智能故障诊断及其在变频器中的应用研究
|
摘要
目前,交流传动系统的故障诊断技术受到广泛关注,在电机的可靠性解决后,变频器的故障诊断研究成为重点。故障诊断已进入智能化的诊断阶段,智能诊断方法无需对象的数学模型,引起极大的重视和研究。但是目前已开发的方法都存在局限性,融合多种智能诊断方法已成为当今故障诊断的一个研究热点。
本文在研究智能故障诊断理论的基础上,提出了两种集成智能故障诊断方法,一种是融合FTA和BAM的故障诊断方法,一种是基于BP神经网络和D-S证据理论的故障诊断方法,分别对变频器及变频器最易失效的逆变电路部分进行故障诊断研究。
在研究FTA和BAM神经网络在故障诊断中应用的基础上,提出了一种融合FTA和BAM的故障诊断方法。利用FTA得到系统所有的故障模式,进而由故障模式和根据经验得到的故障分析归纳出BAM的学习样本,即故障模式和故障分析之间的对应。BAM通过联想记忆矩阵并行联想,得到诊断结果,扩展综合故障诊断能力。用上述方法对变频器进行故障诊断仿真分析,结果表明该方法用于解决此类问题是有效的。
根据BP和D-S证据理论的特点,使BP神经网络和D-S证据理论优势互补,构建基于BP和D-S推理的综合诊断模型。各测点建立各自的BP,其信号由自己的BP先诊断,完成单测点的局部诊断。然后利用子BP的局部诊断结果构造D-S证据理论的可信度函数,完成多测点证据信息的融合,最终得到综合诊断结果。通过对变频器的关键部件-逆变电路故障诊断的仿真实例分析,验证了该方法的可行性和有效性。
At present, the fault diagnosis technology of the ac transmission system is widely concerned, and the research on diagnosis is focusing on the inverter’s fault diagnosis after the reliability of the motor is solved. The fault diagnosis has entered the phase of intelligent diagnosis. The intelligent fault diagnosis methods have been paid much attention because they don’t need the math model of the object. However, in terms of the various intelligent methods which have been developed at present, there are limitations,so the technology of integrating multiple intelligent diagnosis has become a hotspot..
In this article, on the basis of studying about the theory of intelligent fault diagnosis,two methods of integrated intelligent fault diagnosis were proposed, one was the integration of FTA and BAM, the other one was based on the BP network and D-S evidence theory, specificly, those two intelligent methods were applied to the inverter and the inverter’s most vulnerable part -the inverter circuit. Based on the study about the application of FTA and BAM network in fault diagnosis, a method of fault diagnosis based on the fuse of FTA and BAM was proposed. All of the fault modes were obtained by FTA, then the learning sample of
BAM were summarized which were the corresponding relations between the fault sign(the status of the monitoring points)and the happening or not of the bottom events in fault tree. The results of diagnosis were associated parallel by the associative memory matrix, thus expanding the general ability of fault diagnosis. An emulation of the realization of fault diagnosis with the method mentioned above was performed, and analytical results showed that the approach is valid to fault diagnosis.
According to the characteristics of BP network and D-S evidence theory, a diagnosis model combined the advantages of BP and D-S theory was constructed. Each measuring point established its sub-BP network, finishing partial diagnosis of single measuring point. Then the credibility functions of D-S were constructed by using each BP’s partial diagnosis result, completing the fusion of multiple evidence information. Finally, the comprehensive diagnosis results were got. The feasibility and effectiveness of this method was verified by the simulation analysis of fault diagnosis in inverter’s key components-the inverter circuit.
