scholarly journals Bearing Fault Diagnosis Using Orthogonal Matching Pursuit with Pulse Atoms Based on Vibration Model

2015 ◽  
Vol 3 (2) ◽  
pp. 164-175 ◽  
Author(s):  
Huijie Zhu ◽  
Xinqing Wang ◽  
Yanfeng Li ◽  
Mengxi Liu ◽  
Tianshuai Liu
Keyword(s):  
Genetic Algorithm ◽  
Fault Location ◽  
Matching Pursuit ◽  
Rolling Bearing ◽  
Orthogonal Matching Pursuit ◽  
New Approach ◽  
Bearing Fault Diagnosis ◽  
Vibration Model ◽  
Np Hard Problem ◽  
Population Genetic Algorithm

AbstractIn this paper, a new approach to rolling bearing diagnosis is proposed, which applied orthogonal matching pursuit with pulse atoms. Solving orthogonal matching pursuit with pulse atoms (OMP_PA) is an NP-hard problem. With the help of multi-population genetic algorithm, better solution is obtained, and the shortcoming of sensitiveness to parameters setting in genetic algorithm is improved. According to the comparisons with other algorithms, OMP_PA could precisely extract the pulse components, and the interferential components are almost filtered. The experiments show that, OMP_PA could determine the fault location of bearings, and clearly displayed the vibration model. In conclusion, it provides a new way to the diagnosis for bearings.

GAsDock: a new approach for rapid flexible docking based on an improved multi-population genetic algorithm

2004 ◽  
Vol 14 (18) ◽  
pp. 4671-4676 ◽  
Author(s):  
Honglin Li ◽  
Chunlian Li ◽  
Chunshan Gui ◽  
Xiaomin Luo ◽  
Kaixian Chen ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Population Genetic ◽  
Flexible Docking ◽  
New Approach ◽  
Population Genetic Algorithm

A Bearing Fault Diagnosis Method Using Fusion of Lie Group Classifier and GA

2013 ◽  
Vol 790 ◽  
pp. 659-662
Author(s):  
Si Yuan Zhao ◽  
Wang Tao ◽  
Ge Xin ◽  
Yun Liu
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Lie Group ◽  
Rolling Bearing ◽  
Singular Value ◽  
Support Vector ◽  
Ring Fault ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

A novel bearing fault diagnosis method based on Lie group was proposed, and genetic algorithm (GA) was introduced to optimize feature amount. This method was applied to inner ring fault, outer ring fault and rolling element fault of rolling bearing. Firstly, the rolling bearing vibration signal was decomposed as intrinsic model functions (IMF) by using the empirical mode decomposition (EMD) method. The energy of every IMF and the singular value of the IMF matrix were chosen as features. The Shannon and Renyi entropy of the energy and singular value distribution were also extracted. Secondly genetic algorithm was used to reduce feature redundancy, with lowest classifier error rate and least feature amount as finess function. At last, a comparison was made between this method and least square support vector machine (LSSVM).The results showed that Lie group clkassifier was more sensitivce to feature. This method could use less feature amount to diagnose fault.

scholarly journals Vibration response of defect-ball-defect of rolling bearing with compound defects on both inner and outer races

2021 ◽  
Vol 1207 (1) ◽  
pp. 012006
Author(s):  
Wei Luo ◽  
Changfeng Yan ◽  
Junbao Yang ◽  
Yaofeng Liu ◽  
Lixiao Wu
Keyword(s):  
Rolling Bearing ◽  
Vibration Response ◽  
Radial Clearance ◽  
Rolling Bearings ◽  
Outer Race ◽  
Response Characteristics ◽  
Bearing Fault Diagnosis ◽  
Theoretical Criterion ◽  
Vibration Model ◽  
Rolling Elements

Abstract Aiming at the problem that the existing compound defects model of rolling bearings under radial load is difficult to reflect the actual contact between rolling elements and defects. A new model is proposed to accurately reflect the simultaneous or sequential contact between inner and outer race defects and rolling elements. Considering the coupled excitation between shaft and bearing and pedestal, time-varying displacement excitation, and radial clearance, a four degree-of-freedom vibration model of rolling bearing with compound faults on both inner and outer races is built. The vibration equations are calculated by the method of numerical way, and the model is verified by experiment. The vibration response characteristics of the Defect-Ball-Defect model are studied, which renders a theoretical criterion for bearing fault diagnosis.

Rolling Bearing Fault Diagnosis Based on Fractal Dimension

2012 ◽  
Vol 430-432 ◽  
pp. 2050-2053
Author(s):  
Meng Li
Keyword(s):  
Rolling Bearing ◽  
Box Dimension ◽  
Linear Vibration ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
New Approach ◽  
Bearing Fault Diagnosis ◽  
Rotating Machine ◽  
Non Linear ◽  
A New Technique

Fractal, as a new technique of signal processing, is suitable for analyzing the non-linear fault signals of rotating machine. By researching the characteristic of non-linear vibration signals of rolling bearings, a study of box dimension in analyzing the vibration signals and diagnosing the fault pattern of rolling bearings is proposed. Box dimension algorithm is presented in details and quantificational calculating of non-linear vibration signals generated by bearing system is also discussed. Experimental results show that kinematics mechanisms of rolling bearing result in the different working state, so the box dimensions are different evidently. The application of box dimension in monitoring working state is a new approach to promote the accuracy of rolling bearings.

Sign in / Sign up

Export Citation Format

Share Document