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基于ICEEMDAN-MPE-RF和SVM的齿轮箱特征提取与故障诊断
Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM
- 机车电传动 2023年第1期 页码：42-50
- 作者机构：
上海电力大学 电气工程学院，上海 200090
- 作者简介：
张宇华（1975—），男，博士，副教授，主要从事电力设备故障诊断和状态监测；E-mail：zhyhdq@126.com
- 基金信息：上海绿色能源并网工程技术研究中心项目(13DZ2251900);上海市科技创新行动计划项目(19DZ2204700)
DOI：10.13890/j.issn.1000-128X.2023.01.006
中图分类号： TH17;TH132纸质出版日期：2023-01-10，
收稿日期：2022-07-31，
修回日期：2022-11-26，
- 稿件说明：
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丁晓锋, 张宇华. 基于ICEEMDAN-MPE-RF和SVM的齿轮箱特征提取与故障诊断[J]. 机车电传动, 2023(1): 42-50.
DING Xiaofeng, ZHANG Yuhua. Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM[J]. Electric Drive for Locomotives, 2023(1): 42-50.
丁晓锋, 张宇华. 基于ICEEMDAN-MPE-RF和SVM的齿轮箱特征提取与故障诊断[J]. 机车电传动, 2023(1): 42-50. DOI： 10.13890/j.issn.1000-128X.2023.01.006.
DING Xiaofeng, ZHANG Yuhua. Feature extraction and fault diagnosis of gearbox based on ICEEMDAN, MPE, RF and SVM[J]. Electric Drive for Locomotives, 2023(1): 42-50. DOI： 10.13890/j.issn.1000-128X.2023.01.006.
摘要
针对齿轮箱非平稳振动信号特征提取难、特征向量冗余度高和故障识别率低的问题，提出基于改进的自适应噪声完备集成经验模态分解（Improved complete ensemble empirical mode decomposition with adaptive noise，ICEEMDAN）、多尺度排列熵（Multi-scale permutation entropy，MPE）、随机森林（Random forest，RF）特征重要性排序和支持向量机（Support vector machine，SVM）的齿轮箱特征提取与故障诊断方法。首先，通过ICEEMDAN将各种故障状态的齿轮振动信号分解为一系列不同频率分布的本征模态分量（Intrinsic mode functions，IMF）；然后，计算各阶IMF的MPE值获得非平稳信号时频分布下的非线性动力学特征；最后，通过RF算法评估特征重要性，选择高重要性敏感特征组成最优特征子集输入SVM进行故障模式识别。试验结果表明，该方法特征提取和表征能力强，在不同工况下的平均识别率可达99.79%，在多工况和小样本数据集上比其他方法更具稳健性。
Abstract
To solve the challenges related to non-stationary vibration signals in gearboxes
i.e. difficult feature extraction
high redundancy of feature vectors and low fault identification rate
this paper proposed a method of feature extraction and fault diagnosis of gearboxes based on the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)
multi-scale permutation entropy (MPE)
random forest (RF) feature importance ranking and support vector machine (SVM). Firstly
the vibration signals of gears in various fault states were decomposed into a series of intrinsic mode functions (IMF) with different frequency distributions by ICEEMDAN. Then
the MPE values of the IMFs were calculated to obtain the nonlinear dynamic features of non-stationary signals in time-frequency distribution. Finally
the importance of such features was evaluated by the RF algorithm
and the sensitive features with high importance were selected to form the optimal feature subset as the input to SVM for fault pattern recognition. The experimental results show that this method with strong feature extraction and representation ability and as high as 99.79% recognition rate on average under different operating conditions is more robust in multi-operating conditions and small sample data sets
compared with other methods.
关键词
齿轮箱改进的自适应噪声完备集成经验模态分解多尺度排列熵随机森林支持向量机特征提取故障诊断
Keywords
gearboximproved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)multi-scale permutation entropy (MPE)random forest (RF)support vector machine (SVM)feature extractionfault diagnosis
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