| 引用本文: | 刘晓东,刘朦月,陈寅生,朱文炜.EEMD-PE与M-RVM相结合的轴承故障诊断方法[J].哈尔滨工业大学学报,2017,49(9):122.DOI:10.11918/j.issn.0367-6234.201604066 |
| LIU Xiaodong,LIU Mengyue,CHEN Yinsheng,ZHU Wenwei.Rolling bearing fault diagnosis based on EEMD-PE coupled with M-RVM[J].Journal of Harbin Institute of Technology,2017,49(9):122.DOI:10.11918/j.issn.0367-6234.201604066 |
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| 摘要: |
| 滚动轴承振动信号中包含了大量轴承运行状态信息，但是由于振动信号具有非线性和非平稳性的特点，难以充分提取振动信号中的故障特征，导致现有基于模式识别的轴承故障诊断方法的故障识别准确率较低.为了提高滚动轴承故障识别的准确率，提出了一种基于集合经验模态分解-排列熵(EEMD-PE)特征提取与多分类相关向量机(M-RVM)相结合的轴承故障诊断方法.首先，该方法利用EEMD对非线性和非平稳信号的自适应分解能力，将轴承故障信号分解为一组包含故障特征的本征模态函数(IMFs).然后，利用排列熵提取由EEMD分解得到的IMFs中的故障特征，并组成特征向量.最后，采用EEMD-PE对不同故障状态下的训练样本集进行特征提取，组成特征向量集对M-RVM分类器进行建模，以概率输出的形式实现对滚动轴承的故障诊断.实验结果表明：EEMD-PE特征提取方法能够对滚动轴承振动信号的故障特征进行有效提取，M-RVM能够对故障滚动轴承振动信号包含的故障特征进行识别.与现有轴承故障诊断方法相比较，所提出的方法能够提高故障识别准确率，达到99.58%.
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| 关键词: 滚动轴承 故障诊断 EEMD PE M-RVM |
| DOI：10.11918/j.issn.0367-6234.201604066 |
| 分类号:TH133.33 |
| 文献标识码:A |
| 基金项目: |
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| Rolling bearing fault diagnosis based on EEMD-PE coupled with M-RVM |
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LIU Xiaodong,LIU Mengyue,CHEN Yinsheng,ZHU Wenwei
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(School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China)
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| Abstract: |
| Vibration signals of faulty rolling bearing contain a large amount of information about the bearing operating status. However, it is difficult to extract the fault features completely because of its characteristics of nonlinearity and non-stationarity, which leads to a problem of relatively low fault identification rate of current fault diagnosis methods based on pattern recognition. In order to improve the accuracy of rolling bearing fault diagnosis, this paper proposes a fault diagnosis method of rolling bearing, which adopts ensemble empirical mode decomposition and permutation entropy (EEMD-PE) to extract the fault features coupled with multiclass relevance vector machine (M-RVM) to achieve the goal of fault classification. Firstly, the vibration signal of faulty rolling bearing decomposes into a series of intrinsic mode functions (IMFs) by using the adaptive decomposition ability of nonlinear and non-stationary signals. Afterwards, the fault features contained in IMFs are extracted by permutation entropy, and the features constitute the feauture vector. Finally, EEMD-PE method is used to extract the fault feaures of training sample set under different fault conditions. The M-RVM classifier is trained by using feature vector set, and the multiple fault identification is implemented in the form of probability output. The experimental results show that EEMD-PE feature extraction method can effectively extract fault features of rolling bearing vibration signal, M-RVM can identify the fault feature contained in rolling bearing vibration signals. Compared with the existing bearing fault diagnosis methods, this method can improve the fault identification rate reaching up to 99.58%.
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| Key words: rolling bearing fault diagnosis EEMD PE M-RVM |