本文在研究智能故障诊断理论的基础上,提出了两种集成智能故障诊断方法,一种是融合FTA和BAM的故障诊断方法,一种是基于BP神经网络和D-S证据理论的故障诊断方法,分别对变频器及变频器最易失效的逆变电路部分进行故障诊断研究。
在研究FTA和BAM神经网络在故障诊断中应用的基础上,提出了一种融合FTA和BAM的故障诊断方法。利用FTA得到系统所有的故障模式,进而由故障模式和根据经验得到的故障分析归纳出BAM的学习样本,即故障模式和故障分析之间的对应。BAM通过联想记忆矩阵并行联想,得到诊断结果,扩展综合故障诊断能力。用上述方法对变频器进行故障诊断仿真分析,结果表明该方法用于解决此类问题是有效的。
根据BP和D-S证据理论的特点,使BP神经网络和D-S证据理论优势互补,构建基于BP和D-S推理的综合诊断模型。各测点建立各自的BP,其信号由自己的BP先诊断,完成单测点的局部诊断。然后利用子BP的局部诊断结果构造D-S证据理论的可信度函数,完成多测点证据信息的融合,最终得到综合诊断结果。通过对变频器的关键部件-逆变电路故障诊断的仿真实例分析,验证了该方法的可行性和有效性。
At present, the fault diagnosis technology of the ac transmission system is widely concerned, and the research on diagnosis is focusing on the inverter’s fault diagnosis after the reliability of the motor is solved. The fault diagnosis has entered the phase of intelligent diagnosis. The intelligent fault diagnosis methods have been paid much attention because they don’t need the math model of the object. However, in terms of the various intelligent methods which have been developed at present, there are limitations,so the technology of integrating multiple intelligent diagnosis has become a hotspot..
In this article, on the basis of studying about the theory of intelligent fault diagnosis,two methods of integrated intelligent fault diagnosis were proposed, one was the integration of FTA and BAM, the other one was based on the BP network and D-S evidence theory, specificly, those two intelligent methods were applied to the inverter and the inverter’s most vulnerable part -the inverter circuit. Based on the study about the application of FTA and BAM network in fault diagnosis, a method of fault diagnosis based on the fuse of FTA and BAM was proposed. All of the fault modes were obtained by FTA, then the learning sample of
BAM were summarized which were the corresponding relations between the fault sign(the status of the monitoring points)and the happening or not of the bottom events in fault tree. The results of diagnosis were associated parallel by the associative memory matrix, thus expanding the general ability of fault diagnosis. An emulation of the realization of fault diagnosis with the method mentioned above was performed, and analytical results showed that the approach is valid to fault diagnosis.
According to the characteristics of BP network and D-S evidence theory, a diagnosis model combined the advantages of BP and D-S theory was constructed. Each measuring point established its sub-BP network, finishing partial diagnosis of single measuring point. Then the credibility functions of D-S were constructed by using each BP’s partial diagnosis result, completing the fusion of multiple evidence information. Finally, the comprehensive diagnosis results were got. The feasibility and effectiveness of this method was verified by the simulation analysis of fault diagnosis in inverter’s key components-the inverter circuit.
