基于VPRS和神经网络的旋转机械故障诊断  

Rotating Machinery Fault Diagnosis Based on VPRS and Neural Network

作　　者：倪啸[1] 蔡瑞英[1] 

机构地区：[1]南京工业大学信息科学与工程学院,江苏南京210009

出　　处：《微计算机信息》2008年第34期160-161,166,共3页Control & Automation

摘　　要：在故障诊断领域中,由于大量相关且冗余的数据存在,我们往往很难找出故障原因与故障征兆之间的对应关系,所以只能通过对诊断信息的合理约简,来减少冗余数据对系统结果的影响。本文提出了一种基于变精度粗糙集模型和神经网络相结合的方法应用于旋转机械故障诊断,目的是为了更好的解决故障特征提取和诊断规则获取问题。In fault diagnosis, there are not one to one definite relations between the fault reasons and the fault symptoms with relatively high information redundancy. We always try to decrease redundance of diagnostic information by reduction. In this paper, we propose a method based on VPRS model and neural network is applied to fault diagnosis of rotating machinery, where the aim is to solve fault features extraction and diagnostic rules acquisition.

关 键 词：故障诊断 变精度粗糙集 离散化 约简 人工神经网络 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]