Granular Computing with Application to Fault Diagnosis

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Granular Computing with Application to Fault Diagnosis

更新时间：2025-07-09

- Granular Computing with Application to Fault Diagnosis
- Vol. 43, Issue 9, Pages: 37-41(2009)
- 作者机构：
  
  西安交通大学机械制造系统工程国家重点实验室,西安,710049
- 作者简介：
- 基金信息：
- DOI：
  CLC： TH17;TP183
- Online First：10 September 2009，
  
  Published：2009
- 稿件说明：
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Granular Computing with Application to Fault Diagnosis[J]. 2009, 43(9): 37-41.
DOI：

Granular Computing with Application to Fault Diagnosis[J]. 2009, 43(9): 37-41. DOI：

摘要

为了更好地揭示原发性、早期微弱以及复合故障的发生发展规律

以粒计算理论为基础

提出了基于相容粒度空间模型的智能故障诊断方法.该方法通过层次化和粒度化来表示原始数据中蕴涵的不同设备运行状态的信息

使得不同故障状态被高效地映射到粒结构的不同层次上

达到正确区分各类故障的目的.利用得到的约简属性集构建相容粒度空间模型

对电力机车轮对轴承的早期微弱故障、严重故障以及复合故障进行诊断

取得了较高的分类精度.实验结果表明

与RBF神经网络方法相比

模型有着很高的分类性能

对9类故障状态的分类准确率达到了91.11%

说明相容粒度空间模型在机械故障诊断方面具有很好的分类性能.由于约简方法可以弥补特征评估技术不能直接获得最优特征组合的不足

因此提高了模型的工作效率.

Abstract

To get accurate diagnosis of the faults in mechanical equipments

especially the early stage weak and the compound faults

a new approach to intelligent fault diagnosis of the machinery based on granular computing(GrC)is proposed. A complicate problem can be divided into several small ones which are easily understood and solved according to the idea of GrC. The tolerance granularity space mode is constructed by means of the inner-class distance defined in the attributes space. The fault information can be decomposed into different granularity levels

and be clearly analyzed at each level. To improve the diagnosis accuracy

a reduction method of the fault features based on GrC is also proposed

which directly gets the minimal reduction to construct the tolerance granularity space mode for the best classification accuracy. The proposed approach is applied to the fault diagnosis of locomotive bearing. The results show this mode is endowed with better classification performance than RBF neural network

and the attribute reduction method provide a good way to improve the diagnosis efficiency.

关键词

Keywords

references

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雷亚国,何正嘉,訾艳阳,等. 基于特征评估和神经网络的机械故障诊断模型 [J]. 西安交通大学学报, 2006,40(5):558-562.

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【本刊相关文献链接】

具有模糊隶属度的模糊支持向量机算法.西安交通大学学报,2009,43(7): 40-43.

自适应移频变尺度随机共振在故障诊断中的应用.西安交通大学学报,2009,43(7):69-73.

免疫编程的故障特征构造方法.西安交通大学学报,2007,41(9):1036-1039.

基于支持向量机的发动机故障诊断.西安交通大学学报,2007, 41(9):1124-1126.

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