引文
[1]许丽婷.智能故障诊断专家系统体系结构的研究[D].西安:西安建筑科技大学,2006,1-9
[2]鄂加强.智能故障诊断技术及其应用[M].长沙:湖南大学出版社,2006,1-15
[3]孙丰涛,张承慧,崔纳新等.变频器故障诊断技术研究与分析[J].电机与控制学报,2005,9(3):272-274
[4]王柯.基于粗糙集和支持向量机的智能故障诊断方法研究[D].无锡:江南大学,2008,2-3
[5]何建平,陆治国.电气传动[M].重庆:重庆大学出版社,2002,196-200
[6]余嘉.交-交变频器智能故障诊断技术研究[D].重庆:重庆大学,2005,4-47
[7]黄勇.富士变频器的故障诊断及对策研究[D].上海:上海交通大学,2005,1-4
[8]蔡宗平,汤正平,王跃钢等.控制系统故障诊断浅析[J].电光与控制,2007,14(4):19-22
[9]张松鹤,罗跃纲,岳玉莲.基于自适应神经网络的智能故障诊断研究[J].大连民族学院学报,2004年1月,6(1):27-30
[10]邵延峰,薛红军.故障树分析法在系统故障诊断中的应用[J].中国制造业信息化,2007年1月,36(1):72-74
[11]吴明强,李霁红,曹爱东等.故障诊断专家系统综合智能推理技术研究[J].计算机测量与控制,2004,12(10):932-934
[12]韩光臣.复杂机电装备故障诊断关键技术研究[D].西安:西北工业大学,2005,1-7
[13]宋华,张洪,王行仁.T-S模糊故障树分析方法[J].控制与决策,2006,20(2):854- 859
[14] Hong YingYi ,Lee LunHui.Application of fuzzy fault-tree analysis to assess the reliability of a protection system for a switchyard[J]. International Journal of Emerging Electric Power Systems, October 3, 2008, 9(4):145-16
[15]范兆军,郑海起,戚洪海.基于信息融合技术的机械系统故障诊断框架研究[J].科学技术与工程,2006年12月,23(6):4709-4712
[16]戴威.Bayes推理在液压系统故障诊断中的应用[J].液压与气动,2007(7):98– 99
[17]徐德明.模糊信息融合在电子电路故障诊断中的应用[J].中国科技信息,2008(1):4-6
[18]孙雅囡.基于模糊神经网络和D-S证据理论的故障诊断方法研究[J].中国舰船研究, 2008(1):45-47
[19]李乾坤.基于融合策略的智能故障诊断系统的研究[D].四川:四川大学,2004,1-6
[20]蒋超.轨道交通自动门远程监控系统中的智能故障诊断系统研究[D].南京:南京理工大学,2004,2-4
[21]贺小辉.基于神经网络的智能故障诊断技术研究与应用[D].上海:上海大学,2005,3-8
[22]李合平,朱宏,张志强.基于神经网络和专家系统的雷达故障诊断方法[J].微计算机信息(测控自动化),2008,24(9-1):215-217
[23]孟德远,赵武.基于神经网络和专家系统的乳化液泵站故障诊断研究[J].煤矿机械,2008,29(10):217-220
[24]张广斌,葛运建,方凯.神经网络和专家系统在变速器检测中的应用[J].华中科技大学学报(自然科学版),2008,53-56
[25]陈维,陈永革,赵强.基于BP神经网络的装备故障诊断专家系统研究[J].指挥控制与仿真,2008,30(4):103-105
[26]成燕.基于神经网络的变频器故障诊断的研究[D].沈阳:沈阳工业大学,2002,41-45
[27]陈伯时,陈敏逊.交流调速系统[M].北京:机械工业出版社,2005,120-135
[28]张作良.基于小波变换和神经网络的电压型变频器故障诊断系统[D].长沙:中南大学,2008,7-16
[29]黄琼玲.分数阶Fourier变换在变频调速异步电动机故障诊断中的应用[D].武汉:武汉科技大学,2008,2-16
[30] P.Vas. Investigation of squirrel-cage induction motors with concentrated type of rotor asymmetries.Elekrroreehnika,1975,68(3):461-463
[31]周东华.国内动态系统故障诊断技术的一些最新进展[J].自动化博览,2007,24(10):16-18
[32] M.E.H.Benbouzid.Bibliography on induction motors faults detection and diagnosis.IEEE Transactions on Energy Conversion,1999,14(4):1065-1074
[33]孙向作.基于数据融合的电机故障诊断技术研究[D].太原:中北大学,2008,1-52
[34]苏晓丹.三相感应电机故障诊断及容错控制研究[D].长沙:江南大学,2008,1-44
[35] Tang HongCheng , Li ZhuXin, Wu HuaFeng. Asynchronous motor fault diagnosis based on artificial immunity[J].Zhongguo Dianji Gongcheng Xuebao, December 1, 2005,25(23):158-162
[36] Lebaroud,Abdesselam.Time-frequency classification applied to induction machine faults monitoring[C].IECON Proceedings(Industrial Electronics Conference),2006, 5051-5056
[37] DuYuyuan,Wang Xu. Research on fault diagnosis system base on output waveform of high voltage inverter[C]. Chinese Control and Decision Conference, 2008, CCDC 2008, 556-559
[38]产萍.用PWM放大器实现400HZ电源[J].国际电子元器件,2004(3):97-101
