Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples

Authors

Jingjing Wei, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
Qinming Liu, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
Chunming Ye, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;
Guanlin Li, Business School, University of Shanghai for Science and Technology, Shanghai 200093, China;

Abstract

Abstract: In view of the equipment fault diagnosis with small and missing sample data, a method of missing data filling based on support vector regression optimized by genetic algorithm is proposed to improve the accuracy of equipment fault diagnosis. The support vector regression optimized by genetic algorithm was trained by other data values of missing data, and univariate prediction results were obtained. The training set was reconstructed through correlation analysis, so as to obtain the multivariate prediction results. Dynamic weights were established to combine univariate prediction results and multivariate prediction results to fill in the missing data. The complete data is taken as the input, and the equipment fault is diagnosed by support vector machine. Example analysis shows that the method proposed in this paper has a high fault diagnosis accuracy.

Recommended Citation

Wei, Jingjing; Liu, Qinming; Ye, Chunming; and Li, Guanlin (2021) "Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples," Journal of System Simulation: Vol. 33: Iss. 6, Article 13.
DOI: 10.16182/j.issn1004731x.joss.20-0046
Available at: https://dc-china-simulation.researchcommons.org/journal/vol33/iss6/13

First Page

1342

Revised Date

2020-03-17

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.20-0046

Last Page

1349

CLC

TP29;TP391

Recommended Citation

Wei Jingjing, Liu Qinming, Ye Chunming, Li Guanlin. Fault Diagnosis of Mechanical Equipment Based on GA-SVR with Missing Data in Small Samples[J]. Journal of System Simulation, 2021, 33(6): 1342-1349.

DOI

10.16182/j.issn1004731x.joss.20-0046