基于概率神经网络算法的永磁同步直线电机局部退磁故障诊断研究
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摘要
针对永磁同步直线电机(permanent magnet synchronous linear motor,PMSLM)的局部退磁故障问题,引入一种基于空间气隙磁密重构特征提取与概率神经网络(probabilistic neural network,PNN)算法相结合的局部退磁故障分类识别方法。采用等效磁化强度法分析永磁体在局部退磁情况下,PMSLM气隙磁密在不同空间位置的分布特性;利用有限元法定量计算PMSLM空间气隙中心线、气隙中心线上方、气隙中心线下方3个位置处的气隙磁密强度,将其融合为唯一识别退磁故障类型的特征量,并进行多种局部退磁故障类型下的仿真分析,建立了丰富的退磁故障样本库;建立神经网络和径向基网络,并对PNN网格结构进行优化,利用PNN分类算法实现局部退磁故障的精确分类识别,并进行分类器精度校验仿真实验。样机实验结果表明,所提方法能够准确辨识PMSLM局部退磁故障的组合类型,识别率高达到99.4%。
A partial demagnetization fault classification and identification method based on the spatial air gap flux density reconstruction and probabilistic neural network(PNN) algorithm was introduced to solve the partial demagnetization faults of permanent magnet synchronous linear motors(PMSLM). An equivalent magnetization method was used to analyze the distribution characteristics of PMSLM air gap flux density in different spatial locations under the condition of local demagnetization of permanent magnets. Finite element method was used to quantitatively calculate the air gap flux density on, above and below the air gap centerline, and they were fused into a special fault feature quantity to uniquely identify the types of demagnetization, and the simulation analysis under various types of demagnetization faults were carried out to build rich demagnetization sample database. Finally, the neural network and the radial basis network were established to make the grid structure optimized, and the PNN algorithm was used to realize the accurate classification and identification of local demagnetization faults, and the accuracy of the classifier was simulated. Prototype experiment results show that the proposed approach can classify PMSLM local demagnetization fault types, and the recognition rate is of 99.4%.
A partial demagnetization fault classification and identification method based on the spatial air gap flux density reconstruction and probabilistic neural network(PNN) algorithm was introduced to solve the partial demagnetization faults of permanent magnet synchronous linear motors(PMSLM). An equivalent magnetization method was used to analyze the distribution characteristics of PMSLM air gap flux density in different spatial locations under the condition of local demagnetization of permanent magnets. Finite element method was used to quantitatively calculate the air gap flux density on, above and below the air gap centerline, and they were fused into a special fault feature quantity to uniquely identify the types of demagnetization, and the simulation analysis under various types of demagnetization faults were carried out to build rich demagnetization sample database. Finally, the neural network and the radial basis network were established to make the grid structure optimized, and the PNN algorithm was used to realize the accurate classification and identification of local demagnetization faults, and the accuracy of the classifier was simulated. Prototype experiment results show that the proposed approach can classify PMSLM local demagnetization fault types, and the recognition rate is of 99.4%.
引文
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[15]Yu F,Xu X.A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network[J].Applied Energy,2014,134(134):102-113.
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[18]禹建丽,卞帅.基于BP神经网络的变压器故障诊断模型[J].系统仿真学报,2014,26(6):1343-1349.Yu Jianli,Bian Shuai.Fault diagnosis model of transformer based on BP neural network[J].Journal of system Simulation,2014,26(6):1343-1349(in Chinese).
[19]刘乐,孙虎儿,谢志谦.基于SVD-LMD模糊熵与PNN的滚动轴承故障诊断[J].机械传动,2017(3):172-176.Liu Le,Sun Huer,Xie Zhiqian.Roller bearing fault diagnosis based on fuzzy entropy and PNN of SVD-LMD[J].Journal of Mechanical,2017(3):172-176(in Chinese).
[20]Wang H,Wang P,Liu T.Power quality disturbance classification using the S-transform and probabilistic neural network[J].Energies,2017(10):107.
[21]连远锋,李国和,吴发林,等.基于遗传PNN网络的组合导航故障诊断研究[J].仪器仪表学报,2012,33(1):120-126.Lian Yuanfeng,Li Guohe,Wu Falin,et al.Fault-diagnosis method for INS/GPS integrated navigation system based on PNN and genetic algorithm[J].Chinese Journal of Scientific Instrument,2012,33(1):120-126(in Chinese).
[22]唐旭,王秀和,李莹.三相不对称供电异步起动永磁同步电动机的退磁研究[J].中国电机工程学报,2015,35(23):6172-6178.Tang Xu,Wang Xiuhe,Li Ying.Demagnetization study for line-start permanent magnet synchronous motor fed by three-phase unbalanced voltages[J].Proceedings of the CSEE,2015,35(23):6172-6178(in Chinese).
[2]邓江明,陈特放,唐建湘,等.单边直线感应电机动态最大推力输出的滑差频率优化控制[J].中国电机工程学报,2013,33(12):123-130.Deng Jiangming,Chen Tefang,Tang Jianxiang,et al.Optimum slip frequency control of maglev single-sided linear induction motors to maximum dynamic thrust[J].Proceedings of the CSEE,2013,33(12):123-130(in Chinese).
[3]Li L,Tang Y,Pan D.Design optimization of air-cored pmlsm with overlapping windings by multiple population genetic algorithm[J].IEEE Transactions on Magnetics,2014,50(11):1-5.
