永磁直流电机故障在线监测与智能诊断的研究
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摘要
小功率永磁直流电机因其具有结构简单、运行可靠、体积小、重量轻、效率高等优点,被广泛地应用于汽车工业以及航空、航天领域。为了这些电机的安全运行,本文研究了一种简便的永磁直流电机故障在线监测与智能诊断方法。针对在线故障监测的实时性与简便性的要求,提出了一种基于电流信号分析与处理的永磁直流电动机常见故障的诊断方法。以易于检测的永磁直流电机电枢电流为故障诊断的信号源,综合利用基于信号处理的方法来实现故障特征的提取,基于人工智能方法实现故障模式识别。
在分析了永磁直流电动机的故障机理和电机动、稳态电流在正常与故障状态下的变化特性的基础上,建立了基于电枢电流分析的永磁直流电机故障诊断的数学模型。确立稳态电流i的均值ia v、稳态电枢电流频谱谱峰对应的脉动频率f及其脉动幅度i std、电机起动电流的峰值im和峰值点附近的下降速率k这五个量确立为永磁直流电机故障诊断的特征参数,并将它们相对于正常状态的变化量组成故障特征向量: Te = [ΔfΔi_(av)Δi_(std)Δi_mΔk ]。
以数台永磁直流电机为实验样本,对其常见的元件开路、匝间短路、电刷磨损,绕组脱焊四种故障进行空、负载电枢电流故障特征提取及故障机理分析,实验结果与故障机理分析的一致性表明:本文提出的基于电流分析的永磁直流电机故障诊断方法对永磁直流电机的常见故障的诊断具有通用性。
故障模式识别是实现故障智能诊断的关键环节。专家系统在故障诊断领域中的应用,实现了基于人类专家经验知识的故障模式智能识别技术。但由于永磁直流电机故障特征参数的分散性、随机性和模糊性较大,获取有效的永磁直流电机的诊断知识成为建造故障模式识别专家系统的关键问题之一。机器学习是解决知识获取问题的主要途径。目前应用较为广泛的基于机器学习的故障模式识别方法主要有统计模式识别方法和人工神经网络方法。但统计模式识别方法和人工神经网络方法都是建立在传统的统计学基础之上的分类算法,即只有在足够多的样本前提下,其算法才是合理的,而当训练的是小样本数据集时,就不宜再沿用该分类算法。工程应用中的永磁直流电机尤其航空航天应用领域中的电机不可能带故障长期运行,故难以获得大量典型故障样本。这就在一定程度上制约了统计模式识别和人工神经网络方法在该领域中的应用。
本文以支持向量机算法的数学模型为理论依据,构建了一种基于支持向量机的永磁直流电机故障模式识别分类器,对永磁直流电机的数种故障模式进行识别,并对其和应用BP神经网络识别方法在小样本故障模式识别中的应用进行了对比实验研究。结果表明:基于支持向量机的故障模式识别方法在小样本情况下的诊断精度要高于同样样本情况下的BP神经网络,且避免了基于BP神经网络方法中存在的过学习问题和陷入局部极小值问题。
针对永磁直流电机故障在线诊断中存在类样本数目不平衡、误判损失不等、在线样本数据缺少类别标识以及复杂环境中样本数据存在大量噪声野点等问题,本文对基于支持向量机的永磁直流电机故障模式识别算法作了如下两方面的改进:首先,通过对支持向量机数学模型中的误差惩罚因子进行加权,构建了一种基于加权支持向量机的永磁直流电机故障模式识别算法。理论分析和实验结果表明:该算法可以提高小样本类(故障样本类)诊断精度,降低误判损失。其次,采用了一种基于模糊C均值的聚类算法,对无类别标识的在线数据进行模糊聚类,并根据模糊聚类的隶属度,来判断每个样本数据的所属类,由此同时定位数据中的野点,消除野点后,再对消噪后的数据运用基于支持向量机的模式识别算法进行训练与测试。提高了永磁直流电机复杂运行工况下在线样本数据的故障识别精度,扩充了基于支持向量机的故障模式识别算法的应用领域。
本方法只需要监测电机电枢电压与电流,不需采集更多的信号,对传感器等硬件的要求小,实现简单,对系统影响小,成本低。在电机生产线的质量控制、汽车与航空与航天器中电机的在线状态监测方面具有实用性,会有广泛的应用前景。
Permanent-magnetic DC motors are widely used in automobile and military or civil airplanes due to their lots of merits such as simple configuration, reliable running, small size, light weight, high efficiency, etc. To ensure these motors work safely, this paper studies a simple and efficient method to monitor the condition of permanent-magnetic DC motor and diagnose the possible online faults intellectualized.
In order to meet the requirement of real-time and simplicity in online faults detection,a universal diagnosing method was given to detect the typical faults of permanent-magnetic DC motor based on current signal analyzing and processing. Several signal analysis methods were synthetically used to extract the characteristic of failure from the armature current which is easy to be detected. Then the fault pattern recognization was made by artificial intelligence method.
After analyzing faults mechanisms and the changing characteristic of dynamic or stable current in cases of both failure and no failure, mathematical model of faults diagnosing based on armature current was created. Parameters: average ia v of stable currenti , pulsant frequency f , pulsant range is td, peak amplitude im of starting current and its gradient k near the peak, were defined as characteristics for faults diagnosis of permanent-magnetic DC motor. Differences of the five partners in case of failure and no failure made up the characteristic vector Te = [ΔfΔi_(av)Δi_(std)Δi_mΔk ].
Several failed experiments in no loading or loading condition are done on motors with four common faults in production or application: open circuit of components, brush wear, short circuit between coils and loose weld coils. And their fault mechanism was also analyzed in theory. The consistency between experimental results and the failed mechanism analysis shows that this method is feasible and universal to diagnose the faults of permanent-magnetic DC motor.
Pattern recognition is the key to realize fault intelectual diagnosis. The application of Expert System in field of fault diagnosis realized fault diagnosis intellectually. Due to the decentralization, randomicity and fuzziness of the fault character of permanent-magnetic DC motor, it is hard to get complete and efficient knowledge for creating the expert system for fault diagnosis of permanent-magnetic DC motor. The main approach to solve problem of knowledge acquisition is Machine Learning.The Statistical Pattern Recognition and the Artificial Neural Network are the basic means which are most widely used in fault diagnosis based on machine learning at present. But the theoretical basis of Statistical Pattern Recognition and Artificial Neural Network is traditional statistics. This kind of sort algorithm is valid only in the case of the number of trained sample being infinity and the results from this kind of sort algorithm is invalid when the number of trained sample is small. However, it is difficulty to gain a great deal of representative fault samples because it is impossible to let the motor long running with failure in engineering application especially used in airplanes.
