linear Local Tangent Space Alignment Algorithm on Face Recognition

2014 ◽  
Vol 989-994 ◽  
pp. 2381-2384
Author(s):  
Yuan Xing Lv ◽  
Yan Ni Deng ◽  
Yuan Shi ◽  
Qiang Li ◽  
Wen Peng
Keyword(s):  
Face Recognition ◽  
Tangent Space ◽  
Recognition Rate ◽  
Degree Of Polymerization ◽  
Local Geometry ◽  
Alignment Algorithm ◽  
Scatter Matrix ◽  
The Face ◽  
Local Tangent

This paper proposes an adaptive discriminant linear local tangent space alignment algorithm DALLTSA. On the basis of LLTSA algorithm adding adaptive and discriminant gets DALLTSA.DALLTSA not only combines characteristics in DLLTSA that maintain the local geometry and meets the maximum between-class scatter matrix, but also dynamically selects K-neighbor better to reflect the degree of polymerization between samples. Finally, the face recognition experiments based on Gabor [1] filter and DALLTSA shows that this algorithm improves the recognition rate and robustness.

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