针对滚动轴承的复合故障诊断问题，深入研究了一种基于优化特征模态分解和快速谱相关的复合故障诊断方法。首先，通过理论分析，提出脉冲能量因子指标来实现特征模态分解的参数选择以及最优分量的选取；然后，基于快速谱相关原理设计谱相关相对强度曲线和改进快速谱相关图，用于确定不同故障调制后对应的最优载波，对最优载波进行包络处理，从而分离轴承的复合故障特征，最终实现复合故障的准确性诊断。通过模拟故障实验和工程案例分析结果表明：本文所提方法相比于经验模态分解能够有效滤除噪声干扰，具有良好的鲁棒性，同时，避免了解卷积方法设定参数的缺陷，且与Autogram方法相比，能够有效分离复合故障特征，避免复合故障特征成分耦合。
Abstract
Aiming at the problem of compound fault diagnosis of rolling bearing, a compound fault diagnosis method based on optimized feature mode decomposition and fast spectral correlation is studied in detail. Firstly, through theoretical analysis, the index of pulse energy factor is proposed to realize the parameter selection of feature mode decomposition and the selection of the optimal component. Then, based on the principle of fast spectral correlation, the spectral correlation relative strength curve and the improved fast spectral correlation graph are designed to determine the optimal carrier corresponding to different fault modulation, and the envelope processing of the optimal carrier is carried out, so as to separate the composite fault characteristics of the bearing and finally realize the accurate diagnosis of the composite fault. The results of simulated fault experiments and engineering case analysis show that compared with empirical mode decomposition, the proposed method can effectively filter noise interference and has good robustness. At the same time, it can avoid the defects of understanding parameters set by convolution method, and can effectively separate composite fault features and avoid the coupling of composite fault feature components compared with Autogram method.
关键词
滚动轴承 /
复合故障 /
特征分离 /
特征模态分解 /
快速谱相关
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Key words
rolling bearing /
compound fault /
feature separation /
feature mode decomposition /
fast spectral correlation
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参考文献
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