钱恩丽,黄国勇,李锶宇,何 冬.基于 CMFDE 的单向阀早期微弱故障诊断方法[J].电子测量与仪器学报,2021,35(4):169-176 |
基于 CMFDE 的单向阀早期微弱故障诊断方法 |
Method of CMFDE in check valve early weak fault diagnosis |
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DOI: |
中文关键词: 单向阀 早期微弱故障 复合多尺度波动散布熵 波动散布熵 故障诊断 |
英文关键词:check valve early weak fault composite multiscale fluctuation dispersion entropy fluctuation dispersion entropy fault diagnosis |
基金项目:国家自然科学基金(61663017)项目资助 |
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中文摘要: |
对单向阀早期微弱故障进行诊断可预防高压隔膜泵因单向阀磨损击穿无法正常工作而造成经济损失及安全事故。 针
对往复式高压隔膜泵单向阀早期微弱故障特征不明显且受大量噪声干扰的问题,提出基于复合多尺度波动散布熵( composite
multiscale fluctuation dispersion entropy, CMFDE)的单向阀早期微弱故障诊断方法。 首先,用正切 S 型替换 CMFDE 方法中的正
态分布函数映射,提高 CMFDE 的抗噪性;其次,计算振动信号的复合多尺度波动散布熵值,构建特征矩阵,并将其输入支持向
量机(support vector machine, SVM)分类器中进行故障诊断;最后,利用单向阀实际工程数据验证该方法的有效性,并进行对比
实验。 实验结果与对比分析表明,不需对单向阀原始信号进行降噪,简化了诊断过程。 复合多尺度波动散布熵可精确反映单 |
英文摘要: |
The early weak fault diagnosis of check valve can prevent economic loss and safety accident caused by the failure of the high
pressure diaphragm pump due to the wear and breakdown of check valve. Aiming at the problem that early weak fault feature of the check
valve of the reciprocating high pressure diaphragm pump are not obvious and are disturbed by a lot of noise, Method: a method for early
weak fault diagnosis of check valve based on composite multiscale fluctuation dispersion entropy (CMFDE) is proposed. First of all,
replace the normal cumulative distribution function mapping in the CMFDE method with tangent sigmoid to improve the noise resistance
of CMFDE. Then, calculate the composite multiscale fluctuation dispersion entropy of the vibration signal, and construct the feature
matrix and input it into the support vector machine (SVM) classifier for fault diagnosis. Finally, the actual engineering data of the check
valve is used to verify the effectiveness of the method, and a comparative experiment is carried out. The experimental results and
comparative analysis show that it is not necessary to reduce the noise of the original signal of check valve, which simplifies the diagnosis
process. The composite multiscale fluctuation dispersion entropy feature can accurately reflect the different signal characteristics of check
valve, which improves the identification rate of the early weak fault diagnosis of check valve, and the fault diagnosis result is less
affected by classifiers, with the identification accuracy reaching 96. 667%. |
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