| 陈 鹏,赵小强,朱奇先.基于多尺度排列熵和改进多分类相关向量机的
滚动轴承故障诊断方法[J].电子测量与仪器学报,2020,34(2):20-28 |
| 基于多尺度排列熵和改进多分类相关向量机的
滚动轴承故障诊断方法 |
| Rolling bearing fault diagnosis method based on multi scale permutationentropy and improved multi class relevance vector machine |
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| DOI： |
| 中文关键词: 滚动轴承 故障诊断 多尺度排列熵 多分类相关向量机 蝗虫优化算法 |
| 英文关键词:rolling bearings fault diagnosis multi scale permutation entropy multi class relevant vector machine grasshopper optimization algorithm |
| 基金项目:国家自然科学基金（61763029）、大型电气传动系统与装备技术国家重点实验室开放基金（SKLLDJ012016020）资助项目 |
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| 中文摘要: |
| 针对传统的时域、频域和时频域参数提取方法，难以从滚动轴承振动信号中提取出丰富的故障特征问题，提出通过多尺度排列熵提取故障特征，并结合改进的多分类相关向量机进行故障诊断的方法。由于多分类相关向量机的核函数参数不具有自适应选择的能力对故障诊断精度有较大影响，通过一种新智能优化算法 蝗虫优化算法改进多分类相关向量机，实现多分类相关向量机的自适应优化故障诊断。采用美国西储大学的试验数据验证表明，提出的优化故障诊断模型能够实现滚动轴承不同类型的故障诊断和不同故障程度的辨识，与粒子群优化多分类相关向量机的故障诊断模型相比，提出的故障诊断模型准确率达到了100%。 |
| 英文摘要: |
| It’s difficult to extract the rich fault information from vibration signal by the traditional feature extraction methods of time domain, frequency domain and time frequency domain parameter. In order to solve this problem, a multi scale permutation entropy is proposed to extract fault features and combine the improved multi class relevant vector machine to fault diagnosis. Since the kernel parameters of the multi class relevant vector machine do not have the adaptive ability, it has the great influence on the accuracy of fault diagnosis. The multi class relevance vector machine is improved by a grasshopper optimization algorithm to realize the adaptive fault diagnosis. The experimental data from the University of Western Reserve in the United States show that the proposed optimized fault diagnosis model can realize the fault diagnosis of different types and the identification of different fault degrees. Compared with the fault diagnosis model of particle swarm optimization optimizes multi class relevant vector machine, the accuracy of proposed fault diagnosis model is 100%. |
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