融合Morlet小波与GA优化多模态核的轴承故障检测算法
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摘要
针对轴承故障检测算法特征分辨性较低、准确度较低等问题,提出一种融合Morlet小波和遗传算法优化的多模态核方法轴承故障检测算法.该算法首先针对原始轴承故障信号提取多个尺度和多个位移条件下的Morlet小波变换特征,然后设计一个多模态核方法,包含线性核函数与径向基(RBF)核函数,最后在支持向量机(SVM)训练过程中采用遗传算法(GA)优化多模态核的参数,使用最优化多模态核进行轴承故障检测.在UoCn的智能维护中心数据集上分别测试了滚珠故障、内圈裂纹故障和外圈裂纹故障的检测,并对单一核与多模态核间的错误率与效率进行对比.实验结果表明,改进算法能获得鲁棒的轴承故障检测特征,且多模态核在GA的优化下能快速收敛,获得最优化结果,通过牺牲少量的时间效率而极大提升了轴承故障检测准确率.
The author proposed a bearing fault detection algorithm based on Morlet wavelet and multimode kernel optimized by genetic algorithm.Firstly,the algorithm extracted the characteristics of Morlet wavelet transform under conditions of multi-scale and multiple displacement for the fault signal of original bearing.Secondly,the author designed a multi-mode kernel method,including linear kernel and radical basis function(RBF)kernel.Finally,the genetic algorithm(GA)was used to optimize parameters of multi-mode kernel in the support vector machine(SVM)training process,and the optimizing multi-mode kernel for bearing fault detection was carried out.The ball fault,inner ring crack fault and outer ring crack fault were tested on the data set of UoCn intelligent maintenance center,and the error rate and efficiency of single-mode kernel and multi-mode kernel were compared.The experimental results show that the improved algorithm can get the robust features for bearing fault detection,and the multi-mode kernel can converge quickly and get optimal results under theoptimization of GA,the accuracy of bearing fault detection is greatly improved by sacrificing a small amount of time efficiency.
The author proposed a bearing fault detection algorithm based on Morlet wavelet and multimode kernel optimized by genetic algorithm.Firstly,the algorithm extracted the characteristics of Morlet wavelet transform under conditions of multi-scale and multiple displacement for the fault signal of original bearing.Secondly,the author designed a multi-mode kernel method,including linear kernel and radical basis function(RBF)kernel.Finally,the genetic algorithm(GA)was used to optimize parameters of multi-mode kernel in the support vector machine(SVM)training process,and the optimizing multi-mode kernel for bearing fault detection was carried out.The ball fault,inner ring crack fault and outer ring crack fault were tested on the data set of UoCn intelligent maintenance center,and the error rate and efficiency of single-mode kernel and multi-mode kernel were compared.The experimental results show that the improved algorithm can get the robust features for bearing fault detection,and the multi-mode kernel can converge quickly and get optimal results under theoptimization of GA,the accuracy of bearing fault detection is greatly improved by sacrificing a small amount of time efficiency.
引文
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[11]杨望灿,张培林,张云强,等.基于邻域自适应局部保持投影的轴承故障诊断模型[J].振动与冲击,2014(1):39-44.(YANG Wangcan,ZHANG Peilin,ZHANG Yunqiang,et al.The Bearing Fault Diagnosis Model Based on Neighborhood Self-adaptation Is Maintained[J].Vibration and Shock,2014(1):39-44.)
[12]LIN Jing,QU Liangsheng.Feature Extraction Based on Morlet Wavelet and Its Application for Mechanical Fault Diagnosis[J].Journal of Sound and Vibration,2000,234(1):135-148.
[13]ZHENG Haibao,Li Z,Chen X.Gear Fault Diagnosis Based on Continuous Wavelet Transform[J].Mechanical Systems and Signal Processing,2002,16(2/3):447-457.
[14]颜晓娟,龚仁喜,张千锋,等.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.(YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng,et al.The Application of Optimized Genetic Algorithm for Optimization of SVM in Short-Term Wind Speed Prediction[J].Power System Protection and Control,2016,44(9):38-42.)
[15]Zhou E,Fu M C,Marcus S I.A Particle Filtering Framework for Randomized Optimization Algorithms[C]//Winter Simulation Conference.Piscataway,NJ:IEEE,2008:647-654.
