| 基于Logistic回归的电梯健康评估 |
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| 引用本文： | 潘鹏,王廷银,潘健鸿,吴海燕,金晓磊,樊明辉,吴允平.基于Logistic回归的电梯健康评估[J].计算机系统应用,2018,27(10):255-260. |
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| 作者姓名： | 潘鹏 王廷银 潘健鸿 吴海燕 金晓磊 樊明辉 吴允平 |
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| 作者单位： | 福建师范大学 光电与信息工程学院, 福州 350007,福建师范大学 光电与信息工程学院, 福州 350007,福建省特种设备检验研究院, 福州 350008,福建师范大学 数学与信息学院, 福州 350007;数字福建环境监测物联网实验室, 福州 350007,福州大学 物理与信息工程学院, 福州 350108,福州大学 物理与信息工程学院, 福州 350108,福建师范大学 光电与信息工程学院, 福州 350007 |
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| 基金项目： | 国家自然科学基金面上项目（61175123）；福建省自然科学基金面上项目（2015J01238） |
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| 摘 要： | 电梯的隐患故障与其运行状态存在着一定的关联性.根据电梯的组成要素、体现电梯健康状况表征，合理选择评价参数，基于logistic回归方法建立评价模型.通过分析评价模型基本原理，整合原始数据、引入惩罚因子、交叉验证、高阶拟线性等方法，解决了数据不均衡现状和差异性，提高了评价模型准确度，达到了对电梯健康实时监控并预警.
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| 关 键 词： | logistic回归 交叉验证 数据不均衡 过拟合 |
| 收稿时间： | 2018/1/20 0:00:00 |
| 修稿时间： | 2018/2/9 0:00:00 |
Elevator Status Estimate Based on Logistic Regression |
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| PAN Peng,WANG Ting-Yin,PAN Jian-Hong,WU Hai-Yan,JIN Xiao-Lei,FAN Ming-Hui and WU Yun-Ping.Elevator Status Estimate Based on Logistic Regression[J].Computer Systems& Applications,2018,27(10):255-260. |
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| Authors: | PAN Peng WANG Ting-Yin PAN Jian-Hong WU Hai-Yan JIN Xiao-Lei FAN Ming-Hui and WU Yun-Ping |
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| Affiliation: | College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China,College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China,Fujian Special Equipment Inspection and Research Institute, Fuzhou 350008, China,College of Mathematics and Informatics, Fujian Normal University, Fuzhou 350007, China;Digit Fujian Internet-of-Things Laboratory of Environmental Monitoring, Fujian Normal University. 350007, China,College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China,College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China and College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou 350007, China |
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| Abstract: | There are some associations between potential failures and running states of elevators. According to the key elements of elevators, the assessment model based on logistic regression algorithm is established by choosing assessment parameters and characterization reflecting the status of elevators. Through analyzing the evaluating model''s basic principles, preprocessing original data, introducing the methods of penalty factor, cross validation, and high order pseudo linear, unbalanced data and difference issues are solved, and the evaluating model''s accuracy is improved, thus the real-time supervision and pre-warning of elevators'' status are established. |
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| Keywords: | logistic regression cross validation unbalanced data overfitting |
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