| 基于SVM的岸桥起升电动机工作状态分类 |
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| 引用本文： | 唐刚,李庆中,胡雄.基于SVM的岸桥起升电动机工作状态分类[J].上海海事大学学报,2019,40(2):78-82. |
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| 作者姓名： | 唐刚 李庆中 胡雄 |
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| 作者单位： | 上海海事大学物流工程学院 |
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| 基金项目： | 国家自然科学基金 (31300783)；国家高技术研究发展计划(“八六三”计划)(2013A20411606)；中国博士后科学基金(2014M561458)；教育部博士点基金 (20123121120004)；上海高校一流学科——管理科学与工程资助项目；上海海事大学科研基金 (20130474) |
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| 摘 要： | 为解决岸桥起升电动机的状态识别和实时监测问题,分析起升电动机的大量样本集,提出基于标准差的模糊 C 均值(standard deviation-based fuzzy C-means,S-FCM)聚类算法对起升电动机的状态进行聚类分析,并构建两种SVM模型。实验结果表明:起升电动机振动烈度可以聚类为4类。对两种SVM模型进行对比和验证,选出最理想的实时监测模型。该方法可以为设备维护保养提供依据并且可以实时在线监测岸桥起升电动机的工作状态。
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| 关 键 词： | 起升电动机 模糊 C 均值(FCM)聚类 工况分类 支持向量机(SVM) |
| 收稿时间： | 2018/5/28 0:00:00 |
| 修稿时间： | 2019/1/9 0:00:00 |
Classification of hoisting motor working states of quay cranes based on SVM |
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| TANG Gang,LI Qingzhong,HU Xiong.Classification of hoisting motor working states of quay cranes based on SVM[J].Journal of Shanghai Maritime University,2019,40(2):78-82. |
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| Authors: | TANG Gang LI Qingzhong HU Xiong |
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| Institution: | Shanghai Maritime University Logistics Engineering College |
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| Abstract: | In order to solve the problem of state recognition and real time monitoring of hoisting motors of quay cranes, a large number of samples of hoisting motors are analyzed, and the standard deviation based fuzzy C means (S FCM) clustering algorithm is proposed. The working states of hoisting motors are clustered and two support vector machine (SVM) models are constructed. The experimental results show that the vibration intensity of hoisting motors can be clustered into four categories. The two SVM models are compared and verified to select the optimal real time monitoring model. The method can provide a basis for equipment maintenance and real time on line monitoring of the working states of the quay crane hoisting motors. |
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| Keywords: | hoisting motor fuzzy C means (FCM) clustering working condition classification support vector machine (SVM) |
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