滑动轴承摩擦故障趋势预测的系统自记忆模型

张峻宁1,张培林1,华春蓉2,吴定海1

振动与冲击 ›› 2017, Vol. 36 ›› Issue (11) : 20-26.

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振动与冲击 ›› 2017, Vol. 36 ›› Issue (11) : 20-26.
论文

滑动轴承摩擦故障趋势预测的系统自记忆模型

  • 张峻宁1,张培林1,华春蓉2,吴定海1
作者信息 +

Power system self-memory model for predicting the wear  trend of sliding bearing

  • ZHANG Jun-ning1, ZHANG Pei-lin1, CHUA Chun-rong2,Wu Ding-hai1
Author information +
文章历史 +

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,本文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

The sliding bearing’s wear trend is hard to predict because its time series is nonlinear.To solve this problem,a wear trend prediction method for sliding bearing based on System self memory prediction model is put forward in this text.First,according to the different signal excitation source,the acquisition signal is separated into impact sound and random noise.Then the function fitting, derivation and grey theory are respectively inversed to get system differential equations of Impact sound and random sound.And Bidirectional Difference is used to obtain the influence coefficient of differential equation on the dynamic core of the bearing wear signal system.By introducing self memory function,the dynamic core of the bearing wear signal system is inversed into a Differential-Difference Equation.Then a self memory prediction model of sliding bearing is obtained.By applicating the proposed method in the case of sliding bearing wear degradation of static and dynamic loads,the validity of the method is verified.And it provides a new way to predict the degradation trend of sliding bearing.

关键词

滑动轴承 / 摩擦故障 / 发展趋势 / 非线性动力系统 / 信号分离 / 自记忆模型

Key words

plain bearing / friction fault / development trend / nonlinear dynamical systems / signal separation / self-memorization model

引用本文

导出引用
张峻宁1,张培林1,华春蓉2,吴定海1. 滑动轴承摩擦故障趋势预测的系统自记忆模型[J]. 振动与冲击, 2017, 36(11): 20-26
ZHANG Jun-ning1, ZHANG Pei-lin1, CHUA Chun-rong2,Wu Ding-hai1. Power system self-memory model for predicting the wear  trend of sliding bearing[J]. Journal of Vibration and Shock, 2017, 36(11): 20-26

参考文献

[1] 陈瑞华, 杨宗伟. 基于时序AR与灰色GM模型的滚动轴承故障诊断研究[J]. 机械传动 2009, 33(6): 89-90.
   CHEN Rui-hua, YANG Zong-wei. Roller Bearing   Fault Diagnosis Based on Grey AR Combination Model[J].Journal of Mechanical Transmission, 2009, 33(6): 89-90.
[2] 肖 婷, 汤宝平, 秦 毅, 陈 昌等. 基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测[J]. 振 动 与 冲 击, 2015, 34(9): 149-151.
   XIAO Ting,TANG Bao-ping,QIN Yi,CHEN Chang. Degradation trend prediction of rolling bearing based on manifold learning and least squares support vector machine[J]. Proceedings of the CSEE, 2015, 34(9): 149-151.
[3] 张志明,稈惠涛,徐鸿等.神经网络组合预报投型及其在汽轮发电机组状态检修中的应用[J].中国电机工程学报,2003, 23 (9) : 204-205.
   ZHANG Zhi-ming, CHEN Hui-tao, XU Hong, et al. Neural network based combining prediction model and its application in condition based maintenance of turbo-generator set [J]. Proceedings of the CSEE, 2003,23 (9): 204-205.
[4] 李永祥,杨建国,郭前建,等.数控机床热误差的混合预测模型及应用[J].上海交通大学学报,2006,40(12) : 2030-2033.
   LI Yong-xiang, YANG Jian-guo, GUO Qian-jian, et al. The application of hybrid prediction model to thermal error modeling on NC machine tools[J].Journal of Shanghai Jiaotong University, 2006,40(12) : 2030-2033.
[5] WANG W,SU J Y,HOU B W,et al. Dynamic prediction of building subsidence deformation with data-based
mechanistic self-memory model[J]. Chin Sci Bull,2012,57( 26) : 3430-3435.
[6] CHEN X D,XIA J,XU Q. Differential hydrological grey model ( DHGM ) with self-memory function and its
application to flood forecasting[J]. Sci China Tech Sci,2009,39( 2) : 341-350.
[7] FENG G L,CAO H X,GAO X Q,et al. Prediction of precipitation during summer monsoon with self-memorial Model[J]. Adv Atmos Sci, 2001, 18: 701-709.
[8] Qin P, Yan B, Tan D M. Study on fault diagnosis of sliding bearings using ae signals[J].JOURNAL OF SOUTHWEST JIAO TONG UNIVERSITY, 2001, 36(3): 272-273(in Chinese) [秦萍, 阎兵, 谭达明. 利用声发射诊断滑动轴承接触摩擦故障的研究[J]. 西南交通大学学报, 2001, 36(3): 272-273].
[9] 王跃飞, 张振涛, 张波等. 利用声发射的往复空压缩机环状阀泄露故障诊断试验[J]. 西安交通大学学报, 2012, 46(9): 26-28.
   WANG Yue-fei, ZHANG Zhen-tao, ZHANG Bo, et al.  Experiment on Fault Diagnosis of Air Compressor Ring Valve Leakage Using Acoustic Emission[J]. JOURNAL OF XI'AN JIAO TONG UNIVERSITY,2012, 46(9): 26-28.
[10] 方杰. 基于采样技术的非正弦周期信号均方根值测量方法[J]. 机电技术, 2013, 5(1): 141-143.
   FANG Jie. Study on Fault Diagnosis of Sliding Bearings Using AE Signals[J].JOURNAL OF SOUTHWEST JIAO TONG UNIVERSITY, 2013, 5(1): 141-143.
[11] Young P. Data-based mechanistic modeling,generalized sensitivity and dominant mode analysis[J]. Computer Physics Communica-tion, 1999, 117:125-129.
[12] 李新春.基于声发射监测的滑动轴承状态诊断技术研究
     [D]. 长沙理工大学, 2011(5): 33-34.
[13] Aharon M, Elad M, Bruckstein A, et al. K-SVD an algorithm for designing of overcomplete dictionaries for sparse representa-tion[J]. IEEE Trans. on Signal Processing,2006,54(11): 4315-4320.
[14] 刘湘平, 谢学斌, 黄东等. 基于动力系统自忆性原理的地下工程围岩变形预测方法[J]. 煤炭学报, 2010, 35( 5) :   739-744.
   LIU Xiang-ping, XIE Xue-bin, HUANG Dong, et al. Displacement prediction method of underground excavation based on self-memorization model of dynamic system[J]. Chinese Journal of China Coal Society, 2010,   35( 5) : 739-744.

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