Fault diagnosis method based on SVM (Support Vector Machine) is developed for permanent-magnetic DC motor according to the mathematical model of SVM. The fault diagnosis results of this method in cases where only limited training samples are available is compared with that of another classification algorithm BP ANN. It shows that SVM have better performance than ANN both in training speed and recognition rate. SVM can also avoid over-fitting and trapping in local extreme which often happened in the neural networks algorithm, especially in case of limited trained samples.
To overcome the problems existing in the online fault diagnosis of permanent-magnetic DC motor, such as non-symmetry of dataset, different loss by misjudgments and interference of noisy or outliers, the recognition algorithms of SVM is improved in following two ways. Firstly, a weighted support vector machine algorithm is developed through weighting error punishing factor of SVM. Both results of several experiments and analysis in theory show that this weighted support vector machines improve classification accuracy for class with small size, and reduce the different loss by misjudgments in fault diagnosis. Secondly, an improved support vector machine algorithm based on fuzzy C-means is proposed. The online data are clustered by the fuzzy C-means and the outliers are recognized according to the membership grade calculated from the fuzzy C-means. And then the data which removed the outliers are trained and tested by the support vector machine algorithm above mentioned. The results from experiments shows that support vector machine algorithm based on fuzzy C-means have better tolerance of noise and anti-noise performance. It enhances fault recognition precision in the complex case and extends the application field of fault diagnosis method based on SVM.
This method needs no more messages than measured voltage and current of armature. Therefore, the advantages are lower request for hardware like sensor, more simple realization, less influence on system and lower cost. It is practical and of good perspective in online monitoring online the condition of a working permanent-magnetic DC motor in an automobile or airplane or space probe, and in controlling the quality of motor manufacture line.
在分析了永磁直流电动机的故障机理和电机动、稳态电流在正常与故障状态下的变化特性的基础上,建立了基于电枢电流分析的永磁直流电机故障诊断的数学模型。确立稳态电流i的均值ia v、稳态电枢电流频谱谱峰对应的脉动频率f及其脉动幅度i std、电机起动电流的峰值im和峰值点附近的下降速率k这五个量确立为永磁直流电机故障诊断的特征参数,并将它们相对于正常状态的变化量组成故障特征向量: Te = [ΔfΔi_(av)Δi_(std)Δi_mΔk ]。
以数台永磁直流电机为实验样本,对其常见的元件开路、匝间短路、电刷磨损,绕组脱焊四种故障进行空、负载电枢电流故障特征提取及故障机理分析,实验结果与故障机理分析的一致性表明:本文提出的基于电流分析的永磁直流电机故障诊断方法对永磁直流电机的常见故障的诊断具有通用性。
故障模式识别是实现故障智能诊断的关键环节。专家系统在故障诊断领域中的应用,实现了基于人类专家经验知识的故障模式智能识别技术。但由于永磁直流电机故障特征参数的分散性、随机性和模糊性较大,获取有效的永磁直流电机的诊断知识成为建造故障模式识别专家系统的关键问题之一。机器学习是解决知识获取问题的主要途径。目前应用较为广泛的基于机器学习的故障模式识别方法主要有统计模式识别方法和人工神经网络方法。但统计模式识别方法和人工神经网络方法都是建立在传统的统计学基础之上的分类算法,即只有在足够多的样本前提下,其算法才是合理的,而当训练的是小样本数据集时,就不宜再沿用该分类算法。工程应用中的永磁直流电机尤其航空航天应用领域中的电机不可能带故障长期运行,故难以获得大量典型故障样本。这就在一定程度上制约了统计模式识别和人工神经网络方法在该领域中的应用。
本文以支持向量机算法的数学模型为理论依据,构建了一种基于支持向量机的永磁直流电机故障模式识别分类器,对永磁直流电机的数种故障模式进行识别,并对其和应用BP神经网络识别方法在小样本故障模式识别中的应用进行了对比实验研究。结果表明:基于支持向量机的故障模式识别方法在小样本情况下的诊断精度要高于同样样本情况下的BP神经网络,且避免了基于BP神经网络方法中存在的过学习问题和陷入局部极小值问题。
针对永磁直流电机故障在线诊断中存在类样本数目不平衡、误判损失不等、在线样本数据缺少类别标识以及复杂环境中样本数据存在大量噪声野点等问题,本文对基于支持向量机的永磁直流电机故障模式识别算法作了如下两方面的改进:首先,通过对支持向量机数学模型中的误差惩罚因子进行加权,构建了一种基于加权支持向量机的永磁直流电机故障模式识别算法。理论分析和实验结果表明:该算法可以提高小样本类(故障样本类)诊断精度,降低误判损失。其次,采用了一种基于模糊C均值的聚类算法,对无类别标识的在线数据进行模糊聚类,并根据模糊聚类的隶属度,来判断每个样本数据的所属类,由此同时定位数据中的野点,消除野点后,再对消噪后的数据运用基于支持向量机的模式识别算法进行训练与测试。提高了永磁直流电机复杂运行工况下在线样本数据的故障识别精度,扩充了基于支持向量机的故障模式识别算法的应用领域。
本方法只需要监测电机电枢电压与电流,不需采集更多的信号,对传感器等硬件的要求小,实现简单,对系统影响小,成本低。在电机生产线的质量控制、汽车与航空与航天器中电机的在线状态监测方面具有实用性,会有广泛的应用前景。
Permanent-magnetic DC motors are widely used in automobile and military or civil airplanes due to their lots of merits such as simple configuration, reliable running, small size, light weight, high efficiency, etc. To ensure these motors work safely, this paper studies a simple and efficient method to monitor the condition of permanent-magnetic DC motor and diagnose the possible online faults intellectualized.
In order to meet the requirement of real-time and simplicity in online faults detection,a universal diagnosing method was given to detect the typical faults of permanent-magnetic DC motor based on current signal analyzing and processing. Several signal analysis methods were synthetically used to extract the characteristic of failure from the armature current which is easy to be detected. Then the fault pattern recognization was made by artificial intelligence method.
After analyzing faults mechanisms and the changing characteristic of dynamic or stable current in cases of both failure and no failure, mathematical model of faults diagnosing based on armature current was created. Parameters: average ia v of stable currenti , pulsant frequency f , pulsant range is td, peak amplitude im of starting current and its gradient k near the peak, were defined as characteristics for faults diagnosis of permanent-magnetic DC motor. Differences of the five partners in case of failure and no failure made up the characteristic vector Te = [ΔfΔi_(av)Δi_(std)Δi_mΔk ].
Several failed experiments in no loading or loading condition are done on motors with four common faults in production or application: open circuit of components, brush wear, short circuit between coils and loose weld coils. And their fault mechanism was also analyzed in theory. The consistency between experimental results and the failed mechanism analysis shows that this method is feasible and universal to diagnose the faults of permanent-magnetic DC motor.
