摘要
为解决传统滚动轴承故障诊断及状态监测中依赖单一故障特征频率,以及诊断过程中人为主观因素造成诊断结果的不定性与效率低等问题,以包络谱与共振解调技术为例,提出一种自动识别滚动轴承故障特征频率及其倍频与调制频率的方法。该方法对信号进行包络谱或共振解调分析,在此基础上通过迭代算法依次找出转频与故障频率成分,并依据各成分在识别结果中的比例来进行故障诊断。人为仿真故障及滚动轴承加速寿命实验证明了文章方法的有效性。
In order to solve problems, such as, overdependence on single fault characteristic frequency, and the ambiguity and inefficiency of diagnosis results caused by subjective factors in fault diagnosis of rolling bearings, a method using the envelope spectrum and resonance demodulation analysis of signals was proposed to identify the fault characteristic frequency, and its multiplications and modulation frequency components of rolling bearings. Firstly, the original signal was analyzed with the envelope spectrum or resonance demodulation analysis. Then, a specific algorithm was used to identify faulty frequency components and rotating frequency in the spectrum. At last, the fault diagnosis of rolling bearings was conducted with the corresponding proportions of the identified frequency components. The fault diagnosis simulations and the life acceleration tests of rolling bearings demonstrated the validity of the proposed method. © 2017, Editorial Office of Journal of Vibration and Shock. All right reserved.
出处
《振动与冲击》
EI
CSCD
北大核心
2017年第9期58-62,121,共6页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(51275383)
关键词
故障诊断
频率识别
包络谱
智能算法
Bearings (machine parts)
Demodulation
Failure analysis
Identification (control systems)
Optical variables measurement
Roller bearings
Spectrum analysis
