基于SIE和SVR的液压泵故障定量诊断

为更好地实现液压泵故障定量诊断,对故障定量诊断中的退化特征提取和故障程度诊断方法进行研究。针对排列熵算法的不足,提出空间信息熵(spatial information entropy,简称SIE)的概念,分析了空间信息熵3个参数(时间序列的分区数s、相空间重构的嵌入维数m和延迟时间τ)变化对其性能带来的影响,为其选取提供了依据。仿真分析结果也验证了其作为液压泵退化特征的有效性和优越性。基于空间信息熵算法提取液压泵故障退化特征集,针对退化特征与故障程度之间存在的非线性关系,提出采用果蝇优化算法优化参数的支持向量回归机实现液压泵的故障定量诊断。对实测液压泵振动信号分析结果表明,空间信息熵在表征液压泵故障程度方面具有更好的性能。将果蝇算法优化参数的支持向量回归机用于液压泵的故障定量诊断得到了理想的定量诊断效果,并通过对比分析验证了提出的支持向量回归机模型的有效性和优越性。

Quantitative Diagnosis of Hydraulic Pump Fault Based on Spatial Information Entropy and Support Vector Regression

In order to more accurately realize the purpose of quantitative diagnosis of hydraulic pump faults, we investigated the methods of degradation feature extraction and fault degree diagnosis. We proposed spatial information entropy based on the study of permutation entropy. Then, we analyzed the influence of values variation of partition numbers, embedding dimension and delay time, which were used in the calculation of spatial information entropy, and the conclusion was meaningful for the selection of them. The results obtained by adopting the spatial information entropy algorithm to the simulation signal affirmed the availability and superiority of using spatial information entropy as the degradation feature of pump faults. We extracted the degradation feature set with the spatial information entropy algorithm, considering the nonlinear relationship between the degradation feature and fault degree. We optimized the parameters of the support vector regression machine with a fruit fly optimization algorithm, then used it to realize the quantitative diagnosis of hydraulic pump faults. The analysis results of the practical vibration signal verified that spatial information entropy performed better than did permutation entropy on the degradation state reflection. The results of the diagnosis model demonstrated the improved adaptability of spatial information entropy and the favorable performance of the proposed diagnosis model. The results also demonstrated the better optimizing ability of the fruit fly optimization algorithm compared with traditional algorithms.