| 基于递归定量分析的液压泵故障识别方法 |
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| 引用本文： | 姜万录,李振宝,张生,雷亚飞,王浩楠.基于递归定量分析的液压泵故障识别方法[J].液压与气动,2019,0(2):18-23. |
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| 作者姓名： | 姜万录 李振宝 张生 雷亚飞 王浩楠 |
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| 作者单位： | 1. 燕山大学河北省重型机械流体动力传输与控制重点实验室， 河北秦皇岛066004；
2. 燕山大学先进锻压成形技术与科学教育部重点实验室， 河北秦皇岛066004 |
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| 基金项目： | 国家自然科学基金(51875498，51475405)；河北省自然科学基金重点项目(E2018203339) |
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| 摘 要： | 提出了一种局部投影消噪和递归定量分析相结合的轴向柱塞泵故障识别方法。以轴向柱塞泵故障振动信号为研究对象，首先用局部投影消噪方法对振动信号进行消噪；其次对消噪后的振动信号绘制递归图，进而通过递归定量分析对递归图所反映出的系统动力学信息进行特征提取，选择确定率（DET）和递归熵(ENTR)2个特征构成特征向量，构成故障特征样本；然后通过核模糊C均值聚类(KFCMC)方法对训练样本进行聚类,进而依据最小欧氏距离准则对测试样本进行故障识别；最后，将递归定量分析方法和相空间复杂网络定量特征方法进行对比。结果表明，基于递归定量分析的轴向柱塞泵故障识别方法具有更高的故障确诊率。
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| 关 键 词： | 复杂网络 递归定量分析 核模糊C均值聚类 故障诊断 轴向柱塞泵 |
| 收稿时间： | 2018-08-31 |
Fault Recognition Method Based on Recurrence Quantitation Analysis for Hydraulic Pump |
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| JIANG Wan-lu,LI Zhen-bao,ZHANG Sheng,LEI Ya-fei,WANG Hao-nan.Fault Recognition Method Based on Recurrence Quantitation Analysis for Hydraulic Pump[J].Chinese Hydraulics & Pneumatics,2019,0(2):18-23. |
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| Authors: | JIANG Wan-lu LI Zhen-bao ZHANG Sheng LEI Ya-fei WANG Hao-nan |
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| Affiliation: | 1. Hebei Provincial Key Laboratory of Heavy Machinery Fluid Power Transmission and Control,
Yanshan University, Qinhuangdao, Hebei066004;
2. Key Laboratory of Advanced Forging & Stamping Technology and Science (Yanshan University),
Ministry of Education of China, Qinhuangdao, Hebei066004 |
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| Abstract: | A fault recognition method for axial piston pump with local projective noise reduction method and recurrence quantitation analysis integrated is presented. Fault vibration signals of axial piston pump are taken as the analysis objects. Firstly, the vibration signal is denoised by local projective noise reduction method. Secondly, the recurrence plots of vibration signals after noise cancellation are drawn. The system dynamic behavior reflected in the recurrence plots is extracted by recurrence quantification analysis. The determinism (DET) and the recurrence entropy(ENTR) are selected to form the eigenvectors. The fault feature samples are constructed. Then, the fault training samples are clustered by the kernel fuzzy C-means clustering (KFCMC) method. The minimum Euclidean distance principle is used to identify the fault test samples. Finally, through comparing the recurrence quantitation analysis method with the quantitative feature method of phase space complex network, we find that the recurrence quantitation analysis-based fault recognition method of hydraulic pump has a higher fault diagnosis correct rate. |
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