Pattern recognition is the key to realize fault intelectual diagnosis. The application of Expert System in field of fault diagnosis realized fault diagnosis intellectually. Due to the decentralization, randomicity and fuzziness of the fault character of permanent-magnetic DC motor, it is hard to get complete and efficient knowledge for creating the expert system for fault diagnosis of permanent-magnetic DC motor. The main approach to solve problem of knowledge acquisition is Machine Learning.The Statistical Pattern Recognition and the Artificial Neural Network are the basic means which are most widely used in fault diagnosis based on machine learning at present. But the theoretical basis of Statistical Pattern Recognition and Artificial Neural Network is traditional statistics. This kind of sort algorithm is valid only in the case of the number of trained sample being infinity and the results from this kind of sort algorithm is invalid when the number of trained sample is small. However, it is difficulty to gain a great deal of representative fault samples because it is impossible to let the motor long running with failure in engineering application especially used in airplanes.
Fault diagnosis method based on SVM (Support Vector Machine) is developed for permanent-magnetic DC motor according to the mathematical model of SVM. The fault diagnosis results of this method in cases where only limited training samples are available is compared with that of another classification algorithm BP ANN. It shows that SVM have better performance than ANN both in training speed and recognition rate. SVM can also avoid over-fitting and trapping in local extreme which often happened in the neural networks algorithm, especially in case of limited trained samples.
To overcome the problems existing in the online fault diagnosis of permanent-magnetic DC motor, such as non-symmetry of dataset, different loss by misjudgments and interference of noisy or outliers, the recognition algorithms of SVM is improved in following two ways. Firstly, a weighted support vector machine algorithm is developed through weighting error punishing factor of SVM. Both results of several experiments and analysis in theory show that this weighted support vector machines improve classification accuracy for class with small size, and reduce the different loss by misjudgments in fault diagnosis. Secondly, an improved support vector machine algorithm based on fuzzy C-means is proposed. The online data are clustered by the fuzzy C-means and the outliers are recognized according to the membership grade calculated from the fuzzy C-means. And then the data which removed the outliers are trained and tested by the support vector machine algorithm above mentioned. The results from experiments shows that support vector machine algorithm based on fuzzy C-means have better tolerance of noise and anti-noise performance. It enhances fault recognition precision in the complex case and extends the application field of fault diagnosis method based on SVM.
This method needs no more messages than measured voltage and current of armature. Therefore, the advantages are lower request for hardware like sensor, more simple realization, less influence on system and lower cost. It is practical and of good perspective in online monitoring online the condition of a working permanent-magnetic DC motor in an automobile or airplane or space probe, and in controlling the quality of motor manufacture line.
引文
1 D.Filbert. Advanced Fault Diagnosis for the Mass Production of Small Power Electric Motros. ELECTROMOTION. 1995, 11(2):159~166
2 吕锋,王秀青. 电机设备故障诊断技术的新进展. 上海海运学院学报. 2001, 22 (3): 1~4
3 Y.S.Tarng, B.Y. Lee. Amplitude Demodulation of the Induction Motor Current for the Tool Breakage Detection in Drilling Operation. Robotics and Computer-Integrated Manufacturing. 1999, 15(4): 313~318
4 Devaney, J. E.Michael, Levent. Detecting Motor Bearing Faults: Monitoring an Induction Motor's Current and Detecting Bearing Failure. IEEE Instrumentation and Measurement Magazine. 2004, 7(4): 30~35
5 X.H.Wang, S.L.Chen, W.J.Wang. A Study of Armature Internal Faults for Turbo-generators. IEEE Transactions on Industry Application. 2002, 38(3): 626~631
6 Muller, H.Grant, Landy, F.Charles. A Novel Method to Detect Broken Rotor Bars in Squirrel Cage Induction Motors when Interbar Currents Are Present. IEEE Transactions on Energy Conversion. 2003, 18(1): 71~79
7 Bellini, Alberto, Filippetti, Fiorenzo. On-field Experience with Online Diagnosis of Large Induction Motors Cage Failures Using MCSA. IEEE Transactions on Industry Applications. 2002, 38(4): 1045~1053
8 Thomson, W.T.Fenger. Current Signature Analysis to Detect Induction Motor Faults. Industry Applications Magazine. 2001, 7(4): 26~34
9 X.G.Yin, O.P.Malik, G.S.Hope. Adaptive Ground Fault Protection Schemes for Turbo-generator Based on Third Harmonic Voltages. IEEE Transactions on Power Delivery. 1990, 5(2): 595~603
10 J.W.Pope. A Comparison of 100% Stator Ground Fault Protection Schemes for Generator Stator Windings. IEEE Transaction on Power Apparatus and Systems. 1994, 103(4):832~840
