基于改进鲸鱼算法优化支持向量机的故障诊断的研究与应用
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TP183

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吉林省重点科技研发项目(专项支持)(20180201014YY)


Research and Application of a Fault Diagnosis Based on Improved Whale Algorithm to Optimize Support Vector Machine
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Key Science and Technology Research and Development Project of Jilin Province (Special Support), No.20180201014YY

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    摘要:

    故障诊断在工业生产过程中具有很重要的作用,尤其是对于要求比较高的分子蒸馏来说,微小的故障都会造成其提纯率,因此本文提出一种基于改进鲸鱼算法优化支持向量机的故障分类方法(IWOA-SVM),加入反向学习策略和对数权重因子到普通鲸鱼算法中。首先用反向学习策略(OBL)代替随机初始种群,用反向学习策略选取出反向种群,对种群进行择优选择,一方面OBL能够高效的提高群智能算法的全局搜索能力,另一方面提高鲸鱼算法在重复迭代中的多样性,使其跳出局部最优解,然后引入自适应权重因子并将其加入到鲸鱼优化算法中,利用权重因子的动态变化,很大程度上增强了全局搜索能力。最后采用改进之后的鲸鱼算法对SVM的参数进行寻优,并利用优化之后的支持向量机对刮膜蒸发过程获得的故障数据进行诊断识别,将IWOA-SVM的结果与WOA-SVM、SVM、PSO-SVM以及GWO-SVM做对比。结果表明,相比之下本文提出的IWOA-SVM算法分类准确率提升了2%,且其准确率保持在98%以上,IWOA-SVM在分类结果的准确性以及算法的鲁棒性方面于其他算法。

    Abstract:

    Fault diagnosis has the very important role in the process of industrial production, especially for high molecular distillation, small fault will cause its purification rate, therefore a algorithm based on improved whales to optimize the fault classification method of support vector machine (IWOA - SVM)is proposed in this paper, and the reverse learning strategies and logarithmic weighting factor are added into the ordinary whale algorithm. Firstly, the reverse learning strategy (OBL) is used to replace the random initial population, and the reverse population is selected by the reverse learning strategy. On the one hand, the global search ability of the swarm intelligence algorithm can be effectively improved by OBL, and on the other hand, the diversity of the whale algorithm in repeated iteration can be refined, and make it jump out of the local optimal solution. Then, the adaptive weight factor is introduced and added to the whale optimization algorithm, and the global search ability is greatly enhanced by the dynamic change of the weight factor. Finally, the improved whale algorithm was used to optimize the parameters of SVM, and the optimized support vector machine was used to diagnosis and identify the fault data obtained in the process of scraping evaporation, and the results of IWOA-SVM were compared with WOA-SVM, SVM, PSO-SVM and GWO-SVM. The results show that the classification accuracy of IWOA-SVM proposed in this paper increases by 2%, and its accuracy remains above 98%. It is concluded that IWOA-SVM is better than other algorithms in terms of the accuracy of classification results and the robustness of the algorithm.

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李慧,徐海亮,王浩,等. 基于改进鲸鱼算法优化支持向量机的故障诊断的研究与应用[J]. 科学技术与工程, 2022, 22(13): 5284-5290.
Li Hui, Xu Hailiang, Wang Hao, et al. Research and Application of a Fault Diagnosis Based on Improved Whale Algorithm to Optimize Support Vector Machine[J]. Science Technology and Engineering,2022,22(13):5284-5290.

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  • 收稿日期:2021-06-24
  • 最后修改日期:2021-11-11
  • 录用日期:2021-11-16
  • 在线发布日期: 2022-05-20
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