[1]许佳,胡建村,秦慈伟,等.基于参数优化VMD和散布熵的高压油泵故障诊断[J].内燃机学报,2023,(02):166-174.
　Xu Jia,Hu Jiancun,Qin Ciwei,et al.Fault Diagnosis of High-Pressure Fuel Pump Based on Parameter Optimization VMD and Dispersion Entropy[J].,2023,(02):166-174.
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基于参数优化VMD和散布熵的高压油泵故障诊断
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《内燃机学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2023年02
页码:
166-174
栏目:
出版日期:
2023-03-25

文章信息/Info

Title:
Fault Diagnosis of High-Pressure Fuel Pump Based on Parameter Optimization VMD and Dispersion Entropy
作者:
许佳; 胡建村; 秦慈伟; 陆宁云; 姜斌; 金江善
1. 南京航空航天大学 自动化学院，江苏 南京 211106；2. 中国船舶重工集团公司第七一一研究所，上海 201108；3. 船舶与海洋工程动力系统国家工程实验室，上海 201108
Author(s):
Xu Jia1; Hu Jiancun2;  3; Qin Ciwei2;  3; Lu Ningyun1; Jiang Bin1; Jin Jiangshan2;  3
1. College of Automation Engineering，Nanjing University of Aeronautics and Astronautics，Nanjing 211106，China；2. Shanghai Marine Diesel Engine Research Institute，Shanghai 201108，China；3. National Engineering Laboratory for Marine and Ocean Engineering Power System，Shanghai 201108，China
关键词:
船用柴油机; 高压油泵; 故障诊断; 改进灰狼优化; 变分模态分解; 散布熵
Keywords:
marine diesel engine; high-pressure fuel pump; fault diagnosis; improved grey wolf optimization; variational mode decomposition(VMD); dispersion entropy
分类号:
TK428
文献标志码:
A
摘要:
针对现有基于时域特征的高压油泵故障诊断准确率低的问题，笔者提出一种参数优化变分模态分解(VMD)算法和散布熵的特征提取方法，并采用支持向量机(SVM)进行故障诊断．首先，基于对高压油泵工作原理及典型故障的分析，利用AMESim平台搭建高压油泵仿真模型进行故障模拟和信号采集．然后，针对VMD效果受限于分解个数和惩罚因子选取的问题，采用改进灰狼优化(IGWO)算法对VMD进行参数寻优．通过计算各模态的散布熵值形成故障特征向量，最后，采用SVM对故障特征向量进行训练和诊断，实现高压油泵的故障诊断．该方法的故障诊断准确率可达到95%以上，能有效地实现高压油泵故障诊断．
Abstract:
To solve the problem of low accuracy of fault diagnosis of the high-pressure fuel pump based on time domain features，a feature extraction method for parameter optimization based on variational mode decomposition (VMD) algorithm and dispersion entropy was proposed，and support vector machine(SVM) was used for fault diagnosis. Firstly，based on the analysis of the working principle and typical faults of the high-pressure fuel pump，a simulation model of the high-pressure fuel pump built on AMESim platform was used simulate the faults and collect the signals. After that，to solve the issue that the decomposition effect of VMD is limited by the number of decomposition and the selection of penalty factors，improved grey wolf optimization(IGWO) algorithm was used to optimize the parameters. Then，the dispersion entropy value of each intrinsic mode function was calculated to form the fault feature vectors. Finally，SVM was used to train and diagnose the fault feature vectors，and the fault diagnosis of the high-pressure fuel pump was realized. The results show that the fault diagnosis accuracy can reach more than 95%，and the method proposed is effective to the fault diagnosis of high-pressure pump.

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更新日期/Last Update: 2023-03-25