A Fault Diagnosis Method Based on Improved Bat Algorithm Optimization Support Vector Machine
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摘要: 提出了一种基于变分模态分解（VMD）和时移多尺度散布熵（TSMDE）的故障特征提取结合改进的蝙蝠算法（IBA）来优化支持向量机（SVM）的滚动轴承故障诊断方法。通过变分模态分解，避免了模式混叠问题，提取各模态分量的散布熵构造故障特征向量，作为故障诊断模型的输入；提出了一种新的自适应速度权重因子用于构建改进的蝙蝠算法以优化支持向量机（IBA-SVM），实现了对不同故障类型的轴承进行分类；利用实验数据对提出的诊断方法进行验证，并与用粒子群算法（PSO）优化支持向量机（PSO-SVM）的诊断方法进行对比。结果表明所提出的方法分类准确率更高，用时更少。Abstract: A rolling bearing fault diagnosis method based on variational mode decomposition (VMD) combined with time-shift multiscale dispersion entropy(TSMDE) fault feature extraction and improved bat algorithm (IBA) in order to optimize support vector machine (SVM)was proposed. Firstly, the problem of mode aliasing was avoidedby means of variational mode decomposition, and the dispersion entropy of each modal component was extracted to construct the fault feature vector, which was used as the input of the fault diagnosis model. Then, a new adaptive speed weight factor was proposed to construct an improved bat algorithm for optimizing support vector machine (IBA-SVM), and the bearings with different fault typeswereclassified. Finally, the experimental data were used to verify the proposed diagnostic method and compared with the particle swarm optimization support vector machine (PSO-SVM) method. The results show that the proposed method has higher classification accuracy and less time.
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表 1 数据采集装置参数
故障
类型轴承型号 电机转速/
（r·min−1）故障尺寸/
mm采样
频率/
kHz采样
时间/
s内圈
故障6205-2RS JEM SKF 1772 0.177 8 12000 10 
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表 2 不同分解层数下的中心频率
分解层数K 中心频率/Hz 3 691 2757 3572 4 616 1349 2761 3572 5 615 1347 2750 3623 3623
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表 3 各模态分量熵值
IMF1 IMF2 IMF3 NR 2.2093 3.1791 3.0544 IR 3.2479 3.4435 3.4545 OR 3.5113 3.5294 3.1390 BR 2.7063 3.5787 3.5670
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