[4]李立毅,唐勇斌,刘家曦,等.多种群遗传算法在无铁心永磁直线同步电机优化设计中的应用[J].中国电机工程学报,2013,33(15):69-77.Li Liyi,Tang Yongbin,Liu Jiaxi,et al.Application of the multiple population genetic algorithm in optimum design of air-core permanent magnet linear synchronous motors[J].Proceedings of the CSEE,2013,33(15):69-77(in Chinese).
[5]Hui Wang,Jing Zhao,Jiwen Zhao,et al.Position detection method of linear motor mover based on extended phase correlation algorithm[J].IET Science,Measurement&Technology,2017,11(7):921-928.
[6]唐旭,王秀和,李莹,等.异步起动永磁同步电动机起动过程中永磁体退磁研究[J].中国电机工程学报,2015,35(4):961-970.Tang Xu,Wang Xiuhe,Li Ying,et al.Demagnetization study for line-start permanent magnet synchronous motor during starting process[J].Proceedings of the CSEE,2015,35(4):961-970(in Chinese).
[7]Xing Junqiang,Wang Fengxiang,Wang Tianyu,et al.Study on anti-demagnetization of magnet for high speed permanent magnet machine[J].IEEE Transactions on Applied Superconductivity,2010,20(3):856-860.
[8]张志艳,马宏忠,陈诚,等.永磁电机失磁故障诊断方法综述[J].微电机,2013,46(3):77-80.Zhang Zhiyan,Ma Hongzhong,Chen Cheng,et al.Review of demagnetization fault diagnosis in permanent magnet motor[J].Micromotors Servo Technique,2013,46(3):77-80(in Chinese).
[9]张志艳,曹祥红,马宏忠,等.永磁同步电机均匀失磁故障性能分析[J].微电机,2014(9):9-13.Zhang Zhiyan,Cao Xianghong,Ma Hongzhong,et al.Performance analysis of uniform demagnetization fault in permanent magnet synchronous motor[J]Micromotors Servo Technique,2014(9):9-13(in Chinese).
[10]李红梅,陈涛.电动汽车PMSM退磁故障诊断及故障模式识别[J].电工技术学报,2017,32(5):1-8.Li Hongmei,Chen Tao.Demagnetization fault diagnosis and fault mode recognition of PMSM for EV[J].Transactions of China Electrotechnical Society,2017,32(5):1-8(in Chinese).
[11]Ruiz J R R,Rosero J A,Espinosa A G,et al.Detection of demagnetization faults in permanent-magnet synchronous motors under nonstationary conditions[J].IEEE Transactions on Magnetics,2009,45(7):2961-2969.
[12]Ding S,Hang J,Li H,et al.Demagnetisation fault detection in PMSM using zero sequence current components[J].Electronics Letters,2017,53(3):148-150.
[13]卢伟甫,罗应立,赵海森.自起动永磁同步电机起动过程电枢反应退磁分析[J].电机与控制学报,2012,16(7):29-33.Lu Weifu,Luo Yingli,Zhao Haisen.Armature reaction demagnetization analysis for line-start permanent magnet synchronous motor during start process[J].Electric Machines and Control,2012,16(7):29-33(in Chinese).
[14]Torregrossa D,Khoobroo A,Fahimi B.Prediction of acoustic noise and torque pulsation in PM synchronous machines with static eccentricity and partial demagnetization using field reconstruction method[J].IEEE Transactions on Industrial Electronics,2012,59(2):934-944.
[15]Yu F,Xu X.A short-term load forecasting model of natural gas based on optimized genetic algorithm and improved BP neural network[J].Applied Energy,2014,134(134):102-113.
[16]Jing C,Hou J.SVM and PCA based fault classification approaches for complicated industrial process[J].Neurocomputing,2015,167(C):636-642.
[17]成杰,李新德.基于Hu矩与改进PNN的飞机姿态识别算法[J].航空兵器,2017(1):55-61.Cheng Jie,Li Xinde.An algorithm of aircraft pose recognition based on Hu moments and IPNN[J].Aero Weaponry,2017(1):55-61(in Chinese).
[18]禹建丽,卞帅.基于BP神经网络的变压器故障诊断模型[J].系统仿真学报,2014,26(6):1343-1349.Yu Jianli,Bian Shuai.Fault diagnosis model of transformer based on BP neural network[J].Journal of system Simulation,2014,26(6):1343-1349(in Chinese).
[19]刘乐,孙虎儿,谢志谦.基于SVD-LMD模糊熵与PNN的滚动轴承故障诊断[J].机械传动,2017(3):172-176.Liu Le,Sun Huer,Xie Zhiqian.Roller bearing fault diagnosis based on fuzzy entropy and PNN of SVD-LMD[J].Journal of Mechanical,2017(3):172-176(in Chinese).
[20]Wang H,Wang P,Liu T.Power quality disturbance classification using the S-transform and probabilistic neural network[J].Energies,2017(10):107.
[21]连远锋,李国和,吴发林,等.基于遗传PNN网络的组合导航故障诊断研究[J].仪器仪表学报,2012,33(1):120-126.Lian Yuanfeng,Li Guohe,Wu Falin,et al.Fault-diagnosis method for INS/GPS integrated navigation system based on PNN and genetic algorithm[J].Chinese Journal of Scientific Instrument,2012,33(1):120-126(in Chinese).
[22]唐旭,王秀和,李莹.三相不对称供电异步起动永磁同步电动机的退磁研究[J].中国电机工程学报,2015,35(23):6172-6178.Tang Xu,Wang Xiuhe,Li Ying.Demagnetization study for line-start permanent magnet synchronous motor fed by three-phase unbalanced voltages[J].Proceedings of the CSEE,2015,35(23):6172-6178(in Chinese).
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