A failure diagnosis method of ball bearing based on optimized multiscale variational modal extraction with synchro squeezing transformation

Yongpeng Li, Mingyue Yu, Peng Wu, Baodong Qiao
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Abstract

When a compound failure occurs in a bearing, the failure information included in vibration signals is featured by being weak, complex and combined. This fact makes it difficult to identify a compound failure precisely. Variational mode extraction (VME) solves the problem of variational mode decomposition (VMD) in being difficult to determine the number of decomposition layers and has certain applications in the identification of bearing failures. However, the initial expected central frequency of VME and option of penalty factor is crucial to the extraction of expected mode. To precisely determine the central frequency and penalty factor of VME and fulfill the correct extraction of feature information of compound failures in bearing, the paper has proposed a method based on synchro squeezing transform (SST) and information entropy to adaptively determine the parameters in VME (SST-VME). Firstly, SST is used to make time-frequency analysis of original vibration signals and characteristic frequency bands with larger energy are chosen from time-frequency spectrum to adaptively determine the expected central frequency of VME. Secondly, as information entropy has representation capacity for information included in the signal, penalty factor of VME was adaptively determined according to information entropy. Thirdly, original signals were subjected to VME according to expected central frequency and penalty factor adaptively determined; the expectation mode obtained was denoised by singular value decomposition algorithm. Finally, the type of compound failures of bearing is determined according to the frequency spectrum of denoised signals. To verify the effectiveness of proposed method, a comparison with VMD algorithm is conducted. As indicated by the result, the proposed method is more precise and comprehensive than VMD algorithm to extract the feature information corresponding to compound faults of bearing, and thereby correctly determines the type of compound failures.
基于同步挤压变换的优化多尺度变分模态提取的球轴承故障诊断方法
当轴承发生复合故障时,振动信号中包含的故障信息具有微弱、复杂和综合的特点。因此很难准确识别复合故障。变异模态提取(VME)解决了变异模态分解(VMD)难以确定分解层数的问题,在轴承故障识别中具有一定的应用价值。然而,VME 的初始预期中心频率和惩罚因子的选择对于预期模式的提取至关重要。为了精确确定 VME 的中心频率和惩罚因子,正确提取轴承复合故障的特征信息,本文提出了一种基于同步挤压变换(SST)和信息熵自适应确定 VME 参数的方法(SST-VME)。首先,利用同步挤压变换(SST)对原始振动信号进行时频分析,并从时频谱中选取能量较大的特征频段,自适应地确定 VME 的预期中心频率。其次,由于信息熵对信号中包含的信息具有表示能力,因此根据信息熵自适应地确定 VME 的惩罚因子。第三,根据预期中心频率和自适应确定的惩罚因子对原始信号进行 VME 处理,并利用奇异值分解算法对得到的预期模式进行去噪。最后,根据去噪信号的频谱确定轴承复合故障的类型。为了验证所提方法的有效性,与 VMD 算法进行了比较。结果表明,与 VMD 算法相比,所提出的方法能更精确、更全面地提取轴承复合故障对应的特征信息,从而正确判断复合故障的类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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