Research article Special Issues

Studies on fault diagnosis of dissolved oxygen sensor based on GA-SVM

  • Received: 24 August 2020 Accepted: 25 November 2020 Published: 04 December 2020
  • The present research envisaged the analysis of the dissolved oxygen fault of the water quality monitoring system using the genetic algorithm-support vector machine (GA-SVM). The real-time data collected by the dissolved oxygen sensor was classified into the fault types. The fault types were divided into complete failure fault, impact fault, and constant output fault. Based on the fault classification of the dissolved oxygen parameters, SVM fault diagnosis experiments were conducted. Experimental results show that the accuracy of dissolved oxygen was 98.53%. On comparison with the experimental results of the back propagation (BP) neural network, it was found that the diagnosis results of the dissolved oxygen parameters using SVM were better than those of the BP neural network. The genetic algorithm (GA) was used to optimize the parameters. After iteration, the optimal parameters such as C and g were selected (C is the penalty coefficient, which adjusts the weight of the two index preferences in the optimization direction, i.e., the tolerance for errors, and g is a parameter that comes with the function that implicitly determines the distribution of the data after mapping to the new feature space.). By using GA, after iteration, the optimized values of C and g was found to be 2.1649 and 5.3312, respectively. The experimental results showed that the method exhibited a good accuracy.

    Citation: Pu Yang, Zhenbo Li, Yaguang Yu, Jiahui Shi, Ming Sun. Studies on fault diagnosis of dissolved oxygen sensor based on GA-SVM[J]. Mathematical Biosciences and Engineering, 2021, 18(1): 386-399. doi: 10.3934/mbe.2021021

    Related Papers:

  • The present research envisaged the analysis of the dissolved oxygen fault of the water quality monitoring system using the genetic algorithm-support vector machine (GA-SVM). The real-time data collected by the dissolved oxygen sensor was classified into the fault types. The fault types were divided into complete failure fault, impact fault, and constant output fault. Based on the fault classification of the dissolved oxygen parameters, SVM fault diagnosis experiments were conducted. Experimental results show that the accuracy of dissolved oxygen was 98.53%. On comparison with the experimental results of the back propagation (BP) neural network, it was found that the diagnosis results of the dissolved oxygen parameters using SVM were better than those of the BP neural network. The genetic algorithm (GA) was used to optimize the parameters. After iteration, the optimal parameters such as C and g were selected (C is the penalty coefficient, which adjusts the weight of the two index preferences in the optimization direction, i.e., the tolerance for errors, and g is a parameter that comes with the function that implicitly determines the distribution of the data after mapping to the new feature space.). By using GA, after iteration, the optimized values of C and g was found to be 2.1649 and 5.3312, respectively. The experimental results showed that the method exhibited a good accuracy.


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