11 G.M.Joksimovic, J.Penman. The Detection of Inter-turn Short Circuits in the Stator Windings of Operating Motors. IEEE Transactions on Industrial Electronics. 2000, 47(5): 1078~1084
12 A.J.M.Cardoso, S.M.A.Cruz, D.S.B.Foneca. Inter-'hun Stator Winding Fault Diagnosis inThree Phase Induction Motors by Park's Vector Conversion. IEEETransaction on Energy. 1999, 14(3): 595~598
13 M.A.Cruz, A.J.Stator. Fault Diagnosis in Three-Phase Synchronous and Asynchronous by the Extended Park's Vector. IEEE Transactions on Industry Applications. 2001, 37(5): 1227~1233
14 T.William, C.Thomson, M.Fenger. Current Signature Analysis to Detect Induction Motor Faults. IEEE Transactions on Industry Applications. 2001, 12(8): 26~34
15 N.Arthur, J.Penman. Induction Machine Condition Monitoring with Higher Order Spectra. IEEE Transactions on Industrial Electronics. 2000, 47(5): 1031~1041
16 J.Milimonfared, H.M.Kelk, S.Nadi. A Novel Approach for Broken-rotor-bar Detection in Cage Induction Motors. IEEE Tranactions on Industry Applications. 1999, 35(5): 1000~1006
17 Zanardelli, Wesley. Wavelet-based Methods for the Prognosis of Mechanical and Electrical Failures in Electric Motors. Mechanical Systems and Signal Processing. 2005, 19(2): 411~426
18 P.N.Dobrodeyev, S.A.Volokhov, A.V.Kildishev. Method for Detection of Broken Bars in Induction Motors. IEEE Tranactions on Magnetics. 2000, 36(5): 3608~3610
19 Chattopadhyay, P. Bhaduri; D.Ghosh, D.Dasgupta. Motor Fault Detection - A Novel Neural Network Approach Modelling. Measurement and Control. 2002, 75(1): 35~46
20 R.Ong, J.H.Dymond, R.D.Findlay. Bearing Damage Analysis in a Large Oil-ring-lubricated Induction Machine. IEEE Tranactions on Industrial Electronics. 2000, 47(5): 1085~1089
21 W.T.Thomson, M.Fenger. Current Signature Analysis to Detect Induction Motor Faults. IEE Industry Applications Magazine. 2001, 7 (4): 26 ~34
22 K.George, G.Dimitrios, K.Nick. Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets. IEEE Transactions on Knowledge and Data Engineering. 2003, 15 (5): 1170~1187
23 G.Riter, M.Gallegos. Outliers in Statistical Pattern Recognition and an Application to Automatic Chromosome Classififcation. Pattern Recognition Letters. 1997, 18(4): 525~539
24 M. Burth, D.Filbert, C.Guehmann, K.Roepke, C.Schneider. Non-linear Approachto the Fault Diagnosis of Low Power Motors. Engineering Simulation. 1996, 13(6): 1049~1060
25 C. Guhmann, D.Filbert. Fault Diagnosis of Electric Low-power Motors by Analyzing the Current Signal. IFAC; IMACS. IFAC/IMACS Symposium on Fault Detection, Supervision and Safety for Technical Processes, Baden-Baden, Ger, 1991. Tarrytown, NY, USA, Publ by Pergamon Press Inc, 1992: 141~146
26 M. Burth, K.Roepke, D.Filbert. Diagnosis of Universal Motors by Current and Vibration Signal Analysis (part 1). Technisches Messen TM. 1997, 64(1): 3~10
27 M. Burth, K.Roepke, D.Filbert. Diagnosis of Universal Motors by Current and Vibration Signal Analysis (part 2). Technisches Messen TM. 1997, 64(2): 47~53
28 陈松林. 汽轮发电机定子绕组内部故障规律和保护方案的研究. 电力系统自动化. 2000, 24(4): 42~45
29 孙宇光. 凸极发电机定子绕组内部故障的瞬态计算及有关保护方案的分析.电工技术学报. 2001, 16(1): 1~8
30 刘立生, 邱阿瑞. 希尔伯特变换在电机故障诊断中的应用. 电工电能新技术. 1999, 18(2): 33~36
31 邱阿瑞, 孙建. 电机故障模式识别与诊断. 清华大学学报. 1999,39(3):72~74
32 任震,何建军,黄雯莹. 基于小波包算法的电机故障信号的压缩和重构.中国电机工程学报. 2001, 21(1): 25~29
33 黄群古,任震,黄雯莹. 两种小波变换方法及其在电机故障信号分析中应用.中国电机工程学报. 2000, 20(10): 59~63
34 黄群古,任震,黄雯莹. 梯形小波变换及其在鼠笼电机转子故障分析中的应用. 中国电机工程学报. 2001, 21(6): 29~32
35 任震. 基于实值小波变换的发电机故障检测方法. 中国电机工程学报. 2000, 20(6): 58~64
36 刘振兴,尹项根,张哲. 基于瞬时功率信号频谱分析的鼠笼式异步电机转子故障在线诊断方法. 中国电机工程学报. 2003, 23(10): 148~152
37 曹志彤,何国光,陈宏平. 电机故障特征值的倍频小波分析. 中国电机工程学报. 2003, 23(7): 112~116
38 许伯强,李和明,孙丽玲. 小波分析应用于鼠笼式异步电机转子断条在线检测初探. 中国电机工程学报. 2001, 21(11): 24~28
39 马宏忠, 胡虔生, 黄允凯,王惠庆. 感应电机转子断条根数影响分析. 河海大学学报. 2003, 31(5): 573~576
40 马宏忠, 胡虔生, 黄允凯, 王惠庆. 感应电机转子绕组故障仿真与实验研究. 中国电机工程学报. 2003, 23 (4): 107~112
41 马宏忠,李训铭, 方瑞明, 黄允凯, 胡虔生. 利用失电残余电压诊断异步电机转子绕组故障. 中国电机工程学报. 2004, 24(7): 183~187
42 李桂丹,王晓元,陈益广等. 异步电机转子故障在线监测方法. 微电机. 2003, 36(3): 55~57
43 X.Q.Liu, H.Y.Zhang, J.Liu. Fault Detection and Diagnosis of Permanent–magnetic DC Motor Based on Parameter Estimation and Neural Network. IEEE Transactions on Industrial Electronics. 2000, 47(10): 1021~1030
44 刘向群,杨静,刘军. 基于参数估计和人工神经网络的永磁直流电机故障检测与诊断. 微特电机. 1999, 27(6): 8~12
45 仇越,刘向群,张洪钺. 基与频谱法的航空起动发电电机的故障检测与诊断. 北京航空航天大学学报. 2005, 30(5): 483~487
46 刘向群,仇越,张洪钺. 基于频谱法与神经网络的航空起动发电机的故障检测与诊断. 航空学报. 2004, 25(2): 158~162
47 沈善德. 电力系统辨识. 北京:清华大学出版社,1993: 11~253
48 R.Isermann. Process Fault Detection Based on Modeling and Estimation Methods. Automatica. 1984, 20(4): 387~404
49 陈循, 田江红, 温熙森等. 阶比谱分析与汽车启动电机故障的实时诊断. 国防科技大学学报. 1996, 18(4): 44~48
50 李涌, 韩崇昭,徐为群等. 非线性频谱分析在故障诊断中的应用. 西安交通大学学报. 2000, 21(1): 79~81
51 岳国良,万粟, 张健忠等. 异步电机鼠笼转子断条的频谱分析及诊断. 河北电力技术.1999, 18(5): 19~22
52 王凤昌, 李面换. 异步电机故障的电流监测和诊断. 哈尔滨工业大学学报. 1994, 26(6): 88~93
53 M.Hamid, E.Mohamed, B.Hachemi. Monitoring and Diagnosisof Induction Motors Electrical Faults Using a Current Park’s Vector Pattern Learning Approach. IEEE Tranactions on Industry Applications. 2000, 36 (3): 730~735
