Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain

Liu Zhigang; Zhao Xiaoyan; Zhang Tao; Ao Baolin; Wang Juntao; Dang Qiqian

doi:10.16578/j.issn.1004.2539.2018.09.032

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Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain

更新时间：2022-10-20

- Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain
- Vol. 42, Issue 9, Pages: 164-167(2018)
- 作者机构：
  
  1. 河南工业职业技术学院机械工程学院
  2. 华北水利水电大学机械学院
  3. 郑州机械研究所有限公司
  4. 安阳钢铁股份有限公司
- 作者简介：
- 基金信息：
- DOI：10.16578/j.issn.1004.2539.2018.09.032
  CLC：
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[1]刘志刚,赵晓燕,张涛,敖宝林,王俊涛,党齐乾.风电机组传动链故障智能识别算法研究[J].机械传动,2018,42(09):164-167.

Liu Zhigang, Zhao Xiaoyan, Zhang Tao, et al. Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain. [J]. 42(9):164-167(2018)
[1]刘志刚,赵晓燕,张涛,敖宝林,王俊涛,党齐乾.风电机组传动链故障智能识别算法研究[J].机械传动,2018,42(09):164-167. DOI： 10.16578/j.issn.1004.2539.2018.09.032.

Liu Zhigang, Zhao Xiaoyan, Zhang Tao, et al. Study on the Intelligent Fault Recognition Algorithm for Wind Power Unit Drivetrain. [J]. 42(9):164-167(2018) DOI： 10.16578/j.issn.1004.2539.2018.09.032.

摘要

为了提高风电机组传动链的可靠性,提出了一种量子遗传算法优化支持向量机的故障诊断模型。首先确定支持向量机的模型,然后采用量子遗传算法对惩罚参数和核函数系数进行优化。算法使用量子位编码和量子旋转门实现了对初始种群的编码和更新,提高了优化求解的精确度。通过使用优化后的支持向量机模型对传动链的正常工况、表面磨损和齿轮缺齿等3种类型信息的分类诊断,可以有效解决故障诊断的准确率。

Abstract

In order to improve reliability of wind power unit drivetrain,a fault diagnosis model based on quantum genetic algorithm and support vector machine（ SVM） is presented. The model of SVM is conformed,and the penalty parameter and Kernel function coefficient are optimized by quantum genetic algorithm,which coding and renewal of initial population are completed with quantum encoding and rotation gate,the accuracy of optimal solution is improved. Through using the optimized SVM model,with the test and calculation for drivetrain in three types of normal condition,surface wear and missing teeth,the accuracy rate of fault diagnosis can be effectively solved.

关键词

风电机组传动链故障诊断支持向量机量子遗传算法

Keywords

Wind power unitDrivetrainFault diagnosisSupport vector machineQuantum genetic algorithm

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Department of Mechanical Engineering, Guangzhou Maritime University

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