scholarly journals Rolling Bearing Fault Diagnosis Based on Physical Model and One-Class Support Vector Machine

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Li Xiangyang ◽  
Chen Wanqiang
Keyword(s):  
Support Vector Machine ◽  
Simulated Data ◽  
Rolling Bearing ◽  
Vibration Monitoring ◽  
Radial Clearance ◽  
Support Vector ◽  
Rolling Bearings ◽  
Bearing Fault Diagnosis ◽  
Runge Kutta Method ◽  
Bearing Force

This paper aims at diagnosing the fault of rolling bearings and establishes the system of dynamics model with the consideration of rolling bearing with nonlinear bearing force, the radial clearance, and other nonlinear factors, using Runge-Kutla such as Hertzian elastic contactforce and internal radial clearance, which are solved by the Runge-Kutta method. Using simulated data of the normal state, a self-adaptive alarm method for bearing condition based on one-class support vector machine is proposed. Test samples were diagnosed with a recognition accuracy over 90%. The present method is further applied to the vibration monitoring of rolling bearings. The alarms under the actual abnormal condition meet the demand of bearings monitoring.

Support Vector Machine Optimization Based on Bacterial Foraging Algorithm and Applied in Fault Diagnosis

2011 ◽  
Vol 216 ◽  
pp. 153-157
Author(s):  
D.L. Yang ◽  
Xue Jun Li ◽  
K. Wang ◽  
Ling Li Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Parameter Optimization ◽  
Rolling Bearing ◽  
Optimization Method ◽  
Parameters Optimization ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Bacterial Foraging ◽  
Bacterial Foraging Algorithm

The parameter optimization is the key to study of support vector machine (SVM). With strong global search capability of bacterial foraging algorithm(BFA), the optimization method—support vector machine parameters optimization based on bacterial foraging algorithm was proposed, which can achieve the dynamic optimization of the parametersCandγ,and overcomes the problem of inefficiency for selecting reasonable parameters according to the experience in the traditional fault diagnosis. Compared with other methods, the BFA is simpler and easier for programming, and the optimization SVM model become smaller. The rolling bearing fault diagnosis results show that bacterial foraging algorithm is suitable for support vector machine parameter optimization.

Fault feature extraction and classification based on WPT and SVD: Application to element bearings with artificially created faults under variable conditions

2016 ◽  
Vol 231 (22) ◽  
pp. 4186-4196 ◽  
Author(s):  
Mourad Kedadouche ◽  
Zhaoheng Liu
Keyword(s):  
Support Vector Machine ◽  
Singular Value Decomposition ◽  
Wavelet Packet ◽  
Rolling Bearing ◽  
Wavelet Packet Transform ◽  
Singular Value ◽  
Support Vector ◽  
Rolling Bearings ◽  
The Matrix ◽  
Value Decomposition

Achieving a precise fault diagnosis for rolling bearings under variable conditions is a problematic challenge. In order to enhance the classification and achieves a higher precision for diagnosing rolling bearing degradation, a hybrid method is proposed. The method combines wavelet packet transform, singular value decomposition and support vector machine. The first step of the method is the decomposition of the signal using wavelet packet transform and then instantaneous amplitudes and energy are computed for each component. The Second step is to apply the singular value decomposition to the matrix constructed by the instantaneous amplitudes and energy in order to reduce the matrix dimension and obtaining the fault feature unaffected by the operating condition. The features extracted by singular value decomposition are then used as an input to the support vector machine in order to recognize the fault mode of rolling bearings. The method is applied to a bearing with faults created using electro-discharge machining under laboratory conditions. Test results show that the proposed methodology is effective to classify rolling bearing faults with high accuracy.

A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis

2019 ◽  
Vol 110 ◽  
pp. 36-47 ◽  
Author(s):  
Xin Li ◽  
Yu Yang ◽  
Haiyang Pan ◽  
Jian Cheng ◽  
Junsheng Cheng
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals A rolling bearing fault diagnosis method based on VMD – multiscale fractal dimension/energy and optimized support vector machine

2016 ◽  
Vol 18 (6) ◽  
pp. 3581-3595
Author(s):  
Binbin Xu ◽  
Fei Chen ◽  
Xiaojuan Chen ◽  
Zhaojun Yang ◽  
Qunya Xie ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fractal Dimension ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

scholarly journals Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 137395-137406 ◽  
Author(s):  
Laohu Yuan ◽  
Dongshan Lian ◽  
Xue Kang ◽  
Yuanqiang Chen ◽  
Kejia Zhai
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Rolling Bearing ◽  
Support Vector ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis
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