变载荷条件下的滚动轴承在线故障诊断方法

发布时间：2018-01-21 04:42

本文关键词： 滚动轴承故障诊断 Fisher判别式分析变载荷　出处：《轴承》2016年06期 　论文类型：期刊论文

【摘要】：针对变载荷条件下滚动轴承在线故障诊断问题,提出了一种基于动态FDA的在线故障诊断方法。首先,提取滚动轴承每个采样点振动加速度信号的瞬时能量,并利用当前采样时刻和前d个采样时刻的瞬时能量构建增广故障特征向量;然后,利用FDA对空载状态下无故障、滚动体故障、内圈故障和外圈故障的试验数据进行分析,获取最佳判别矩阵/向量;最后,采用0%,50%,100%和150%这4种载荷条件下的试验数据对该故障诊断方法的有效性进行验证。试验结果表明:所提出的故障诊断方法精度高、计算量小、实时性好,能有效地用于变载荷条件下滚动轴承的在线故障诊断。
[Abstract]:An on-line fault diagnosis method based on dynamic FDA is proposed for rolling bearing on-line fault diagnosis under variable load. Firstly, the instantaneous energy of vibration acceleration signal of each sampling point of rolling bearing is extracted. The extended fault eigenvector is constructed by using the instantaneous energy of the current sampling time and the previous d sampling time. Then, the test data of no-load fault, rolling body fault, inner ring fault and outer ring fault are analyzed by FDA, and the optimal discriminant matrix / vector is obtained. Finally, 0% 50%. The validity of the fault diagnosis method is verified by the test data under the conditions of 100% and 150%. The test results show that the proposed fault diagnosis method has high accuracy, small calculation and good real-time performance. It can be used in on-line fault diagnosis of rolling bearing under variable load condition.
【作者单位】：沈阳航空航天大学自动化学院;
【基金】：辽宁省教育厅科学研究项目(L2013070,L2014069) 航空科学基金项目(20153354005)
【分类号】：TH133.33
【正文快照】： 机械设备运转过程中载荷、转速的变化以及设备故障产生的冲击、摩擦等因素均会导致滚动轴承振动信号具有非平稳性[1]。经验模态分解(Empirical Mode Decomposition,EMD)[2-5]和小波分析[6-8]是近年来广受瞩目的非平稳信号处理技术。EMD虽然是一种自适应信号处理方法,但计算量

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