|
|||||
|
|
| 基于人工免疫的故障诊断模型及其应用 | |
| 引用本文： | 陈强,郑德玲,李湘萍.基于人工免疫的故障诊断模型及其应用[J].北京科技大学学报,2007,29(10):1041-1045. |
| 作者姓名： | 陈强 郑德玲 李湘萍 |
| 作者单位： | 1. 北京科技大学信息工程学院,北京,100083;江西理工大学机电学院,赣州,341000 2. 北京科技大学信息工程学院,北京,100083 3. 江西理工大学继教学院,赣州,341000 |
| 基金项目： | 教育部高等学校博士学科点专项科研基金 |
| 摘 要： | 提出了一种基于免疫原理的故障检测及诊断系统模型. 通过对检测对象正常工作状态下获得的自己模式串的阴性选择,随机产生初始检测器;利用基于人工免疫的进化学习机制,实现对检测对象异常工作状态下获得的非己模式串进行学习和记忆;利用进化学习结果和系统故障信息库知识,区分和标记不同故障在状态空间上对应的区域. 将抗原学习过程中抗体集合变异所产生的各代抗体集合看作随机序列,给出了序列的收敛条件及证明,证明了所提出的动态免疫进化学习算法是概率弱收敛. 应用于机床齿轮箱故障检测和诊断问题的实验结果表明了所提出方法的有效性. |
| 关 键 词： | 人工免疫 进化学习 异常检测 故障诊断 人工免疫 故障检测 诊断模型 应用 application immunity artificial based model diagnosis 有效性 方法 结果 实验 诊断问题 齿轮箱 机床 概率弱收敛 学习算法 免疫进化 |
| 修稿时间： | 2006-05-29 |
Faults diagnosis model based on artificial immunity and its application |
|
| CHEN Qiang,ZHENG Deling,LI Xiangping.Faults diagnosis model based on artificial immunity and its application[J].Journal of University of Science and Technology Beijing,2007,29(10):1041-1045. | |
| Authors: | CHEN Qiang ZHENG Deling LI Xiangping |
| Institution: | 1. Information Engineering School, University of Science and Technology Beijing, Beijing 100083, China;2. Machinery and Power-generating Equipment Engineering School, Jiangxi University of Science and Technology, Ganzhou 341000, China;3. Continue Education College, Jiangxi University of Science and Technology, Ganzhou 341000, China |
| Abstract: | A sort of system for faults detection and diagnosis based on the immunology principle was presented.Initial detectors were produced at random combining negative selection of self-patterns which response normal working situation of detecting objects.The learning and memory of non-self-patterns which response abnormal working situation of detecting objects were realized using the mechanism of evolution leaning based on the artificial immune theory.The corresponding zones of different faults on states space were distinguished and marked using the results of evolution learning and information warehouse of faults.Regarding the set of each era antibodys mutated in the system learning as a random series,the condition of convergence of the series and a proof were presented.The algorithm's astringency was proved.Appling the method in detection and diagnosis for faults of gear case of machine tools,the experimental results indicate that the method is effective. |
| Keywords: | artificial immunity evolution and learning anomaly detection fault diagnosis |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ | |