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计算机工程 ›› 2013, Vol. 39 ›› Issue (3): 187-190,196. doi: 10.3969/j.issn.1000-3428.2013.03.037

• 人工智能及识别技术 • 上一篇    下一篇

基于改进粒子群优化的SVM故障诊断方法

杨柳松1,何光宇2   

  1. (1. 东北林业大学机电工程学院,哈尔滨 150040;2. 空军工程大学工程学院,西安 710038)
  • 收稿日期:2012-04-20 出版日期:2013-03-15 发布日期:2013-03-13
  • 作者简介:杨柳松(1979-),女,讲师、博士研究生,主研方向:支持向量机技术;何光宇,工程师、博士
  • 基金资助:
    中央高校基本科研业务费专项基金资助项目(DL11BB32);黑龙江省科技厅自然科学基金资助项目(F201028)

Support Vector Machine Fault Diagnosis Method Based on Improved Particle Swarm Optimization

YANG Liu-song 1, HE Guang-yu 2   

  1. (1. College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin 150040, China; 2. Engineering Institute, Air Force Engineering University, Xi’an 710038, China)
  • Received:2012-04-20 Online:2013-03-15 Published:2013-03-13

摘要: 针对支持向量机(SVM)分类模型参数选取困难的问题,提出基于遗传免疫的改进粒子群优化算法,克服传统粒子群算法前期收敛快、后期易陷入局部最优的缺陷。将该算法与优化支持向量机分类模型相结合,建立基于遗传免疫粒子群和支持向量机的诊断模型,并用于轴承故障诊断中。结果表明,基于遗传免疫粒子群算法优化的SVM可实现对SVM分类模型参数的自动优化,并能提高SVM分类模型的故障诊断精度,对分散程度较大、聚类性较差的故障样本分类有较强的适用性。

关键词: 支持向量机, 故障诊断, 粒子群优化, 遗传免疫, 轴承, 交叉验证

Abstract: In order to resolve the difficulty that the choice of parameters influence the accuracy of Support Vector Machine(SVM) fault diagnosis model, a genetic-immune Particle Swarm Optimization(PSO) algorithm based on genetic evolution algorithm and immune selection algorithm is presented and used to optimize model parameters of SVM. The forecasting model based on a genetic-immune PSO algorithm and SVM is proposed and used to diagnose bearing fault. The results show that diagnosis model of SVM optimized by genetic-immune PSO algorithm can achieve automatic optimization of parameters, increase diagnosis accuracy of the conventional cross-validation algorithm, and is more fitting to classify the faulty samples scattered greatly.

Key words: Support Vector Machine(SVM), fault diagnosis, Particle Swarm Optimization(PSO), genetic-immune, bearing, cross-validation

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