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.
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.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2009年第9期37-41,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金资助项目(50875197)
教育部留学回国人员科研启动基金资助项目
关键词
粒计算
故障诊断
相容粒度空间模型
约简属性
granular computing
fault diagnosis
tolerance granularity space mode
attribute reduction
