基于智能算法的稳定点自动分析方法研究
U441.3%TB123%TH17; 为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇...
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| Published in | 振动、测试与诊断 Vol. 45; no. 1; pp. 65 - 72 |
|---|---|
| Main Authors | , , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
东南大学土木工程学院 南京,210096%东南大学土木工程学院 南京,210096%山东省交通规划设计院集团有限公司 济南,250101
01.02.2025
北京建筑大学土木与交通工程学院 北京,100044%北京建筑大学土木与交通工程学院 北京,100044 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1004-6801 |
| DOI | 10.16450/j.cnki.issn.1004-6801.2025.01.010 |
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| Abstract | U441.3%TB123%TH17; 为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇中值作为模态参数的代表值,实现模态参数的自动化识别;最后,将含有密集模态的外滩大桥作为识别对象进行试验验证.试验结果表明:所提出方法具有较高的精度,与频域分解(frequency domain decomposition,简称FDD)法的频率结果最大相差仅为0.012 3 Hz,且在线识别的准确率达到82.86%,显著高于基于层次聚类的自动识别方法,实现了无人工干预下模态参数的自动、准确识别,具有一定的工程应用前景. |
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| AbstractList | U441.3%TB123%TH17; 为了提高辨识稳定图中真实模态的准确性与自动化程度,首先,从稳定点定义方式的角度论述了聚类算法效果欠佳的原因,并采用异阶系统非等权重的定义方式输出稳定点;其次,基于数据挖掘思想,采用改进的辨识聚类结构的有序点(ordering points to identify the clustering structure,简称OPTICS)算法自动清洗稳定点集,通过遍历性搜索的方式确定输入参数;然后,提出结合度矩阵去噪的自适应局部密度谱聚类(local density adaptive spectral clustering,简称SC-DA)算法分析稳定点集,并以簇中值作为模态参数的代表值,实现模态参数的自动化识别;最后,将含有密集模态的外滩大桥作为识别对象进行试验验证.试验结果表明:所提出方法具有较高的精度,与频域分解(frequency domain decomposition,简称FDD)法的频率结果最大相差仅为0.012 3 Hz,且在线识别的准确率达到82.86%,显著高于基于层次聚类的自动识别方法,实现了无人工干预下模态参数的自动、准确识别,具有一定的工程应用前景. |
| Abstract_FL | To enhance the effectiveness and degree of automation in identifying physical modes within the stabi-lization diagram,the reason for the inadequate performance of clustering analysis is firstly examined from the perspective of the traditional stable pole definition.Moreover,the definition where systems at various order are assigned unequal weights is employed.Secondly,the improved ordering points to identify the clustering struc-ture(OPTICS)algorithm is adopted to automatically refine the stable pole set,with input parameters deter-mined through a traversal search.Finally,an improved local density adaptive spectral clustering(SC-DA)algo-rithm,which involves a new degree-matrix-based denoising method,is proposed to cluster the stable pole set.The cluster median is utilized as the representative value for modal parameters.Taking the Waitan Bridge as the test object,the results show that the maximum frequency difference between the proposed method and the fre-quency domain decomposition(FDD)method is only 0.012 3 Hz.Additionally,the success rate of online identi-fication is 82.86%,which is superior to the established method based on hierarchical clustering.The proposed method can automatically and accurately identify modal parameters without human intervention,indicating promising prospects for engineering applications. |
| Author | 邓扬 张超 李爱群 钟国强 周泰翔 李雨航 |
| AuthorAffiliation | 北京建筑大学土木与交通工程学院 北京,100044%北京建筑大学土木与交通工程学院 北京,100044;东南大学土木工程学院 南京,210096%东南大学土木工程学院 南京,210096%山东省交通规划设计院集团有限公司 济南,250101 |
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| Author_FL | ZHONG Guoqiang ZHANG Chao ZHOU Taixiang DENG Yang LI Aiqun LI Yuhang |
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| DOI | 10.16450/j.cnki.issn.1004-6801.2025.01.010 |
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| DocumentTitle_FL | Automatic Analysis Method of Stable Poles Based on Intelligent Algorithm |
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| Keywords | stochastic subspace identification(SSI) 稳定图 ordering points to identify the cluster-ing structure(OPTICS)algorithm stabilization diagram modal parameter identification 自动化 随机子空间法 模态参数识别 automation 辨识聚类结构的有序点算法 local density adaptive spectral clustering(SC-DA)algorithm cluster analysis 聚类分析 自适应局部密度谱聚类算法 |
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| PublicationTitle | 振动、测试与诊断 |
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| Publisher | 东南大学土木工程学院 南京,210096%东南大学土木工程学院 南京,210096%山东省交通规划设计院集团有限公司 济南,250101 北京建筑大学土木与交通工程学院 北京,100044%北京建筑大学土木与交通工程学院 北京,100044 |
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| Title | 基于智能算法的稳定点自动分析方法研究 |
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