| 基于杂草算法优化的轴承时频谱图聚类识别研究 |
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| 引用本文： | 林龙,王贤浪. 基于杂草算法优化的轴承时频谱图聚类识别研究[J]. 内燃机与配件, 2021, 0(2): 117-120 |
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| 作者姓名： | 林龙 王贤浪 |
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| 作者单位： | 台州科技职业学院汽车与信息工程学院,台州318020;台州市黄岩鑫城车辆检测中心有限公司,台州318020 |
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| 基金项目： | 2019年度浙江省高等学校国内访问工程师项目(编号FG2019222)。 |
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| 摘 要： | 针对KFCM算法对初始聚类中心敏感导致聚类效果不好等问题,提出一种基于杂草算法(IWO)优化的模糊核聚类算法(IWO-KFCM),将其运用于轴承时频谱图的状态识别.通过小波变换获取轴承运行状态的时频图像,利用灰度梯度共生矩阵提取图像的纹理特征,提出基于可分性测度构造IWO算法的适应度函数;将IWO算法优化获取的初始聚类...
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| 关 键 词： | 时频图像 灰度梯度共生矩阵 杂草算法 可分性测度 半监督KFCM 轴承故障 |
A Time-frequency Image Recognition Method based on IWO-KFCM Algorithm |
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| LIN Long,WANG Xian-lang. A Time-frequency Image Recognition Method based on IWO-KFCM Algorithm[J]. Internal Combustion Engine & Parts, 2021, 0(2): 117-120 |
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| Authors: | LIN Long WANG Xian-lang |
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| Affiliation: | (School of Automobile and Information Engineering,Taizhou Vocational College of Science and Technology,Taizhou 318020,China;Taizhou Huangyan Xincheng Vehicle Testing Center Co.,Ltd.,Taizhou 318020,China) |
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| Abstract: | KFCM algorithm is sensitive to the initial cluster center and leads tolow cluster accuracy.In this paper,a fuzzy kernel cluster algorithm based on invasive weed optimization(IWO-KFCM)is proposed to identifytime-frequency images.Firstly,time-frequency images of the bearing states are obtained through wavelet transform,and GLCM is used to extract the texture features of images.Low dimensional features of high contribution rates can be selected via PCA.This algorithmdefines separability criterion as an evaluation of fitness function,and IWOseeks the optimal solution as the initial cluster centers of KFCM.Finally,the IWO-KFCMis used to clusterdata,andexperiment results of the mutli-class bearing datasets demonstrate the effectiveness and superiority of the proposed algorithm. |
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| Keywords: | time-frequency image GLCM invasive weed optimization separability criterion semi-supervised KFCM bearing fault |
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