摘要
针对滚动轴承故障的特征频率处于较低频带,容易被噪声淹没而难于直接检测的问题,提出了在衡量二维时间序列复杂性方面具有普遍意义的二维近似熵,及基于混沌和二维近似熵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,可以直接检测低频段内滚动轴承微弱的故障特征频率。同时,以二维近似熵作为测度,能够从二维角度全面地量化振子的相变规律,从而客观、准确地识别振子状态并确定故障类型。对滚动轴承内、外圈故障的诊断实例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 heavy noise and difficult to be detected. Two-dimensional approximate entropy （AE2d） was proposed to measure the stares of chaotic oscillator, and a method based on chaotic oscillator and AE2d was presented to diagnose rolling bearings fault. By using the characters of chaotic oscillator being sensitive to weak periodic signal, and AE2d being effective to recognizing the change of chaotic oscillator, the fault could be identified. Diagnosis of inner and outer race fault of rolling bearing confirm the effectiveness of this method.
出处
《振动工程学报》
EI
CSCD
北大核心
2007年第3期268-273,共6页
Journal of Vibration Engineering
基金
国家自然科学基金资助项目(50475117)
天津市应用基础研究计划重点项目(05YFJZJC01800)
关键词
滚动轴承
故障诊断
混沌振子
近似熵
特征提取
rolling bearing
fault diagnosis
chaotic oscillator
approximate entropy
feature extraction
