基于概念漂移检测的制冷系统故障诊断模型自适应
TU831.4%TP311.13%TP306.3; 制冷系统实际运行中,故障诊断模型可能出现诊断性能波动或下降等情况,需具备再学习能力以适应现场数据.本文设计了一种基于正确率阈值的概念漂移检测机制及支持向量机增量学习的故障诊断自适应模型,并将其应用于制冷剂过量故障的再学习.该算法通过两次优化选择、过滤数据信息,保留原有诊断知识,仅学习未知样本信息,可极大地节约模型学习时间,快速适应新环境.结果表明,新的故障种类出现时,诊断模型检测到概念漂移,进而通过增量学习进行自我更新,实现对新故障的学习与诊断.1400个过量故障样本中诊断模型只需要学习600个,且保证最终模型对后续数据流具有较佳诊断性能,正...
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| Published in | 制冷学报 Vol. 40; no. 4; pp. 121 - 128 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
上海理工大学能源与动力工程学院 上海 200093
01.08.2019
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-4339 |
| DOI | 10.3969/j.issn.0253-4339.2019.04.121 |
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| Abstract | TU831.4%TP311.13%TP306.3; 制冷系统实际运行中,故障诊断模型可能出现诊断性能波动或下降等情况,需具备再学习能力以适应现场数据.本文设计了一种基于正确率阈值的概念漂移检测机制及支持向量机增量学习的故障诊断自适应模型,并将其应用于制冷剂过量故障的再学习.该算法通过两次优化选择、过滤数据信息,保留原有诊断知识,仅学习未知样本信息,可极大地节约模型学习时间,快速适应新环境.结果表明,新的故障种类出现时,诊断模型检测到概念漂移,进而通过增量学习进行自我更新,实现对新故障的学习与诊断.1400个过量故障样本中诊断模型只需要学习600个,且保证最终模型对后续数据流具有较佳诊断性能,正确率高达99％.在现场制冷系统故障诊断应用中,诊断模型的再学习和自适应体现出良好的应用前景. |
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| AbstractList | TU831.4%TP311.13%TP306.3; 制冷系统实际运行中,故障诊断模型可能出现诊断性能波动或下降等情况,需具备再学习能力以适应现场数据.本文设计了一种基于正确率阈值的概念漂移检测机制及支持向量机增量学习的故障诊断自适应模型,并将其应用于制冷剂过量故障的再学习.该算法通过两次优化选择、过滤数据信息,保留原有诊断知识,仅学习未知样本信息,可极大地节约模型学习时间,快速适应新环境.结果表明,新的故障种类出现时,诊断模型检测到概念漂移,进而通过增量学习进行自我更新,实现对新故障的学习与诊断.1400个过量故障样本中诊断模型只需要学习600个,且保证最终模型对后续数据流具有较佳诊断性能,正确率高达99％.在现场制冷系统故障诊断应用中,诊断模型的再学习和自适应体现出良好的应用前景. |
| Author | 崔晓钰 范雨强 武浩 徐玲 韩华 |
| AuthorAffiliation | 上海理工大学能源与动力工程学院 上海 200093 |
| AuthorAffiliation_xml | – name: 上海理工大学能源与动力工程学院 上海 200093 |
| Author_FL | Wu Hao Xu Ling Fan Yuqiang Han Hua Cui Xiaoyu |
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| Author_xml | – sequence: 1 fullname: 武浩 – sequence: 2 fullname: 韩华 – sequence: 3 fullname: 崔晓钰 – sequence: 4 fullname: 范雨强 – sequence: 5 fullname: 徐玲 |
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| Keywords | 概念漂移 支持向量机增量学习 自适应学习 制冷系统 故障诊断 |
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| Title | 基于概念漂移检测的制冷系统故障诊断模型自适应 |
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