EEMD与NRS在涡桨发动机转子故障诊断中的应用

丁锋; 栗祥; 韩帅

doi:10.13224/j.cnki.jasp.2018.06.016

EEMD与NRS在涡桨发动机转子故障诊断中的应用

doi: 10.13224/j.cnki.jasp.2018.06.016

西安工业大学机电工程学院，西安 710021

基金项目: 国家自然科学基金（51275374）

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出版历程
- 收稿日期: 2016-12-20
- 刊出日期: 2018-06-28

Application of EEMD and NRS in turboprop engine rotor fault diagnosis

摘要

摘要: 针对涡桨发动机转子系统振动信号的非平稳特征，提出一种基于集成经验模态分解(EEMD)与邻域粗糙集(NRS)的涡桨发动机转子故障诊断方法。该方法先对转子振动信号进行EEMD，提取原始信号的时域特征和多尺度排列熵(MPE)特征，转子系统的大部分故障信息隐藏在前几个高频本征模态函数（IMFs）中，分别计算它们的时域指标、能量特征和奇异值分解(SVD)特征；利用NRS评估各个特征的属性重要度，进而选出敏感特征；将其作为支持向量机(SVM)的输入向量来对转子进行故障诊断。实验结果表明:该方法利用敏感特征集对涡桨发动机转子进行故障诊断的准确率达到了97.5%，同时剔除了大量冗余特征，具有较强的鲁棒性。
- 涡桨发动机 /
- 转子 /
- 故障诊断 /
- 集成经验模态分解(EEMD) /
- 邻域粗糙集(NRS)
Abstract: According to the nonstationary characteristics of vibration signals of turboprop engine rotor system, a rotor fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and neighborhood rough set (NRS) was proposed. EEMD was used to decompose vibration signals. Then the time domain features and multiscale permutation entropies (MPE) of original vibration signals were calculated. Most of the fault information of rotor system was contained in the first several intrinsic mode functions (IMFs). Their time domain indicators, energy features and singular value decomposition (SVD) features were also separately calculated. NRS was applied to evaluate the attribute importance of different features, and sensitive features were selected. The sensitive feature set was fed into support vector machine (SVM) for recognizing different rotor fault states. Experimental results demonstrated that the fault diagnosis accuracy of the method reached 97.5%, and a large number of redundant features were eliminated, indicating that the method had stronger robustness.
- turboprop engine /
- rotor /
- fault diagnosis /
- ensemble empirical mode decomposition (EEMD) /
- neighborhood rough set (NRS)

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