Fault Diagnosis of Reverse Osmosis Water Desalination Based on Optimized Support Vector Machine

Authors

Zhang Biao, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Jianfeng Xing, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;
Zhicheng Ji, Institute of Electrical Automation, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: According to the reverse osmosis membrane fault problems in reverse osmosis water desalination system, a fault diagnosis method based on support vector machine (SVM) was introduced for fault diagnoses. To solve the problem of parameter optimization in SVM, an improved chaos particle swarm algorithm was proposed. The introduction of Chaos theory to particle swarm optimization algorithm may not only enhance the diversity of the population and particle global search ability, but also improve the convergence speed and accuracy of the particle swarm algorithm. The optimized SVM model was applied to the fault diagnosis of reverse osmosis water desalination system. The simulation results show that the improved SVM classifier can effectively diagnose the reverse osmosis membrane fault diagnosis and achieve a higher diagnostic accuracy and efficiency.

Recommended Citation

Biao, Zhang; Xing, Jianfeng; and Ji, Zhicheng (2020) "Fault Diagnosis of Reverse Osmosis Water Desalination Based on Optimized Support Vector Machine," Journal of System Simulation: Vol. 27: Iss. 5, Article 20.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol27/iss5/20

First Page

1057

Revised Date

2014-08-11

DOI Link

https://doi.org/

Last Page

1063

CLC

TP277

Recommended Citation

Zhang Biao, Xing Jianfeng, Ji Zhicheng. Fault Diagnosis of Reverse Osmosis Water Desalination Based on Optimized Support Vector Machine[J]. Journal of System Simulation, 2015, 27(5): 1057-1063.