[39]樊立萍,王忠庆.电力电子技术[M].北京:北京大学出版社,2006,160-180
[40]冯玉生,李宏.电力电子变流装置典型应用实例[J].北京:机械工业出版社,2008,97-100
[41]戴丽.基于小波神经网路的电动机变频系统故障检测的研究[J].电子质量,2007,5-8
[42] Wang Mulan, Zhang Chongwei.Research on fault diagnosis technology of inverter based on associative memory neural network[R]. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2004,v 2, 1694-1697
[43] Tang Qing-Quan,Yan Shi-Chao.Open-circuit fault diagnosis of transistor in three-level inverter[J]. Zhongguo Dianji Gongcheng Xuebao, July 25, 2008,28(21):26-32
[44] Kim Dong-Eok. Lee Dong-Choon. Fault diagnosis of three-phase PWM inverters using wavelet and SVM[R].IEEE International Symposium on Industrial Electronics, 2008 ,329-334
[45] Xiao Lan, Li Rui. Research on the open-circuit fault diagnosis of transistor in inverter paralleling system[J]. Zhongguo Dianji Gongcheng Xuebao, Febrary 16, 2006,26(4):99-104
[46] SunDan, Meng Jun.Inverter faults diagnosis in PMSM DTC drive using reconstructive phase space and fuzzy clustering[R]. Proceedings of the Chinese Society of Electrical Engineering, June 5,2007,27(16):49-53
[47] GuanYufan, Sun Dan, HeYikang. Mean current vector based online real-time fault diagnosis for voltage source inverter fed induction motor drives[R]. Proceedings of IEEE International Electric Machines and Drives Conference, 2007, v 2, 1114-1118
[48]郑连清,王腾,邹涛.基于神经网络的三相全控桥整流电路故障诊断[J].重庆大学学报,2004年9月,27(9):72-74
[49]郝爱国.故障树定量计算算法研究及计算机实现[D].河北:河北工业大学,2005,14-21
[50] DuSheng-zhi,ChenZeng-qiang. Sensitivity to noise in bidirectional associative memory (BAM)[J].IEEE Transactions on Neural Networks, July 2005,16(4) : 887-898
[51] Assaf Tariq, Dugan Joanne Bechta. Diagnostic expert systems from dynamic fault trees[R]. Proceedings of the Annual Reliability and Maintainability Symposium, 2004, 444-450
[52]陈炜,吴志良.故障诊断专家系统结合故障树技术的结构研究[J].航海技术,2005(6),43-44
[53] Lao Dehan, Chen Weihai.ANN based expert system and its application to fault diagnosis[J] Ruan Jian Xue Bao/Journal of Software, 1998, 9(6):611-614
[54] Jiang Shiping,Chen Ying.Design of knowledge base of expert system in ANN-based mechanical design process[J].Zhong guo Jixie Gongc heng/China Mechanical Engineering, Jun 25 ,2002,13(12):1034-1036
[55]张超杰,贺国,梁术海.模糊神经网络及其在控制系统故障诊断中的应用[J].计算机测量与控制,2007,15(4):426-428
[56]刘丙杰,刘勇志.基于模糊神经网络的导弹故障诊断[J].弹箭与制导学报,2003,23(2):68-70
[57]薄海涛,白振兴.基于故障树和神经网络的飞机电源系统故障诊断[J].自动化与仪表,2005(4):65-66
[58]郑亚忠.Hadamard扩谱及CDMA的研究[J].信息技术,2008,第1期,126-132
[59] Wang Zheng,Gong XueHui,Wang Fan. Generator excitation control system fault diagnosis with ANN[J]. November 2008,34(11): 2501-2505
[60]韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006,58-65
[61] Wong W.Eric.BP neural network-based effective fault localization [J]. International Journal of Software Engineering and Knowledge Engineering, June,2009,19(4):573-597
[62]徐德鸿,马皓.电力电子装置自动诊断[M].北京:科学出版社,2001,50-75