54 M.E.H.Benbouzid, M.Vieira, C.Theys. Induction Motors' Faults Detection and Localization Using Stator Current Advanced Signal Processing Techniques.IEEE Transactions on Power Electronics. 1999, 14 (1): 14~22
55 D.R.Rankin. Industrial Application of Phase Current Analysis to Detect Rotor Winding Faults in Squirrel Cage Induction Motors. Power Engineering Journal.1995, 9(2): 77~84
56 曹志彤,陈宏平,何国光. 基于小波重构的异步电机故障诊断. 电工技术学报.2002, 17(7): 80~83
57 许伯强,李和明,孙丽玲. 小波包分析的笼型异步电机转子断条在线检测方法.中小型电机. 2001, 28(6): 47~51
58 杨叔子,丁洪,史铁林等. 基于知识的诊断推理.北京:清华大学出版社,1994: 3~7
59 杨洪耕,刘守亮,肖先勇. 基于 S 变换的电压凹陷分类专家系统. 中国电机工程学报. 2007, 27(1): 98~104
60 马竹梧,沈标正,于洁. 专家系统开发工具及其在电机故障诊断中应用. 电工技术杂志.1999, 31(1): 26~28
61 M.DONG,Y. Zhang,Y. Li,M.D.Judd. An Evidential Reasoning Approach to Transformer Fault Diagnosis. Proceedings of the CSEE. 2006, 26(1): 106~114
62 颜波,张铁,谢存禧.电机自动装配线故障诊断模糊专家系统. 计算机工程. 2002, 28(11): 195~96
63 陈刚,李冰,陈非,黄树红. 大型火电机组引风机的状态检修. 华中科技大学学报. 2007, 35(1): 63~66
64 S. Michalski, L.Chilausky. Learning by Being Told and Learning from Examples: An Experimental Comparison of Two Methods of Knowledge Acquisition in Context of Developing on Expert System for Soybean Disease Diagnosis. Inter-national Journal of Policy Analysis and Information Systems. 1980, 4 (2): 125~160
65 K.Kyusung, P.G.Alexander. Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing. IEEE Transactions on Mechatronics. 2002, 12(7): 201~219
66 唐任远. 现代永磁电机理论与设计. 北京:机械工业出版社,1997: 140~141 94~95 140~141
67 张文海. 直流电机转速的特殊测试方法. 电工技术杂志. 1997, 29(3): 40~42
68 陈雅文. 直流电机测速新方法. 微电机. 1999, 32(1): 36~37
69 黄进,黄建华. 基于小波分析的电机转矩转速特性测试. 中小型电机. 2001, 28(2): 49~52
70 S.M.Cui, F.Chai, H.X.Wu. Virtual Test System for Permanent-magnet DC Motor. Journal of Harbin Institute of Technology. 2003, 35(2): 13~20
71 刘曼兰,张千帆,崔淑梅. 直流电机转速与电流换向脉动频率关系新表达. 微电机. 2006, 40(5): 10~12
72 杨 琳, 崔淑梅, 刘曼兰. 利用电流脉动测试直流电机转速存在问题的分析. 微电机. 2006, 40(4): 82~85
73 牛发亮,黄 进,杨家强. 基于电磁转矩小波变换的感应电机转子断条故障诊断. 中国电机工程学报. 2005, 25(24): 122~126
74 于志伟,苏宝库,曾 鸣. 小波包分析技术在大型电机转子故障诊断系统中的应用. 中国电机工程学报. 2005, 25(22): 158~162
75 B.A.Pearlinutter. Gradient Calculations for Dynamic Recurrent Neural Networks: a Survey. IEEE Tranactions on Neural Networks. 1995, 6(5): 1212~1228
76 X.Li, G.R.Chen, Z.Q.Chen. Chaotifyinglinear Elman networks.IEEE Tranactions on Neural Networks. 2002, 13(5): 1193~1199
77 冷 飞. BP 网络及其应用研究. 中国现代教育装备. 2006, 46(12): 53~54
78 P.Vapnik. 统计学习理论的本质. 张学工. 北京:清华大学出版社, 2000: 1~7
79 潘昊,陈杰,钟珞. BP 神经网络结构与样本训练参数选取的初步探讨. 湖北工学院学报.1997, 12(3): 1~4
80 C.J.C.Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 1998, 2 (2): 121~126
81 B.Samanta, K.R.Balushi, S.A.Araimi. Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection. Engineering Applications of Artificial Intelligence. 2003, 16 (7): 657~665
82 L.B.Jack, A.K.Nandi. Support Vector Machines for Detection and Characterization of Rolling Element Bearing Faults. Journal of Mechanical Engineering Science. 2001, 215 (9): 1065~1074
83 K.Brudzewski, S.Osowski, T.Markiewicz. Classification of Milk by Means of an Electronic Nose and SVM Neural Network. Sensors and Actuators B. 2004, 98 (6): 291~ 298
84 C.WU, H.O.Janming. Travel Time Prediction with Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems. 2004, 5 (4): 276~281
85 邓乃扬 田英杰. 数据挖掘中的新方法—支持向量机. 北京:科学出版社,2004: 166~196 152~154 71~77 1~34
86 S.SKeerthi. Efficient Tuning of SVM Hyper Parameters Using Radius or Margin Bound and Iterative Algorithms. IEEE Tranactions on Neural Networks. 2002, 13 (5): 1225~1229
87 J.H.Chiang, P.Y.Hao. A New Kernel Based Fuzzy Clustering Approach: Support Vector Clustering with Cell Growing. IEEE Transactions on Fuzzy Systems. 2003, 11 (4): 518~527
88 C.W.Hsu, C.J.Lin. A Comparison of Methods for Multi-class Support Vector Machines. IEEE Tranactions on Neural Networks. 2002, 13(2): 415~425
89 O.Chapelle, P.Vapnik, O.Bousquet. Choosing Multiple Parameters for Support Vector Machines. Machine Learning. 2002, 46(9): 131~159
90 董春曦,饶鲜,杨绍全. 支持向量机参数选择方法研究. 系统工程与电子技术. 2004, 26(8): 1117~1120
91 郑小霞,钱峰. 高斯核支持向量机分类和模型参数选择研究. 计算机工程与应用.2006, 42(1): 77~79
92 翟永杰, 韩 璞, 王东风, 王国鹏. 基于损失函数的 SVM 算法及其在轻微故障诊断中的应用. 中国电机工程学报. 2003, 23(9): 199~201
93 H.P.Huang, Y.H.Liu. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. Internation Journal of Fuzzy Systems. 2002, 4 (3): 826~835
94 C.F.Lin, S.D.Wan. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks. 2002, 13 (2): 464~471
95 王 晔, 黄上腾. 基于间隔区域样本数量的加权支持向量机. 计算机工程学报. 2006, 32(6): 31~33
96 张 翔,肖小玲,徐光祐. 基于样本之间紧密度的模糊支持向量机方法. 软件学报. 2006, 17(5): 952~956
97 鹿卫国,戴亚平,涂序彦,高峰. 适用于加权样本集处理的加权支持向量机方法. 北京理工大学学报. 2005, 25(3): 211~213
98 燕继坤,王勇,曹春霞,郑辉. 样本错误加权的支持向量数据描述. 计算机工程. 2005, 31(2): 24~26
99 J.C.Bezdek. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithm. IEEE Tranactions PAMI. 1980, 2(1): 1~8
100 于剑,程乾生. 关于FCM算法中的权重指数m的一点注记. 电子学报. 2003,31(3): 478~480
101 宫改云,高新波,伍忠东. 聚类算法中模糊加权指数 m 的优选方法. 模糊系统与数学. 2005, 19(l): 143~1480
102 高新波,裴继红,谢维信. 模糊 C 均值算法中的加权指数 m 的研究. 电子学报. 2000, 28(4): 80~83
103 刘宜平,沈毅力等. 一种参数 m 在线优化的 FCM 改进算法. 电机与控制学报. 2000, 4(3):164~167
104 诸克军,苏顺华,黎金玲.模糊 C—均值中的最优聚类与最佳聚类数.系统工程理论与实践. 2005, 3(3): 54~56
105 L.O.Hall, B.Ozyurt, J.C.Bezdek. Clustering with a Genetically Optimized Approach. IEEE Tranactions on Evolutionary Computation. 1999, 3(2):103~112.
