【摘要】 针对具有非平稳、非线性特性的滚动轴承振动信号采用传统时频分析方法难以捕捉其振动信号动态变化规律、无法准确描述故障特征的问题，提出基于EMD-WVD时频图和NRBO-SVM的滚动轴承故障诊断方法。首先，将滚动轴承振动信号通过经验模态分解（EMD）方法进行分解，得到多个不同频率的固有模态函数（IMF）分量以适应信号的非平稳性。然后，对于前几个高频IMF分量分别通过Wigner-Ville分布（WVD）进行时频分析，得到多尺度时频图像，以高分辨率揭示信号的非线性特征。接着，通过局部二进制（LBP）特征提取，增强了对非线性特征的识别能力。最后，带入经牛顿-拉夫逊优化算法（NRBO）优化的支持向量机（SVM）进行故障特征分类。结果表明，该方法将滚动轴承故障诊断的准确率从83.33%提升至96.66%，分类处理时间从78 s减少至53 s。更多还原

【Abstract】 To address the problem that the traditional time-frequency analysis method fails to capture the vibration signal dynamic changes in vibration signals of the rolling bearing with non-smooth, non-linear characteristics and can not accurately describe the fault characteristics, a rolling bearing fault diagnosis method based on EMD-WVD time-frequency images and NRBO-SVM is proposed. Firstly, the rolling bearing vibration signal is decomposed by employing the Empirical Mode Decomposition(EMD)method to obtain multiple Intrinsic Mode Functions(IMF) components with different frequencies to adapt to the non-stationarity of the signal. Then, the first few high-frequency IMF components are analyzed by Wigner-Ville distribution(WVD), to obtain multiscale time-frequency images,which reveals the nonlinear characteristics of the signals with high resolution. Afterwards, the recognition of nonlinear features is enhanced by Local Binary Pattern(LBP) feature extraction. Finally, the Support Vector Machine(SVM) optimized by the Newton-Raphson-based optimizer(NRBO) is brought in for fault feature classification. The results show that the method improves the accuracy of fault diagnosis from 83.33% to 96.66%, and reduces the classification processing time from 78 s to 53 s.更多还原