[63] Liu Fu-Xian, Xing Qing-Hua. New decision making method based on D-S fusion evidence [J]. Xitong Gongcheng Lilun yu Shijian, July 2009, 29(7): 125-131
[64] Hua Zong,Yun Zhang. A real-time recognition of working patterns to fault diagnosis based on BP neural network[R].Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2006, v 2, 5769-5772
[65] Su Wei, Cui Guangcai. Faults Diagnosis of Vehicles Transmission System Based on ANN[R]. Proceedings of the International Symposium on Test and Measurement, 2003,v 2, 937-940
[66] Xu Wenshang,Yu Qingming. Application research on fault diagnosis system of mine rotating machinery based on ANN[C].2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, 2007,3531-3535
[67]虞和济,陈长征,张省.基于神经网络的智能诊断[J].振动工程学报,2000,13(2):78-80
[68]王新,王志珍.基于信息融合技术的提升机控制系统故障诊断方法的研究[J].煤矿机电,2007(2):61-63
[69] Wei Xing,Shu NaiQiu.Power transformer fault integrated diagnosis based on improved PSO-BP neural networks and D-S evidential reasoning[J].Dianli Xitong Zidonghua April 10, 2006,30(7):46-50
[70]蔡兵,王培元.大功率IGBT驱动过流保护电路研究[J].襄樊学院学报,2005年9月, 26(5):60-62
[71]郝润科,杨一波.绝缘栅栏双极型晶体管(IGBT)驱动及保护电路的研究[J].上海理工大学学报,2004,26(3):283-285
[72]石磊,刘栋.交流逆变器中IGBT的驱动与保护[J].电气开关,2007(6):1-3
[73]杨志,刘建政,赵争鸣等.用于三电平逆变器中的IGBT驱动保护电路设计[J].电力自动化设备,2004年4月,24(4):42-44
[74]胡俊达.IGBT的驱动与保护电路研究[J].计算机与自动控制,2003(6):37-40
[75]杨斌文,胡浩,张建.IGBT的有关保护问题[J].电气开关,2006(6):7-9
[76]侯宏波,刘青魏.浅析通用变频器主回路保护[J].济南纺织化纤科技,2002(3): 40-42
[2]鄂加强.智能故障诊断技术及其应用[M].长沙:湖南大学出版社,2006,1-15
[3]孙丰涛,张承慧,崔纳新等.变频器故障诊断技术研究与分析[J].电机与控制学报,2005,9(3):272-274
[4]王柯.基于粗糙集和支持向量机的智能故障诊断方法研究[D].无锡:江南大学,2008,2-3
[5]何建平,陆治国.电气传动[M].重庆:重庆大学出版社,2002,196-200
[6]余嘉.交-交变频器智能故障诊断技术研究[D].重庆:重庆大学,2005,4-47
[7]黄勇.富士变频器的故障诊断及对策研究[D].上海:上海交通大学,2005,1-4
[8]蔡宗平,汤正平,王跃钢等.控制系统故障诊断浅析[J].电光与控制,2007,14(4):19-22
[9]张松鹤,罗跃纲,岳玉莲.基于自适应神经网络的智能故障诊断研究[J].大连民族学院学报,2004年1月,6(1):27-30
[10]邵延峰,薛红军.故障树分析法在系统故障诊断中的应用[J].中国制造业信息化,2007年1月,36(1):72-74
[11]吴明强,李霁红,曹爱东等.故障诊断专家系统综合智能推理技术研究[J].计算机测量与控制,2004,12(10):932-934
[12]韩光臣.复杂机电装备故障诊断关键技术研究[D].西安:西北工业大学,2005,1-7
[13]宋华,张洪,王行仁.T-S模糊故障树分析方法[J].控制与决策,2006,20(2):854- 859
[14] Hong YingYi ,Lee LunHui.Application of fuzzy fault-tree analysis to assess the reliability of a protection system for a switchyard[J]. International Journal of Emerging Electric Power Systems, October 3, 2008, 9(4):145-16
[15]范兆军,郑海起,戚洪海.基于信息融合技术的机械系统故障诊断框架研究[J].科学技术与工程,2006年12月,23(6):4709-4712
[16]戴威.Bayes推理在液压系统故障诊断中的应用[J].液压与气动,2007(7):98– 99
[17]徐德明.模糊信息融合在电子电路故障诊断中的应用[J].中国科技信息,2008(1):4-6
[18]孙雅囡.基于模糊神经网络和D-S证据理论的故障诊断方法研究[J].中国舰船研究, 2008(1):45-47
[19]李乾坤.基于融合策略的智能故障诊断系统的研究[D].四川:四川大学,2004,1-6
[20]蒋超.轨道交通自动门远程监控系统中的智能故障诊断系统研究[D].南京:南京理工大学,2004,2-4
[21]贺小辉.基于神经网络的智能故障诊断技术研究与应用[D].上海:上海大学,2005,3-8
[22]李合平,朱宏,张志强.基于神经网络和专家系统的雷达故障诊断方法[J].微计算机信息(测控自动化),2008,24(9-1):215-217
[23]孟德远,赵武.基于神经网络和专家系统的乳化液泵站故障诊断研究[J].煤矿机械,2008,29(10):217-220
[24]张广斌,葛运建,方凯.神经网络和专家系统在变速器检测中的应用[J].华中科技大学学报(自然科学版),2008,53-56