106 A.O.Boudraa. Dynamic Estimation of Number of Clusters in Data Sets. Elect ronics Letters. 1999, 35 (19): 1606~1608
107 S.H.Kwon. Cluster Validity Index for Fuzzy Clustering. Electronics Letters. 1998, 34 (22): 2176~2177
108 X.L.Xie, G.Beni. A Validity Method for Fuzzy Clustering. IEEE Tranactions on PAMI. 1991, 13 (8): 841~847
109 N.R.Pal, J.C.Bezdk. On Cluster Validity for the Fuzzy C-mean Model. IEEE Tranactions on Fuzzy Systems. 1995, 3(3): 370~379
2 吕锋,王秀青. 电机设备故障诊断技术的新进展. 上海海运学院学报. 2001, 22 (3): 1~4
3 Y.S.Tarng, B.Y. Lee. Amplitude Demodulation of the Induction Motor Current for the Tool Breakage Detection in Drilling Operation. Robotics and Computer-Integrated Manufacturing. 1999, 15(4): 313~318
4 Devaney, J. E.Michael, Levent. Detecting Motor Bearing Faults: Monitoring an Induction Motor's Current and Detecting Bearing Failure. IEEE Instrumentation and Measurement Magazine. 2004, 7(4): 30~35
5 X.H.Wang, S.L.Chen, W.J.Wang. A Study of Armature Internal Faults for Turbo-generators. IEEE Transactions on Industry Application. 2002, 38(3): 626~631
6 Muller, H.Grant, Landy, F.Charles. A Novel Method to Detect Broken Rotor Bars in Squirrel Cage Induction Motors when Interbar Currents Are Present. IEEE Transactions on Energy Conversion. 2003, 18(1): 71~79
7 Bellini, Alberto, Filippetti, Fiorenzo. On-field Experience with Online Diagnosis of Large Induction Motors Cage Failures Using MCSA. IEEE Transactions on Industry Applications. 2002, 38(4): 1045~1053
8 Thomson, W.T.Fenger. Current Signature Analysis to Detect Induction Motor Faults. Industry Applications Magazine. 2001, 7(4): 26~34
9 X.G.Yin, O.P.Malik, G.S.Hope. Adaptive Ground Fault Protection Schemes for Turbo-generator Based on Third Harmonic Voltages. IEEE Transactions on Power Delivery. 1990, 5(2): 595~603
10 J.W.Pope. A Comparison of 100% Stator Ground Fault Protection Schemes for Generator Stator Windings. IEEE Transaction on Power Apparatus and Systems. 1994, 103(4):832~840
11 G.M.Joksimovic, J.Penman. The Detection of Inter-turn Short Circuits in the Stator Windings of Operating Motors. IEEE Transactions on Industrial Electronics. 2000, 47(5): 1078~1084
12 A.J.M.Cardoso, S.M.A.Cruz, D.S.B.Foneca. Inter-'hun Stator Winding Fault Diagnosis inThree Phase Induction Motors by Park's Vector Conversion. IEEETransaction on Energy. 1999, 14(3): 595~598
13 M.A.Cruz, A.J.Stator. Fault Diagnosis in Three-Phase Synchronous and Asynchronous by the Extended Park's Vector. IEEE Transactions on Industry Applications. 2001, 37(5): 1227~1233
14 T.William, C.Thomson, M.Fenger. Current Signature Analysis to Detect Induction Motor Faults. IEEE Transactions on Industry Applications. 2001, 12(8): 26~34
15 N.Arthur, J.Penman. Induction Machine Condition Monitoring with Higher Order Spectra. IEEE Transactions on Industrial Electronics. 2000, 47(5): 1031~1041
16 J.Milimonfared, H.M.Kelk, S.Nadi. A Novel Approach for Broken-rotor-bar Detection in Cage Induction Motors. IEEE Tranactions on Industry Applications. 1999, 35(5): 1000~1006
17 Zanardelli, Wesley. Wavelet-based Methods for the Prognosis of Mechanical and Electrical Failures in Electric Motors. Mechanical Systems and Signal Processing. 2005, 19(2): 411~426
18 P.N.Dobrodeyev, S.A.Volokhov, A.V.Kildishev. Method for Detection of Broken Bars in Induction Motors. IEEE Tranactions on Magnetics. 2000, 36(5): 3608~3610
19 Chattopadhyay, P. Bhaduri; D.Ghosh, D.Dasgupta. Motor Fault Detection - A Novel Neural Network Approach Modelling. Measurement and Control. 2002, 75(1): 35~46
20 R.Ong, J.H.Dymond, R.D.Findlay. Bearing Damage Analysis in a Large Oil-ring-lubricated Induction Machine. IEEE Tranactions on Industrial Electronics. 2000, 47(5): 1085~1089
21 W.T.Thomson, M.Fenger. Current Signature Analysis to Detect Induction Motor Faults. IEE Industry Applications Magazine. 2001, 7 (4): 26 ~34
22 K.George, G.Dimitrios, K.Nick. Efficient Biased Sampling for Approximate Clustering and Outlier Detection in Large Data Sets. IEEE Transactions on Knowledge and Data Engineering. 2003, 15 (5): 1170~1187
23 G.Riter, M.Gallegos. Outliers in Statistical Pattern Recognition and an Application to Automatic Chromosome Classififcation. Pattern Recognition Letters. 1997, 18(4): 525~539
