Fault Classification of Low-Speed Bearings Based on Support Vector Machine for Regression and Genetic Algorithms Using Acoustic Emission

In the present work, a multi-fault classification of gears has been attempted by the support vector machine learning technique using the vibration data in time domain. A proper utilization of the support vector machine is based on the selection of support vector machine parameters. The main focus of this article is to examine the performance of the multiclass ability of support vector machine techniques by optimizing its parameters using the grid-search method, genetic algorithm and artificial bee colony algorithm. Four fault conditions were considered. A group of statistical features were extracted from time domain data. The prediction of fault classification is attempted at the same angular speed as the measured data as well as innovatively at the intermediate and extrapolated angular speed conditions. This is due to the fact that it is not feasible to have measurement of vibration data at all continuous speeds of interest. The classification ability is noted and it shows an excellent prediction performance.

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Study on Fault Classification of Power-Shift Steering Transmission Based on v-Support Vector Machine

The 19th International Conference on Industrial Engineering and Engineering Management ◽

10.1007/978-3-642-38433-2_70 ◽

2013 ◽

pp. 647-654 ◽

Cited By ~ 1

Author(s):

Yuan Zhu ◽

Ying-feng Zhang ◽

Ai-yong Du

Keyword(s):

Support Vector Machine ◽

Fault Classification ◽

Support Vector ◽

Power Shift

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Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.628.383 ◽

2014 ◽

Vol 628 ◽

pp. 383-389 ◽

Cited By ~ 6

Author(s):

Ya Hui Peng ◽

Kang Peng ◽

Jian Zhou ◽

Zhi Xiang Liu

Keyword(s):

Support Vector Machine ◽

Genetic Algorithms ◽

Rock Burst ◽

Support Vector ◽

Proposed Model ◽

Svm Model ◽

Rock Burst Risk ◽

Rock Burst Prediction ◽

Optimization Search

Due to the complex features of rock burst hazard assessment systems, a support vector machine (SVM) model for predicting of classification of rock burst was established based on the SVM theory and the actual characteristics of the project in this study. The main factors of rock burst, such as coal seam, dip, buried depth, structure situation, change of pitch angle, change of coal thickness, gas concentration, roof management, pressure relief and shooting were defined as the criterion indices for rock burst prediction in the proposed model. In order to determine reasonable and efficient the parameters of SVM, Firstly, the appropriate fitness function for genetic algorithms (GA) operation was determined, and then optimization parameters of SVM model were selected by real coded GA, therefore, the genetic algorithms and support vector machine (GSVM) model was established. A GSVM model was obtained through training 23 sets of measured data, the cross-validation method was introduced to verify the stability of GSVM model and the ratio of mis-discrimination is 0. Moreover, the proposed model was used to predict 12 new samples rock burst, the correct rate of prediction results is 91.6667% and are identical with actual situation. The results show that the genetic algorithm can speed up SVM parameter optimization search, the proposed model has a high credibility in the study of rock burst prediction of risk classification, which can be applied to practical engineering.

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