[2]李军利,李巍华.一种半监督SVDD-KFCM算法及其在轴承故障检测中的应用[J].计算机科学,2015,42(z1):134-137.(LI Junli,LI Weihua.A Semi-supervised SVDD-KFCM Algorithm and Its Application in Bearing Fault Detection[J].Journal of Computer Science,2015,42(z1):134-137.)
[3]张新鹏,胡茑庆,程哲,等.信号稀疏分解理论在轴承故障检测中的应用[J].国防科技大学学报,2016,38(3):141-147.(ZHANG Xinpeng,HU Yiqing,CHENG Zhe,et al.The Application of Signal Sparse Decomposition Theory in Bearing Fault Detection[J].Journal of National Defense Technology University,2016,38(3):141-147.)
[4]LIU Haiyang,HUANG Weiguo,WANG Shibin,et al.Adaptive Spectral Kurtosis Filtering Based on Morlet Wavelet and Its Application for Signal Transients Detection[J].Signal Processing,2014,96:118-124.
[5]廖辉,邓四二,王春艳,等.小波分析技术在矿山机械轴承故障检测中的综合应用[J].矿山机械,2014,42(6):122-126.(LIAO Hui,DENG Si’er,WANG Chunyan,et al.The Application of Wavelet Analysis Technique in Fault Detection of Mechanical Bearings in Mines[J].Mining Machinery,2014,42(6):122-126.)
[6]Czajkowski K M,Anna P,Rafal K.Single-Pixel Imaging with Morlet Wavelet Correlated Random Patterns[J/OL].2017-09-22.http://arxiv.or/abs/1709.07739.
[7]Neto O P,Pinheiro A O,Pereira V L,et al.Morlet Wavelet Transforms of Heart Rate Variability for Autonomic Nervous System Activity[J].Applied and Computational Harmonic Analysis,2016,40(1):200-206.
[8]朱韶平.基于LDA模型的滚动轴承故障类型检测[J].轴承,2014(7):42-46.(ZHU Shaoping.Detection of Rolling Bearing Fault Type Based on LDA Model[J].Bearings,2014(7):42-46.)
[9]Gryllias K C,Ioannis A A.Estimation of the Instantaneous Rotation Speed Using Complex Shifted Morlet Wavelets[J].Mechanical Systems and Signal Processing,2013,38(1):78-95.
[10]马伦,康建设,孟妍,等.基于Morlet小波变换的滚动轴承早期故障特征提取研究[J].仪器仪表学报,2013,34(4):920-926.(MA Lun,KANG Jianshe,MENG Yan,et al.Study on Early Fault Feature Extraction of Rolling Bearing Based on Morlet Wavelet Transform[J].Chinese Journal of Scientific Instrument,2013,34(4):920-926.)
[11]杨望灿,张培林,张云强,等.基于邻域自适应局部保持投影的轴承故障诊断模型[J].振动与冲击,2014(1):39-44.(YANG Wangcan,ZHANG Peilin,ZHANG Yunqiang,et al.The Bearing Fault Diagnosis Model Based on Neighborhood Self-adaptation Is Maintained[J].Vibration and Shock,2014(1):39-44.)
[12]LIN Jing,QU Liangsheng.Feature Extraction Based on Morlet Wavelet and Its Application for Mechanical Fault Diagnosis[J].Journal of Sound and Vibration,2000,234(1):135-148.
[13]ZHENG Haibao,Li Z,Chen X.Gear Fault Diagnosis Based on Continuous Wavelet Transform[J].Mechanical Systems and Signal Processing,2002,16(2/3):447-457.
[14]颜晓娟,龚仁喜,张千锋,等.优化遗传算法寻优的SVM在短期风速预测中的应用[J].电力系统保护与控制,2016,44(9):38-42.(YAN Xiaojuan,GONG Renxi,ZHANG Qianfeng,et al.The Application of Optimized Genetic Algorithm for Optimization of SVM in Short-Term Wind Speed Prediction[J].Power System Protection and Control,2016,44(9):38-42.)
[15]Zhou E,Fu M C,Marcus S I.A Particle Filtering Framework for Randomized Optimization Algorithms[C]//Winter Simulation Conference.Piscataway,NJ:IEEE,2008:647-654.