24 M. Burth, D.Filbert, C.Guehmann, K.Roepke, C.Schneider. Non-linear Approachto the Fault Diagnosis of Low Power Motors. Engineering Simulation. 1996, 13(6): 1049~1060
25 C. Guhmann, D.Filbert. Fault Diagnosis of Electric Low-power Motors by Analyzing the Current Signal. IFAC; IMACS. IFAC/IMACS Symposium on Fault Detection, Supervision and Safety for Technical Processes, Baden-Baden, Ger, 1991. Tarrytown, NY, USA, Publ by Pergamon Press Inc, 1992: 141~146
26 M. Burth, K.Roepke, D.Filbert. Diagnosis of Universal Motors by Current and Vibration Signal Analysis (part 1). Technisches Messen TM. 1997, 64(1): 3~10
27 M. Burth, K.Roepke, D.Filbert. Diagnosis of Universal Motors by Current and Vibration Signal Analysis (part 2). Technisches Messen TM. 1997, 64(2): 47~53
28 陈松林. 汽轮发电机定子绕组内部故障规律和保护方案的研究. 电力系统自动化. 2000, 24(4): 42~45
29 孙宇光. 凸极发电机定子绕组内部故障的瞬态计算及有关保护方案的分析.电工技术学报. 2001, 16(1): 1~8
30 刘立生, 邱阿瑞. 希尔伯特变换在电机故障诊断中的应用. 电工电能新技术. 1999, 18(2): 33~36
31 邱阿瑞, 孙建. 电机故障模式识别与诊断. 清华大学学报. 1999,39(3):72~74
32 任震,何建军,黄雯莹. 基于小波包算法的电机故障信号的压缩和重构.中国电机工程学报. 2001, 21(1): 25~29
33 黄群古,任震,黄雯莹. 两种小波变换方法及其在电机故障信号分析中应用.中国电机工程学报. 2000, 20(10): 59~63
34 黄群古,任震,黄雯莹. 梯形小波变换及其在鼠笼电机转子故障分析中的应用. 中国电机工程学报. 2001, 21(6): 29~32
35 任震. 基于实值小波变换的发电机故障检测方法. 中国电机工程学报. 2000, 20(6): 58~64
36 刘振兴,尹项根,张哲. 基于瞬时功率信号频谱分析的鼠笼式异步电机转子故障在线诊断方法. 中国电机工程学报. 2003, 23(10): 148~152
37 曹志彤,何国光,陈宏平. 电机故障特征值的倍频小波分析. 中国电机工程学报. 2003, 23(7): 112~116
38 许伯强,李和明,孙丽玲. 小波分析应用于鼠笼式异步电机转子断条在线检测初探. 中国电机工程学报. 2001, 21(11): 24~28
39 马宏忠, 胡虔生, 黄允凯,王惠庆. 感应电机转子断条根数影响分析. 河海大学学报. 2003, 31(5): 573~576
40 马宏忠, 胡虔生, 黄允凯, 王惠庆. 感应电机转子绕组故障仿真与实验研究. 中国电机工程学报. 2003, 23 (4): 107~112
41 马宏忠,李训铭, 方瑞明, 黄允凯, 胡虔生. 利用失电残余电压诊断异步电机转子绕组故障. 中国电机工程学报. 2004, 24(7): 183~187
42 李桂丹,王晓元,陈益广等. 异步电机转子故障在线监测方法. 微电机. 2003, 36(3): 55~57
43 X.Q.Liu, H.Y.Zhang, J.Liu. Fault Detection and Diagnosis of Permanent–magnetic DC Motor Based on Parameter Estimation and Neural Network. IEEE Transactions on Industrial Electronics. 2000, 47(10): 1021~1030
44 刘向群,杨静,刘军. 基于参数估计和人工神经网络的永磁直流电机故障检测与诊断. 微特电机. 1999, 27(6): 8~12
45 仇越,刘向群,张洪钺. 基与频谱法的航空起动发电电机的故障检测与诊断. 北京航空航天大学学报. 2005, 30(5): 483~487
46 刘向群,仇越,张洪钺. 基于频谱法与神经网络的航空起动发电机的故障检测与诊断. 航空学报. 2004, 25(2): 158~162
47 沈善德. 电力系统辨识. 北京:清华大学出版社,1993: 11~253
48 R.Isermann. Process Fault Detection Based on Modeling and Estimation Methods. Automatica. 1984, 20(4): 387~404
49 陈循, 田江红, 温熙森等. 阶比谱分析与汽车启动电机故障的实时诊断. 国防科技大学学报. 1996, 18(4): 44~48
50 李涌, 韩崇昭,徐为群等. 非线性频谱分析在故障诊断中的应用. 西安交通大学学报. 2000, 21(1): 79~81
51 岳国良,万粟, 张健忠等. 异步电机鼠笼转子断条的频谱分析及诊断. 河北电力技术.1999, 18(5): 19~22
52 王凤昌, 李面换. 异步电机故障的电流监测和诊断. 哈尔滨工业大学学报. 1994, 26(6): 88~93
53 M.Hamid, E.Mohamed, B.Hachemi. Monitoring and Diagnosisof Induction Motors Electrical Faults Using a Current Park’s Vector Pattern Learning Approach. IEEE Tranactions on Industry Applications. 2000, 36 (3): 730~735
54 M.E.H.Benbouzid, M.Vieira, C.Theys. Induction Motors' Faults Detection and Localization Using Stator Current Advanced Signal Processing Techniques.IEEE Transactions on Power Electronics. 1999, 14 (1): 14~22
55 D.R.Rankin. Industrial Application of Phase Current Analysis to Detect Rotor Winding Faults in Squirrel Cage Induction Motors. Power Engineering Journal.1995, 9(2): 77~84
56 曹志彤,陈宏平,何国光. 基于小波重构的异步电机故障诊断. 电工技术学报.2002, 17(7): 80~83
57 许伯强,李和明,孙丽玲. 小波包分析的笼型异步电机转子断条在线检测方法.中小型电机. 2001, 28(6): 47~51
58 杨叔子,丁洪,史铁林等. 基于知识的诊断推理.北京:清华大学出版社,1994: 3~7
59 杨洪耕,刘守亮,肖先勇. 基于 S 变换的电压凹陷分类专家系统. 中国电机工程学报. 2007, 27(1): 98~104
60 马竹梧,沈标正,于洁. 专家系统开发工具及其在电机故障诊断中应用. 电工技术杂志.1999, 31(1): 26~28
61 M.DONG,Y. Zhang,Y. Li,M.D.Judd. An Evidential Reasoning Approach to Transformer Fault Diagnosis. Proceedings of the CSEE. 2006, 26(1): 106~114
62 颜波,张铁,谢存禧.电机自动装配线故障诊断模糊专家系统. 计算机工程. 2002, 28(11): 195~96
63 陈刚,李冰,陈非,黄树红. 大型火电机组引风机的状态检修. 华中科技大学学报. 2007, 35(1): 63~66
64 S. Michalski, L.Chilausky. Learning by Being Told and Learning from Examples: An Experimental Comparison of Two Methods of Knowledge Acquisition in Context of Developing on Expert System for Soybean Disease Diagnosis. Inter-national Journal of Policy Analysis and Information Systems. 1980, 4 (2): 125~160
65 K.Kyusung, P.G.Alexander. Induction Motor Fault Diagnosis Based on Neuropredictors and Wavelet Signal Processing. IEEE Transactions on Mechatronics. 2002, 12(7): 201~219
66 唐任远. 现代永磁电机理论与设计. 北京:机械工业出版社,1997: 140~141 94~95 140~141
67 张文海. 直流电机转速的特殊测试方法. 电工技术杂志. 1997, 29(3): 40~42
