粗集理论及其在旋转机械故障诊断中的应用 (二)旋转机械故障诊断知识库的形成  

Rough sets and fault diagnosis of rotating machine based on rough set theory(Ⅱ) Formation knowledge base about rotating machinery fault diagnosis

作　　者：许琦[1] 顾海明[1] 李永生[1] 

机构地区：[1]南京工业大学机械与动力工程学院

出　　处：《南京工业大学学报（自然科学版）》2002年第4期30-33,共4页Journal of Nanjing Tech University(Natural Science Edition)

基　　金：江苏省自然科学基金资助项目 (BK2 0 0 10 95 )

摘　　要：旋转机械故障诊断知识库形成的主要环节是信号的采集和信号的分析、整理 ;在所有的机械故障信号中 ,振动诊断技术是故障诊断的常用手段 ,对振动信号实现连续、在线诊断的最重要的数学工具是快速傅里叶变换(FFT)和小波理论。通过对实验的观察 ,建立转子故障特征向量和故障类型之间对应关系的数据库 。Signal acquisition and data processing are chiefly matter of format knowledge base of rotating machine fault diagnosis. Vibration analysis technology is a common method of fault diagnosis in the kind of fault machine signal. FFT and wavelet transform are important mathematical tool when vibration signal is analyzed in real time and continuously. At the paper ,a data base about rotating fault relationship between eigenvector fault type was set up, and original knowledge base was provide for fault diagnosis decision table based on rough sets.

关 键 词：粗集理论 旋转机械 知识库 小波理论 特征向量 故障诊断 

分 类 号：TH165.3[机械工程—机械制造及自动化] TH133