[25]陈维,陈永革,赵强.基于BP神经网络的装备故障诊断专家系统研究[J].指挥控制与仿真,2008,30(4):103-105
[26]成燕.基于神经网络的变频器故障诊断的研究[D].沈阳:沈阳工业大学,2002,41-45
[27]陈伯时,陈敏逊.交流调速系统[M].北京:机械工业出版社,2005,120-135
[28]张作良.基于小波变换和神经网络的电压型变频器故障诊断系统[D].长沙:中南大学,2008,7-16
[29]黄琼玲.分数阶Fourier变换在变频调速异步电动机故障诊断中的应用[D].武汉:武汉科技大学,2008,2-16
[30] P.Vas. Investigation of squirrel-cage induction motors with concentrated type of rotor asymmetries.Elekrroreehnika,1975,68(3):461-463
[31]周东华.国内动态系统故障诊断技术的一些最新进展[J].自动化博览,2007,24(10):16-18
[32] M.E.H.Benbouzid.Bibliography on induction motors faults detection and diagnosis.IEEE Transactions on Energy Conversion,1999,14(4):1065-1074
[33]孙向作.基于数据融合的电机故障诊断技术研究[D].太原:中北大学,2008,1-52
[34]苏晓丹.三相感应电机故障诊断及容错控制研究[D].长沙:江南大学,2008,1-44
[35] Tang HongCheng , Li ZhuXin, Wu HuaFeng. Asynchronous motor fault diagnosis based on artificial immunity[J].Zhongguo Dianji Gongcheng Xuebao, December 1, 2005,25(23):158-162
[36] Lebaroud,Abdesselam.Time-frequency classification applied to induction machine faults monitoring[C].IECON Proceedings(Industrial Electronics Conference),2006, 5051-5056
[37] DuYuyuan,Wang Xu. Research on fault diagnosis system base on output waveform of high voltage inverter[C]. Chinese Control and Decision Conference, 2008, CCDC 2008, 556-559
[38]产萍.用PWM放大器实现400HZ电源[J].国际电子元器件,2004(3):97-101
[39]樊立萍,王忠庆.电力电子技术[M].北京:北京大学出版社,2006,160-180
[40]冯玉生,李宏.电力电子变流装置典型应用实例[J].北京:机械工业出版社,2008,97-100
[41]戴丽.基于小波神经网路的电动机变频系统故障检测的研究[J].电子质量,2007,5-8
[42] Wang Mulan, Zhang Chongwei.Research on fault diagnosis technology of inverter based on associative memory neural network[R]. Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2004,v 2, 1694-1697
[43] Tang Qing-Quan,Yan Shi-Chao.Open-circuit fault diagnosis of transistor in three-level inverter[J]. Zhongguo Dianji Gongcheng Xuebao, July 25, 2008,28(21):26-32
[44] Kim Dong-Eok. Lee Dong-Choon. Fault diagnosis of three-phase PWM inverters using wavelet and SVM[R].IEEE International Symposium on Industrial Electronics, 2008 ,329-334
[45] Xiao Lan, Li Rui. Research on the open-circuit fault diagnosis of transistor in inverter paralleling system[J]. Zhongguo Dianji Gongcheng Xuebao, Febrary 16, 2006,26(4):99-104
[46] SunDan, Meng Jun.Inverter faults diagnosis in PMSM DTC drive using reconstructive phase space and fuzzy clustering[R]. Proceedings of the Chinese Society of Electrical Engineering, June 5,2007,27(16):49-53
[47] GuanYufan, Sun Dan, HeYikang. Mean current vector based online real-time fault diagnosis for voltage source inverter fed induction motor drives[R]. Proceedings of IEEE International Electric Machines and Drives Conference, 2007, v 2, 1114-1118
[48]郑连清,王腾,邹涛.基于神经网络的三相全控桥整流电路故障诊断[J].重庆大学学报,2004年9月,27(9):72-74
[49]郝爱国.故障树定量计算算法研究及计算机实现[D].河北:河北工业大学,2005,14-21
