Nonlinear feature selection using Gaussian kernel SVM-RFE for fault diagnosis

2018 ◽  
Vol 48 (10) ◽  
pp. 3306-3331 ◽  
Author(s):  
Yangtao Xue ◽  
Li Zhang ◽  
Bangjun Wang ◽  
Zhao Zhang ◽  
Fanzhang Li
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Gaussian Kernel ◽  
Nonlinear Feature ◽  
Kernel Svm

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

scholarly journals Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23903-23926 ◽  
Author(s):  
Mariela Cerrada ◽  
René Sánchez ◽  
Diego Cabrera ◽  
Grover Zurita ◽  
Chuan Li
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Multi Stage

A New Method of Nonlinear Feature Extraction for Multi-Fault Diagnosis of Rotor Systems

2010 ◽  
Vol 41 (10) ◽  
pp. 29-37 ◽  
Author(s):  
Zhixiong Li ◽  
Xinping Yan ◽  
Chengqing Yuan ◽  
Jiangbin Zhao ◽  
Zhongxiao Peng
Keyword(s):  
Feature Extraction ◽  
Fault Diagnosis ◽  
New Method ◽  
Rotor Systems ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

scholarly journals Fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm for feature selection and parameter optimization

2017 ◽  
Vol 9 (1) ◽  
pp. 168781401668529 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Bat Algorithm ◽  
Relevance Vector Machine ◽  
Experimental Results ◽  
Machine Method ◽  
Training Samples ◽  
Kernel Parameter

In this article, fault diagnosis of bearing based on relevance vector machine classifier with improved binary bat algorithm is proposed, and the improved binary bat algorithm is used to select the appropriate features and kernel parameter of relevance vector machine. In the improved binary bat algorithm, the new velocities updating method of the bats is presented in order to ensure the decreasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are equal to the current best location’s element, and the increasing of the probabilities of changing their position vectors’ elements when the position vectors’ elements of the bats are unequal to the current best location’s element, which are helpful to strengthen the optimization ability of binary bat algorithm. The traditional relevance vector machine trained by the training samples with the unreduced features can be used to compare with the proposed improved binary bat algorithm–relevance vector machine method. The experimental results indicate that improved binary bat algorithm–relevance vector machine has a stronger fault diagnosis ability of bearing than the traditional relevance vector machine trained by the training samples with the unreduced features, and fault diagnosis of bearing based on improved binary bat algorithm–relevance vector machine is feasible.

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