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Rolling bearing fault diagnosis based on LCD-TEO and multifractal detrended fluctuation analysis

机译：基于LCD-TEO和多重分形趋势波动分析的滚动轴承故障诊断

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摘要

A rolling bearing vibration signal is nonlinear and non-stationary and has multiple components and multifractal properties. A rolling-bearing fault-diagnosis method based on Local Characteristic-scale Decomposition-Teager Energy Operator (LCD-TEO) and multifractal detrended fluctuation analysis (MF-DFA) is first proposed in this paper. First the vibration signal was decomposed into several intrinsic scale components (ISCs) by using LCD, which is a newly developed signal decomposition method. Second, the instantaneous amplitude was obtained by applying the TEO to each major ISC for demodulation. Third, the intrinsic multifractality features hidden in each major ISC were extracted by using MF-DFA, among which the generalized Hurst exponents are selected as the multifractal feature in this paper. Finally, the feature vectors were obtained by applying principal components analysis (PCA) to the extracted multifractality features, thus reducing the dimension of the multifractal features and obtaining the fault feature insensitive to variation in working conditions, further enhancing the accuracy of diagnosis. According to the extracted feature vector, rolling bearing faults can be diagnosed under variable working conditions. The experimental results demonstrate its desirable diagnostic performance under both different working conditions and different fault severities. Simultaneously, the results of comparison show that the performance of the proposed diagnostic method outperforms that of Hilbert-Huang transform (HHT) combined with MF-DFA or LCD-TEO combined with mono-fractal analysis.
机译：滚动轴承振动信号是非线性且不稳定的，具有多个分量和多重分形特性。本文首先提出了一种基于局部特征尺度分解-Teager能量算子（LCD-TEO）和多重分形趋势波动分析（MF-DFA）的滚动轴承故障诊断方法。首先，通过使用LCD（一种新开发的信号分解方法），将振动信号分解为几个固有比例分量（ISC）。其次，通过将TEO应用于每个主要ISC进行解调，可获得瞬时幅度。第三，利用MF-DFA提取了各主要ISC中隐藏的固有多重分形特征，并选择了广义Hurst指数作为多重分形特征。最后，通过对提取的多重形特征进行主成分分析（PCA），得到特征向量，从而减小了多重形特征的维数，获得了对工况变化不敏感的断层特征，进一步提高了诊断的准确性。根据提取的特征向量，可以在可变工作条件下诊断滚动轴承故障。实验结果证明了其在不同的工作条件和不同的故障严重程度下的理想诊断性能。同时，比较结果表明，所提出的诊断方法的性能优于结合MF-DFA或LCD-TEO结合单分形分析的Hilbert-Huang变换（HHT）。

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