摘要
针对滚动轴承故障的特征信号处于较低频带内、容易被噪声淹没、难以检测的问题,提出了基于混沌和符号序列统计的滚动轴承故障诊断方法.该方法利用混沌振子对微弱周期信号的敏感性,通过检测处于低频带内的故障特征周期信号来诊断轴承故障,同时采用符号序列统计量来识别振子状态的变化,达到了客观、自动识别振子状态和确定故障的目的.通过诊断滚动轴承的滚动体故障和内圈故障,验证了该方法的有效性.
The fault characteristic signals of rolling bearings are in low frequency band, and the useful signals are often buried in noise and difficult to be detected. In this research a method based on chaotic oscillator and symbol sequence statistics was presented to diagnose rolling bearings fault, using the character of chaotic oscillator being sensitive to weak periodic signal. The symbol sequence statistics was used to recognize the states of chaotic oscillator accurately and automatically, and then the fault could be identified. Diagnosis of rolling ball fault and inner race fault of rolling bearings confirm the effectiveness of this method.
出处
《西安交通大学学报》
EI
CAS
CSCD
北大核心
2005年第3期261-265,共5页
Journal of Xi'an Jiaotong University
基金
国家自然科学基金重点资助项目(50335030).
关键词
混沌振子
符号序列统计
滚动轴承
故障诊断
Chaos theory
Failure analysis
Interference suppression
Pattern recognition
Signal interference
Signal processing
