POVMD与包络阶次谱的变工况滚动轴承故障诊断

发布时间：2019-01-22 10:14

【摘要】：针对变转速滚动轴承故障特征提取较难的问题,提出一种基于参数优化变分模态分解(parameter optimized variational mode decomposition,简称POVMD)与包络阶次谱的变工况滚动轴承故障诊断方法。首先,采用POVMD对变转速滚动轴承振动信号进行分解,得到若干个本征模态函数之和;其次,对各个分量的时域信号进行角域重采样,将时变信号转化为平稳信号处理,再利用Hilbert变换估计重采样后的平稳信号的包络;最后,对得到的包络信号进行阶比分析,从谱图中读取故障特征信息。将POVMD方法与经验模态分解进行了对比,仿真信号分析结果表明了POVMD方法的优越性。将提出的变转速滚动轴承故障诊断方法应用于试验数据分析,分析结果表明,所提出的方法能够实现变转速滚动轴承的故障诊断,而且诊断效果优于现有方法。
[Abstract]:In order to solve the problem of difficult feature extraction of variable-speed rolling bearing, a fault diagnosis method for variable-speed rolling bearing based on parameter-optimized variable-mode decomposition (POVMD) and envelope-order sub-spectrum is proposed. firstly, decomposing a vibration signal of a variable-speed rolling bearing by adopting a POVMD to obtain a sum of a plurality of intrinsic mode functions; secondly, carrying out angular domain re-sampling on a time domain signal of each component, converting the time-varying signal into a stable signal processing, then the envelope of the re-sampled stationary signal is estimated by using a Hilbert transform; and finally, the obtained envelope signal is subjected to the order ratio analysis, and the fault characteristic information is read from the spectrogram. In this paper, the method of POVMD is compared with the empirical mode decomposition, and the results of the simulation signal show the superiority of the POVMD method. The fault diagnosis method of the variable speed rolling bearing is applied to the test data analysis, and the result shows that the proposed method can realize the fault diagnosis of the variable speed rolling bearing, and the diagnosis effect is superior to the existing method.
【作者单位】：安徽工业大学机械工程学院;
【基金】：国家自然科学基金资助项目(51505002) 安徽省高校自然科学研究重点资助项目(KJ2015A080) 研究生创新研究基金资助项目(2016062)
【分类号】：TH133.33

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