Evolutionary Soft Subspace Clustering Method for Fault Diagnosis of High-Dimensional Features

doi:10.7652/xjtuxb201305021

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Evolutionary Soft Subspace Clustering Method for Fault Diagnosis of High-Dimensional Features

更新时间：2025-07-09

- Evolutionary Soft Subspace Clustering Method for Fault Diagnosis of High-Dimensional Features
- Vol. 47, Issue 5, Pages: 115-120(2013)
- 作者机构：
  
  西安交通大学机械工程学院,西安,710049
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb201305021
  CLC： TP391.41
- Online First：10 May 2013，
  
  Published：2013
- 稿件说明：
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Evolutionary Soft Subspace Clustering Method for Fault Diagnosis of High-Dimensional Features[J]. 2013, 47(5): 115-120.
DOI：

Evolutionary Soft Subspace Clustering Method for Fault Diagnosis of High-Dimensional Features[J]. 2013, 47(5): 115-120. DOI： 10.7652/xjtuxb201305021.

摘要

针对复杂机械设备故障诊断中特征量众多且对各种故障敏感程度不同的现象

提出了采用软子空间聚类算法来实现故障的识别方法。同时

针对传统软子空间聚类易陷入局部最优

目标函数设计受限制的缺点

又提出了采用进化计算实现聚类的方法。利用同类样本在相关特征维上方差小的假设

新的目标函数能更好地评价聚类结果的质量。在该算法中

通过设计类中心和权重值的混合编码以及聚类导向搜索算子

使算法更适于聚类问题的优化

而且设计的修复算子可有效地去除不合理的聚类结果。采用5组UCI数据集、2组轴承滚珠故障数据集和3组往复式压缩机气阀故障数据集对算法进行了测试

结果表明:该算法明显好于几种的软子空间聚类算法

在Rand指标上最多可高出0.226 6

并且对2组不同工况下一级缸气阀故障可实现100%的故障识别。

Abstract

Many features can be collected in a complex machine

however

using all these features for fault diagnosis is inappropriate due to different sensitivity of each feature. A soft subspace clustering schedule is proposed. A novel evolutionary algorithm is suggested to avoid the shortages in the traditional soft subspace clustering

such as local optimum and constraint in designing objective function. Under the hypothesis that the samples in the same cluster have small variance in relevant dimensions

a new objective function is constructed in the proposed algorithm to optimize. Considering the special meaning of each gene

a new encoding frame and a cluster-oriented search operator are designed. A repair operator enables to eliminate some meaningless individuals in the evolution process. The experiments on five UCI

two bearing fault and three valve fault of reciprocating compressor datasets demonstrate the better performance of the new algorithm. It is at most 0.226 6 higher than other competitive soft subspace clustering methods on Rand index(RI)

especially for two of the valves

fault diagnosis rate gets 100%.

关键词

Keywords

references

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