The Research on the Method of Feature Selection in Support Vector Machine Based Entropy

2011 ◽  
Vol 354-355 ◽  
pp. 1192-1196
Author(s):  
Xiao Yan Zhu ◽  
Xi Tian ◽  
Xiao Xun Zhu
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Input Parameter ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
State Recognition ◽  
Fault State

The large rotating machinery functioning of the rotor is one of the most important issues. It has great significance to identify the fault early and implement intelligent fault diagnosis. However there is a big nonlinear about large rotating machinery and has less fault samples. This led great difficulties for feature selection and state recognition. Based on Entropy in feature selection, we extract each intrinsic mode’s function energy as eigenvector and make them for input parameter of the support vector machine (SVM) to fault diagnosis. The experiment shows that this method can classify the fault state, and completed intelligent fault diagnosis.

scholarly journals A New Intelligent Fault Diagnosis Method of Rotating Machinery under Varying-Speed Conditions Using Infrared Thermography

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yongbo Li ◽  
Xianzhi Wang ◽  
Shubin Si ◽  
Xiaoqiang Du
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Rotating Machinery ◽  
Support Vector ◽  
Intelligent Fault Diagnosis ◽  
Fault Features ◽  
Diagnosis Method ◽  
Systematic Framework ◽  
Machinery Fault Diagnosis

A novel systematic framework, infrared thermography- (IRT-) based method, for rotating machinery fault diagnosis under nonstationary running conditions is presented in this paper. In this framework, IRT technique is first applied to obtain the thermograph. Then, the fault features are extracted using bag-of-visual-word (BoVW) from the IRT images. In the end, support vector machine (SVM) is utilized to automatically identify the fault patterns of rotating machinery. The effectiveness of proposed method is evaluated using lab experimental signal of rotating machinery. The diagnosis results show that the IRT-based method has certain advantages in classification rotating machinery faults under nonstationary running conditions compared with the traditional vibration-based method.

Rolling Bearing Fault Diagnosis Based on Wavelet Packet Feature Entropy-MFSVM

2010 ◽  
Vol 121-122 ◽  
pp. 813-818 ◽  
Author(s):  
Wei Guo Zhao ◽  
Li Ying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Support Vector ◽  
Wavelet Packet Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Fuzzy Support Vector Machine ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

On the basis of wavelet packet-characteristic entropy(WP-CE) and multiclass fuzzy support vector machine(MFSVM), the author proposes a new fault diagnosis method of vibrating of hearings，in which three layers wavelet packet decomposition of the acquired vibrating signals of hearings is performed and the wavelet packet-characteristic entropy is extracted，the eigenvector of wavelet packet of the vibrating signals is constructed，and taking this eigenvector as fault sample multiclass fuzzy support vector machine is trained to implement the intelligent fault diagnosis. The simulation result from the proposed method is effective and feasible.

Fault Classification Model of Rotor Based on Support Vector Machine

2011 ◽  
Vol 66-68 ◽  
pp. 1982-1987
Author(s):  
Wei Niu ◽  
Guo Qing Wang ◽  
Zheng Jun Zhai ◽  
Juan Cheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rotating Machinery ◽  
Classification Model ◽  
Fault Classification ◽  
Support Vector ◽  
Vibration Signals ◽  
Good Classification ◽  
Speed Change ◽  
Diagnosis Model

The vibration signals of rotating machinery in operation consist of plenty of information about its running condition, and extraction and identification of fault signals in the process of speed change are necessary for the fault diagnosis of rotating machinery. This paper improves DDAG classification method and proposes a new fault diagnosis model based on support vector machine to solve the problem of restricting the rotating machinery fault intelligent diagnosis due to the lack of fault data samples. The testing results demonstrate that the model has good classification precision and can correctly diagnose faults.

scholarly journals Rotating Machinery Fault Diagnosis for Imbalanced Data Based on Fast Clustering Algorithm and Support Vector Machine

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Xiaochen Zhang ◽  
Dongxiang Jiang ◽  
Te Han ◽  
Nanfei Wang ◽  
Wenguang Yang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Imbalanced Data ◽  
Principal Component ◽  
Rotating Machinery ◽  
Support Vector ◽  
Data Set ◽  
Diagnosis Model ◽  
Sample Set

To diagnose rotating machinery fault for imbalanced data, a method based on fast clustering algorithm (FCA) and support vector machine (SVM) was proposed. Combined with variational mode decomposition (VMD) and principal component analysis (PCA), sensitive features of the rotating machinery fault were obtained and constituted the imbalanced fault sample set. Next, a fast clustering algorithm was adopted to reduce the number of the majority data from the imbalanced fault sample set. Consequently, the balanced fault sample set consisted of the clustered data and the minority data from the imbalanced fault sample set. After that, SVM was trained with the balanced fault sample set and tested with the imbalanced fault sample set so the fault diagnosis model of the rotating machinery could be obtained. Finally, the gearbox fault data set and the rolling bearing fault data set were adopted to test the fault diagnosis model. The experimental results showed that the fault diagnosis model could effectively diagnose the rotating machinery fault for imbalanced data.

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