磷虾算法优化多分类支持向量机的轴承故障诊断
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摘要
为了提高滚动轴承故障类型诊断准确度,提出了磷虾算法优化多分类支持向量机的轴承故障诊断方法。对于时频域特征参数的提取,将CEEMD算法与小波包优势结合,提出了CEEMD与小波包半软阈值去噪相结合的提取方法;对于特征参数降维,针对轴承振动信号的非线性特点,使用局部线性嵌入算法降维,对降维后特征参数使用模糊C均值聚类进行验证,可以看出LLE降维不仅降低了计算量而且有利于模式识别;将二叉树法与投票法支持向量机结合,给出了混合多分类支持向量机,使用磷虾算法对其进行参数优化。实验验证可知,磷虾算法优化的多分类支持向量机具有很高的输出精度,轴承状态识别准确率为100%,使用粒子群算法优化的支持向量机输出精度低,轴承状态识别准确率为79%。
To improve fault diagnosis accuracy degree of rolling bearing,fault diagnosis method based on multi-classification support vector machine optimized by krill algorithm is proposed. For extracting time and frequency domain parameters,combing advantages of CEEMD and wavelet packet,parameters extracting method integrating CEEMD and wavelet packet is put forward. For characteristic parameters dimension,considering nonlinearity of bearing vibration signal,LLE algorithm is used to reduce the dimension,clarified by fuzzy C-means clustering,dimensionality reduction not only can reduce computation,but can also benefit pattern recognition. Binary tree and voting method are integrated,so that a new mixed multi-classification SVM is given,and its parameters are optimized by krill algorithm. It can be seen through experiment,output accuracy of SVM optimized by krill algorithm is very high,and bearing state recognition accuracy degree is 100%. Output accuracy of SVM optimized by PSO algorithm is relatively low,and bearing state recognition accuracy degree is 79%
To improve fault diagnosis accuracy degree of rolling bearing,fault diagnosis method based on multi-classification support vector machine optimized by krill algorithm is proposed. For extracting time and frequency domain parameters,combing advantages of CEEMD and wavelet packet,parameters extracting method integrating CEEMD and wavelet packet is put forward. For characteristic parameters dimension,considering nonlinearity of bearing vibration signal,LLE algorithm is used to reduce the dimension,clarified by fuzzy C-means clustering,dimensionality reduction not only can reduce computation,but can also benefit pattern recognition. Binary tree and voting method are integrated,so that a new mixed multi-classification SVM is given,and its parameters are optimized by krill algorithm. It can be seen through experiment,output accuracy of SVM optimized by krill algorithm is very high,and bearing state recognition accuracy degree is 100%. Output accuracy of SVM optimized by PSO algorithm is relatively low,and bearing state recognition accuracy degree is 79%
引文
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[10]马元元,郝海涛,杨延娇.基于局部线性重构与高斯核映射的聚类研究[J].控制工程,2017,24(7):1493-1500.
[2]蔡剑华,黄国玉,黎小琴.基于频率切片小波变换时频分析的齿轮故障诊断[J].机械强度,2017,39(5):1026-1030.
[3]宁志强,陶元芳.智能语音交互机械故障诊断专家系统研究[J].中国工程机械学报,2018,16(1):88-95.
[4]李钦奉,薛晓听,朱永梅.基于故障树的机床设备润滑系统可靠性分析[J].组合机床与自动化加工技术,2018(6):65-67.
[5]Li Z,Outbib R,Giurgea S,et al.Online implementation of SVM based fault diagnosis strategy for PEMFC systems[J].Applied Energy,2016,164(2):284-293.
[6]Xu Y,Gao Y,Li Q.Imbalance fault diagnosis of helicopter rotor based on wavelet transform and neutral network[J].Nanjing Hangkong Hangtian Daxue Xuebao/journal of Nanjing University of Aeronautics&Astronautics,2017,49(2):212-218.
[7]凌鹏,陈跃威.基于CEEMD互近似熵和FCM滚动轴承故障诊断[J].计算机仿真,2018,35(3):314-410.
[8]Li J,Liu C,Zeng Z,et al.GPR signal denoising and target extraction with the CEEMD method[J].IEEE Geoscience&Remote Sensing Letters,2015,12(8):1615-1619.
[9]梁波.基于小波包和希尔伯特包络分析的盾构机主轴承故障诊断方法研究[D].兰州:兰州理工大学,2018.
[10]马元元,郝海涛,杨延娇.基于局部线性重构与高斯核映射的聚类研究[J].控制工程,2017,24(7):1493-1500.