基于改进MF-DFA的液压泵退化特征提取方法
Degradation Feature Extraction of Hydraulic Pump Based on Improved MF-DFA

针对液压泵振动信号通常具有非线性强和信噪比低的特点，提出了一种基于改进多重分形去趋势波动分析（multi fractal detrended fluctuation analysis，简称MF-DFA）的液压泵性能退化特征提取方法。首先，引入滑动窗口技术改进传统MF-DFA方法在时间序列数据分割过程中存在的不足，提高了MF-DFA方法的计算精度；然后，利用改进的MF-DFA方法计算液压泵多重分形谱参数，分析了不同分形谱参数对液压泵退化状态的反映能力，选取奇异指数α-0和多重分形谱宽度α作为退化特征量；最后，以液压泵不同退化状态下的实测数据为例验证了该算法的有效性。试验结果表明，该方法能够准确提取液压泵退化特征，提高了退化状态识别的准确率。
To solve the problem that the vibration signals of a hydraulic pump usually appear with nonlinear and a low signal to noise ratio, we devised a degradation feature extraction method based on improved multi-fractal detrended fluctuation analysis (MF-DFA). First, we proposed an improved MF-DFA based on slip window technology to overcome the problem of data partitioning, then used the MF-DFA to calculate four kinds of multi-fractal parameters of a hydraulic pump. We analyzed four parameters and consequently selected the singular index a0 and the width of multi-fractal spectrum △a as the degradation feature. Finally, the results from analyzing actual data showed that the proposed method effectively recognized the degradation status of the hydraulic pump.