机器状态智能诊断与管理系统

Intellective diagnosis and manage system of bearing test machine

利用小波变换的时频特性和多分辨率特点 ,将待处理的信号通过调整小波尺度来抽取具有实质性的故障特征 ,确保了信息量的完整性。将模糊理论与人工神经网络相结合 ,应用于滚动轴承的故障诊断中 ,并采用面向对象的分析方法和编程技术实现了轴承实验机的智能诊断与管理。

A method using the time-frequency and multi-distinguishability characteristic of wavelet transform is porposed to extract fault characteristic processed signal by adjusting wavelet scale,which ensures informational completeness.In addition,a method is put forword which combines artificial neural network with the theory of fuzzy logic and applied to the fault diagnosis of rolling bearing.Then by means of object-oriented analytic methods as well as programming skills,we implement the intellective diagnosis and manage system of bearing test machine.