68 陈雅文. 直流电机测速新方法. 微电机. 1999, 32(1): 36~37
69 黄进,黄建华. 基于小波分析的电机转矩转速特性测试. 中小型电机. 2001, 28(2): 49~52
70 S.M.Cui, F.Chai, H.X.Wu. Virtual Test System for Permanent-magnet DC Motor. Journal of Harbin Institute of Technology. 2003, 35(2): 13~20
71 刘曼兰,张千帆,崔淑梅. 直流电机转速与电流换向脉动频率关系新表达. 微电机. 2006, 40(5): 10~12
72 杨 琳, 崔淑梅, 刘曼兰. 利用电流脉动测试直流电机转速存在问题的分析. 微电机. 2006, 40(4): 82~85
73 牛发亮,黄 进,杨家强. 基于电磁转矩小波变换的感应电机转子断条故障诊断. 中国电机工程学报. 2005, 25(24): 122~126
74 于志伟,苏宝库,曾 鸣. 小波包分析技术在大型电机转子故障诊断系统中的应用. 中国电机工程学报. 2005, 25(22): 158~162
75 B.A.Pearlinutter. Gradient Calculations for Dynamic Recurrent Neural Networks: a Survey. IEEE Tranactions on Neural Networks. 1995, 6(5): 1212~1228
76 X.Li, G.R.Chen, Z.Q.Chen. Chaotifyinglinear Elman networks.IEEE Tranactions on Neural Networks. 2002, 13(5): 1193~1199
77 冷 飞. BP 网络及其应用研究. 中国现代教育装备. 2006, 46(12): 53~54
78 P.Vapnik. 统计学习理论的本质. 张学工. 北京:清华大学出版社, 2000: 1~7
79 潘昊,陈杰,钟珞. BP 神经网络结构与样本训练参数选取的初步探讨. 湖北工学院学报.1997, 12(3): 1~4
80 C.J.C.Burges. A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery. 1998, 2 (2): 121~126
81 B.Samanta, K.R.Balushi, S.A.Araimi. Artificial Neural Networks and Support Vector Machines with Genetic Algorithm for Bearing Fault Detection. Engineering Applications of Artificial Intelligence. 2003, 16 (7): 657~665
82 L.B.Jack, A.K.Nandi. Support Vector Machines for Detection and Characterization of Rolling Element Bearing Faults. Journal of Mechanical Engineering Science. 2001, 215 (9): 1065~1074
83 K.Brudzewski, S.Osowski, T.Markiewicz. Classification of Milk by Means of an Electronic Nose and SVM Neural Network. Sensors and Actuators B. 2004, 98 (6): 291~ 298
84 C.WU, H.O.Janming. Travel Time Prediction with Support Vector Regression. IEEE Transactions on Intelligent Transportation Systems. 2004, 5 (4): 276~281
85 邓乃扬 田英杰. 数据挖掘中的新方法—支持向量机. 北京:科学出版社,2004: 166~196 152~154 71~77 1~34
86 S.SKeerthi. Efficient Tuning of SVM Hyper Parameters Using Radius or Margin Bound and Iterative Algorithms. IEEE Tranactions on Neural Networks. 2002, 13 (5): 1225~1229
87 J.H.Chiang, P.Y.Hao. A New Kernel Based Fuzzy Clustering Approach: Support Vector Clustering with Cell Growing. IEEE Transactions on Fuzzy Systems. 2003, 11 (4): 518~527
88 C.W.Hsu, C.J.Lin. A Comparison of Methods for Multi-class Support Vector Machines. IEEE Tranactions on Neural Networks. 2002, 13(2): 415~425
89 O.Chapelle, P.Vapnik, O.Bousquet. Choosing Multiple Parameters for Support Vector Machines. Machine Learning. 2002, 46(9): 131~159
90 董春曦,饶鲜,杨绍全. 支持向量机参数选择方法研究. 系统工程与电子技术. 2004, 26(8): 1117~1120
91 郑小霞,钱峰. 高斯核支持向量机分类和模型参数选择研究. 计算机工程与应用.2006, 42(1): 77~79
92 翟永杰, 韩 璞, 王东风, 王国鹏. 基于损失函数的 SVM 算法及其在轻微故障诊断中的应用. 中国电机工程学报. 2003, 23(9): 199~201
93 H.P.Huang, Y.H.Liu. Fuzzy Support Vector Machines for Pattern Recognition and Data Mining. Internation Journal of Fuzzy Systems. 2002, 4 (3): 826~835
94 C.F.Lin, S.D.Wan. Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks. 2002, 13 (2): 464~471
95 王 晔, 黄上腾. 基于间隔区域样本数量的加权支持向量机. 计算机工程学报. 2006, 32(6): 31~33
96 张 翔,肖小玲,徐光祐. 基于样本之间紧密度的模糊支持向量机方法. 软件学报. 2006, 17(5): 952~956
97 鹿卫国,戴亚平,涂序彦,高峰. 适用于加权样本集处理的加权支持向量机方法. 北京理工大学学报. 2005, 25(3): 211~213
98 燕继坤,王勇,曹春霞,郑辉. 样本错误加权的支持向量数据描述. 计算机工程. 2005, 31(2): 24~26
99 J.C.Bezdek. A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithm. IEEE Tranactions PAMI. 1980, 2(1): 1~8
100 于剑,程乾生. 关于FCM算法中的权重指数m的一点注记. 电子学报. 2003,31(3): 478~480
101 宫改云,高新波,伍忠东. 聚类算法中模糊加权指数 m 的优选方法. 模糊系统与数学. 2005, 19(l): 143~1480
102 高新波,裴继红,谢维信. 模糊 C 均值算法中的加权指数 m 的研究. 电子学报. 2000, 28(4): 80~83
103 刘宜平,沈毅力等. 一种参数 m 在线优化的 FCM 改进算法. 电机与控制学报. 2000, 4(3):164~167
104 诸克军,苏顺华,黎金玲.模糊 C—均值中的最优聚类与最佳聚类数.系统工程理论与实践. 2005, 3(3): 54~56
105 L.O.Hall, B.Ozyurt, J.C.Bezdek. Clustering with a Genetically Optimized Approach. IEEE Tranactions on Evolutionary Computation. 1999, 3(2):103~112.
106 A.O.Boudraa. Dynamic Estimation of Number of Clusters in Data Sets. Elect ronics Letters. 1999, 35 (19): 1606~1608
107 S.H.Kwon. Cluster Validity Index for Fuzzy Clustering. Electronics Letters. 1998, 34 (22): 2176~2177
108 X.L.Xie, G.Beni. A Validity Method for Fuzzy Clustering. IEEE Tranactions on PAMI. 1991, 13 (8): 841~847
109 N.R.Pal, J.C.Bezdk. On Cluster Validity for the Fuzzy C-mean Model. IEEE Tranactions on Fuzzy Systems. 1995, 3(3): 370~379
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