滑动轴承摩擦故障趋势预测的系统自记忆模型

发布时间：2018-06-07 23:26

本文选题：滑动轴承 + 摩擦故障　；参考：《振动与冲击》2017年11期

【摘要】：针对滑动轴承时间序列非线性引起的接触摩擦故障趋势难预测的问题,提出一种基于系统自记忆预测模型的滑动轴承接触摩擦故障趋势预测方法。该方法首先根据信号激励源不同的特点,将采集信号分离为冲击声和随机声,然后采用函数拟合、求导和灰色理论分别反演出冲击声和随机声的系统微分方程,并运用双向差分求取不同微分方程对轴承接触摩擦故障信号系统动力核的影响系数。通过引入自记忆函数,将滑动轴承摩擦故障系统动力核反演成一个微分-差分方程,由此得到滑动轴承的自记忆预测模型。应用到静载荷和动载荷的滑动轴承接触摩擦故障实例中,验证了所提方法的有效性,为滑动轴承磨损退化趋势预测提供了一种新的途径。
[Abstract]:Aiming at the difficulty of predicting the trend of contact friction fault caused by nonlinear time series of sliding bearing, a method of predicting contact friction fault trend of sliding bearing based on system self-memory prediction model is proposed. According to the different characteristics of signal excitation sources, the collected signal is divided into shock sound and random sound, and then the differential equations of shock sound and random sound are derived by function fitting and grey theory respectively. The influence coefficient of different differential equations on the dynamic core of bearing contact friction fault signal system is obtained by using bidirectional difference. By introducing self-memory function, the dynamic kernel of sliding bearing friction fault system is inversed into a differential-difference equation, and the self-memory prediction model of sliding bearing is obtained. The proposed method is applied to sliding bearing contact friction faults under static load and dynamic load, and a new way to predict wear degradation trend of sliding bearing is provided.
【作者单位】：军械工程学院七系;西南交通大学机械工程学院;
【基金】：国家自然科学基金(51205405;51305454)
【分类号】：TH133.31

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