[50] DuSheng-zhi,ChenZeng-qiang. Sensitivity to noise in bidirectional associative memory (BAM)[J].IEEE Transactions on Neural Networks, July 2005,16(4) : 887-898
[51] Assaf Tariq, Dugan Joanne Bechta. Diagnostic expert systems from dynamic fault trees[R]. Proceedings of the Annual Reliability and Maintainability Symposium, 2004, 444-450
[52]陈炜,吴志良.故障诊断专家系统结合故障树技术的结构研究[J].航海技术,2005(6),43-44
[53] Lao Dehan, Chen Weihai.ANN based expert system and its application to fault diagnosis[J] Ruan Jian Xue Bao/Journal of Software, 1998, 9(6):611-614
[54] Jiang Shiping,Chen Ying.Design of knowledge base of expert system in ANN-based mechanical design process[J].Zhong guo Jixie Gongc heng/China Mechanical Engineering, Jun 25 ,2002,13(12):1034-1036
[55]张超杰,贺国,梁术海.模糊神经网络及其在控制系统故障诊断中的应用[J].计算机测量与控制,2007,15(4):426-428
[56]刘丙杰,刘勇志.基于模糊神经网络的导弹故障诊断[J].弹箭与制导学报,2003,23(2):68-70
[57]薄海涛,白振兴.基于故障树和神经网络的飞机电源系统故障诊断[J].自动化与仪表,2005(4):65-66
[58]郑亚忠.Hadamard扩谱及CDMA的研究[J].信息技术,2008,第1期,126-132
[59] Wang Zheng,Gong XueHui,Wang Fan. Generator excitation control system fault diagnosis with ANN[J]. November 2008,34(11): 2501-2505
[60]韩力群.人工神经网络教程[M].北京:北京邮电大学出版社,2006,58-65
[61] Wong W.Eric.BP neural network-based effective fault localization [J]. International Journal of Software Engineering and Knowledge Engineering, June,2009,19(4):573-597
[62]徐德鸿,马皓.电力电子装置自动诊断[M].北京:科学出版社,2001,50-75
[63] Liu Fu-Xian, Xing Qing-Hua. New decision making method based on D-S fusion evidence [J]. Xitong Gongcheng Lilun yu Shijian, July 2009, 29(7): 125-131
[64] Hua Zong,Yun Zhang. A real-time recognition of working patterns to fault diagnosis based on BP neural network[R].Proceedings of the World Congress on Intelligent Control and Automation (WCICA), 2006, v 2, 5769-5772
[65] Su Wei, Cui Guangcai. Faults Diagnosis of Vehicles Transmission System Based on ANN[R]. Proceedings of the International Symposium on Test and Measurement, 2003,v 2, 937-940
[66] Xu Wenshang,Yu Qingming. Application research on fault diagnosis system of mine rotating machinery based on ANN[C].2007 8th International Conference on Electronic Measurement and Instruments, ICEMI, 2007,3531-3535
[67]虞和济,陈长征,张省.基于神经网络的智能诊断[J].振动工程学报,2000,13(2):78-80
[68]王新,王志珍.基于信息融合技术的提升机控制系统故障诊断方法的研究[J].煤矿机电,2007(2):61-63
[69] Wei Xing,Shu NaiQiu.Power transformer fault integrated diagnosis based on improved PSO-BP neural networks and D-S evidential reasoning[J].Dianli Xitong Zidonghua April 10, 2006,30(7):46-50
[70]蔡兵,王培元.大功率IGBT驱动过流保护电路研究[J].襄樊学院学报,2005年9月, 26(5):60-62
[71]郝润科,杨一波.绝缘栅栏双极型晶体管(IGBT)驱动及保护电路的研究[J].上海理工大学学报,2004,26(3):283-285
[72]石磊,刘栋.交流逆变器中IGBT的驱动与保护[J].电气开关,2007(6):1-3
[73]杨志,刘建政,赵争鸣等.用于三电平逆变器中的IGBT驱动保护电路设计[J].电力自动化设备,2004年4月,24(4):42-44
[74]胡俊达.IGBT的驱动与保护电路研究[J].计算机与自动控制,2003(6):37-40
[75]杨斌文,胡浩,张建.IGBT的有关保护问题[J].电气开关,2006(6):7-9
[76]侯宏波,刘青魏.浅析通用变频器主回路保护[J].济南纺织化纤科技,2002(3): 40-42
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |