液压泵性能退化过程中，振动信号非线性强，导致退化特征提取困难、表征能力有限，为此，本文提出一种基于改良型局部特征尺度分解（Improved Local Characteristic-scale Decomposition, ILCD）融合与多重分形去趋势波动分析（Multi-fractal Detrended Fluctuation Analysis, MF-DFA）的退化特征提取方法。首先，在对信号进行ILCD分解的基础上，通过构建敏感因子从各内禀尺度分量（Intrinsic Scale Components，ISCs）筛选出包含关键故障信息的敏感分量，并依据融合规则实现多通道振动信号的融合处理，以改善重构信号中的特征信息；在此基础上，利用带有加窗分割的MF-DFA方法对融合信号作进一步处理，选取多重分形谱敏感参数作为液压泵性能退化特征向量；最后，利用液压泵实测振动信号，验证了该方法的有效性。
Abstract
In the process of hydraulic pump degradation, the vibration signal is nonlinear,from which it is hard to extract effective degradation features. Therefore, a novel method based on the Improved Local Characteristic-scale Decomposition (ILCD) fusion and Multi-fractal Detrended Fluctuation Analysis (MF-DFA) was proposed. Vibration signals were initially decomposed by ILCD. Intrinsic Scale Components were selected based on the presented sensitive factor to obtain important feature informations effectively. Multi-dimensional vibration signals were fused according to the fusion rules for improving the feature information in the reconstructed signal. Furthermore, the fused signal was processed by MF-DFA which contains the window division, and the multi-fractal spectral parameters were extracted as degradation features. Finally, the proposed method was verified by the real sampled vibration signals of a hydraulic pump.
关键词
改良型局部特征尺度分解算法 /
多通道信号融合 /
敏感因子 /
退化特征提取
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Key words
Improved local characteristic-scale decomposition algorithm /
multi-channel signals fusion /
sensitive factor /
degradation feature extraction
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