| 李远军,孙继炫.基于特征提取与识别两阶段的汽车电机轴承故障诊断[J].电子测量与仪器学报,2019,33(2):56-63 |
| 基于特征提取与识别两阶段的汽车电机轴承故障诊断 |
| Car motor bearing fault diagnosis based on fault feature extraction and recognition stages |
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| DOI： |
| 中文关键词: LCD符号熵 历史学习 果蝇算法 相关向量机 轴承故障诊断 |
| 英文关键词:LCD symbol entropy history study fruit fly optimization algorithm relevance vector machine bearing fault diagnosis |
| 基金项目:湖北职业教育教学改革项目（G2012B037）、北京市自然科学基金（1183126）资助项目 |
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| 中文摘要: |
| 针对特征提取和故障识别这两个轴承故障诊断的关键环节，提出一种汽车电机轴承故障诊断新方法。该方法在特征提取环节：提出了将LCD分解和符号熵（SE）相结合的特征提取方法；在故障识别环节为提高果蝇算法（FOA）对相关向量机（RVM）参数的寻优能力，在FOA算法中增加了向“历史”学习的策略，提出具有历史学习能力的果蝇算法（HSAFOA），有效地提升了RVM的分类性能。汽车电机轴承不同类型、不同程度故障诊断实例表明，LCD符号熵可有效对故障进行表征，而HSAFOA算法则提升了RVM的识别效果，相比于其他一些方法更有优势。 |
| 英文摘要: |
| Aiming at the two key points (feature extraction and fault recognition) of bearing fault diagnosis, a new car motor bearing fault diagnosis method was proposed. At the feature extraction link: a feature extraction method based on LCD decomposition and symbol entropy was proposed. At the fault identification link: in order to improve search ability of fruit fly optimization algorithm (FOA) to relevance vector machine (RVM), study of “history” strategy was introduced to FOA, then, FOA with history study ability (HSAFOA) was proposed and effectively improved the classification performance of RVM. Different fault types and different fault degrees of rolling bearing fault diagnosis experiment results show that the LCD symbol entropy can represent fault effectively and HSAFOA improved the identification accuracy of RVM, it has a certain superiority when compared with some other methods. |
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