基于IPSO-LS-SVM的异步电动机转子故障诊断
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摘要
针对异步电动机转子的故障诊断问题,为了提高诊断精度和诊断效率,提出基于混合核函数的最小二乘支持向量机与改进的粒子群算法相结合(IPSO-LS-SVM)的故障诊断方法。首先对PSO的惯性权值策略进行研究,给出一种非线性递减惯性权值策略,然后利用改进后的PSO优化基于混合核函数的LS-SVM,最后,应用改进算法完成转子的故障诊断。结果表明,改进算法通过较少的迭代次数即寻找到最优参数,克服了陷入局部极小值的缺陷,诊断效率和诊断精度都得到了提升。
In order to solve the problem of fault diagnosis for asynchronous motor rotor and improve the accuracy and efficiency of the diagnosis,a fault diagnosis method based on the least squares SVM and improved particle swarm optimization(PSO) algorithm is proposed.Firstly,by studying the inertia weight strategy of PSO,a nonlinear decreasing inertia weight strategy is given.Then,the improved PSO algorithm is used to optimize the mixed kernel function based on LS-SVM.Finally,the proposed algorithm is used to diagnose the rotor fault.The results show that this algorithm can find the optimal parameters through fewer iterations,which overcomes the defects of local minimum,and improves the diagnostic efficiency and diagnostic accuracy.
In order to solve the problem of fault diagnosis for asynchronous motor rotor and improve the accuracy and efficiency of the diagnosis,a fault diagnosis method based on the least squares SVM and improved particle swarm optimization(PSO) algorithm is proposed.Firstly,by studying the inertia weight strategy of PSO,a nonlinear decreasing inertia weight strategy is given.Then,the improved PSO algorithm is used to optimize the mixed kernel function based on LS-SVM.Finally,the proposed algorithm is used to diagnose the rotor fault.The results show that this algorithm can find the optimal parameters through fewer iterations,which overcomes the defects of local minimum,and improves the diagnostic efficiency and diagnostic accuracy.
引文
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[2]Han Q K,Chu F L.Dynamic response of cracked rotor-bearing system under time-dependent base movements[J].Journal of Sound and Vibration,2013,332(25):6847-6870.
[3]鲍晓华,吕强.感应电机气隙偏心故障研究综述及展望[J].中国电机工程学报,2013,33(6):93-100.
[4]Suykens J A K,Gestel T V,Brabanter J D,et al.Least square support vector machine[J].Euphytica,2002,2(2).
[5]李鑫滨,陈云强,张淑清.基于LS-SVM多分类器融合决策的混合故障诊断算法[J].振动与冲击,2013,32(19):159-164.
[6]胥永刚,盂志鹏,陆明.基于双树复小波包变换和SVM的滚动轴承故障诊断方法[J].航空动力学报,2014,29(1):67-73.
[7]Yildizer E,Bald A M,Hassan M,et al.Efficient contentbased image retrieval using multiple support vector machines ensemble[J].Expert Systems with Applications,2012,39(3):2385-2396.
[8]王冠林.基于模态分割方法的小型无人直升机动力学频域辨识建模[D].北京:清华大学,2012.
[9]林蔚天.改进的粒子群优化算法研究及其若干应用[D].上海:华东理工大学,2014.
[10]Donat W,Choi K,An W,et al.Data visualization,date reduction and classifier fusion for intelligent fault diagnosis in gas turbine engines[J].Journal of Engineering for Gas Turbines and Power,2008,130(4).
[11]Saimurugan M,Ramachandran K I,Sugumaran V,et al.Multi component fault diagnosis of rotational mechanical system based on decision tree and support vector machine[J].Expert Systems with Applications,2011,38(4).