氧化铝烧结窑排烟风机信息融合故障诊断方法与系统研究
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摘要
排烟风机是国民经济建设中不可缺少的关键设备,对其进行故障诊断方法的研究具有重要意义。当前排烟风机故障诊断手段主要是频谱诊断,随着智能诊断技术的发展,相继出现了针对单个领域的诊断专家系统,如转子故障诊断系统、电机故障诊断系统等,但由于排烟风机结构差异、环境恶劣等因素的影响,特别是在冶金、矿山等恶劣环境,诊断效果较差,为了提高排烟风机监测与故障诊断的准确性,本文作者综合运用信息融合理论、提升小波信号预处理方法、盲源分离故障诊断方法、BP-ART2神经网络故障诊断、多专家协同诊断理论等先进理论和算法,对多传感器信息在多层结构上进行多诊断方法的信息融合,并在理论研究的基础上,开发了排烟风机运行状态监测与故障诊断微机集中式和DSP分布式两种系统。
信号预处理方法中,在提升小波信号分析的基础上,设计了改进型小波去噪阈值函数和平滑递变的自适应提升小波函数,提出了基于信号局部特征的自适应提升小波信号去噪方法,该方法在大型排烟风机故障信号去噪处理中取得了良好的应用效果。
在故障源数不确定情况下的动态源数估计中设计了引入拓展四阶累积量矩阵的盲源分离动态源数估计算法,并研究了根据源数与传感器数的关系(正定、超定、欠定),选择相应分离算法的自适应盲源分离故障诊断方法,该方法在数据融合层面能有效地识别和诊断排烟风机动态故障。
在特征融合层,研究了综合BP网络与ART2自适应共振网络二者优点的改进型BP-ART2神经网络故障诊断方法,在ART2结构的输入层增加非线性映射隐层,通过非线性映射降低输入特征的维数,从而提高ART2神经网络的诊断效率。在故障聚类中,提出了ART2警戒阈值的局部自适应调整算法,对每个聚类设置各自的警戒阈值,并根据聚类结果与期望值的误差来调整隐层映射权值;在聚类评判指标上,采用双重评判指标,将信号与相应聚类中心的幅值差,与警戒阈值一起作为判断聚类的评判指标,当两者同时满足时聚类成功。从而提高了排烟风机故障诊断的效率和准确性。
针对排烟风机转子故障诊断、电气故障诊断以及机电耦合故障诊断等各种诊断方法,在排烟风机机械诊断与电气诊断的基础上,研究了综合时域诊断与频域诊断相融合、机械诊断与电气诊断相融合的黑板型多专家协同诊断系统,实现了机械与电气双重角度的故障诊断。并将多专家诊断黑板结构按照诊断逻辑划分为8个信息层,分别包含相应的诊断条件、诊断方法和诊断结论,并建立了相应的黑板监督机制,设计了多专家融合诊断算法。
在决策融合层,模仿诊断专家综合考虑多个传感器诊断信息,设计了多传感器加权激励融合方法,实现多个传感器诊断结果相互比较与应证,根据两两传感器诊断故障之间的相关加权激励系数矩阵,分析各故障的相互激励与增强程度,计算加权融合结果,最后将所有两两传感器加权融合结果进行综合融合并归一化,得出多传感器故障融合诊断结果。并对多种诊断方:法得到的局部诊断结果,采用D-S证据理论决策融合得到全局诊断结论。
在本文所研究的信号处理与故障诊断方法的基础上,结合排烟风机的力学分析与针对现场干扰信号的信号处理以及故障诊断的要求,研究开发了排烟风机运行状态监测与故障诊断微机集中式监测和DSP分布式监测两种系统,并已成功应用于生产实践的风机监测现场,实现了大型排烟风机状态实时监测与故障诊断。
Smoke exhauster fan is important equipment in economic construction. It is of great significance to research intelligent fault diagnosis method. At present, the main fault diagnosis method of smoke exhauster fan is frequency spectrum diagnosis. As the development of intelligence diagnosis technology, diagnosis expert system have appeared consecutively, specifically for single fields, for instance, rotator fault diagnose system, motor fault diagnose system etc.. Since the effect factor of machinery structure difference and adverse circumstances, especially in metallurgy, mine etc., therefore, diagnosis effect is relatively poor. In order to improve accuracy of monitoring and diagnose of smoke extaction fan, the author apply the advanced theory and algorithm synthetically such as information fusion theory, signal pretreatment method of lifting wavelet, blind source separation, BP-ART2 neural networks fault diagnose, multi-experts collaborative diagnose theory, and so on, to fuse multi-sensor information by multi-diagnosis method on multi-level structure, on basis of research of theory, and develop two kind monitorings and fault diagnosis systems of smoke extaction fan: PC concentrated and DSP distributed.
In signal pretreatment of information fusion theory, a kind improved wavelet threshold function and splitting changing function of lifting wavelet is designed, and an adaptive de-noising method of lifting wavelet on basis of local feature of signals is brought forward , which gain fine signal treatment effect.
On the data level of information fusion theory, under uncertain situation of number of fault source in dynamic estimation of source number, a source number estimation algorithm based on exhibition fourth-order cumulants is designed, and an adaptive fault diagnosis method of blind source separation which chooses the relactive algorithm such as determine, over-determined and Under-determined, according to the relation of the source number and the sensor number is researched. This method is able to distinguish and diagnose of the dynamic fault of smoke extaction fan effectively.
On the feature level of information fusion theory, an improved BP-ART2 neural network fault diagnosis method which utilizes synthetically both benefit of BP neural network and ART2 adaptive resonance theory is researched. On the iput layer of ART2 structure an nonlinear hidden layer is adden to reduce the dimension of input feature and inprove the diagnosis efficiency of ART2 neural network . In the fault cluster of ART2 an local adaptivly adjustive algrithm based on warning threshod of ART2 is designed, and respective threshod is interposed to every cluster , and the hidden layer mapping weight is adjusted according to the error between clustering result and the expected value. On the index of clustering deciding double indexes is adopt to judge the threshod, the first one is the amplitude error between the signal and corresponding clustering centre, the second one is the warning threshod . When the both indexes are satisfied clustering is successful.
Secondly, On the feature level of information fusion theory, aimed at various diagnose method of smoke exhauster fan such as rotor fault diagnosis, electricity fault diagnosis, electromechanical coupling fault diagnosis, and so on, on the basis of machinery diagnosis and electricity diagnosis, a multi-experts coordination diagnosis system based on blackboard is researched which fuses synthetically time-domain diagnosis and frequency-domain diagnosis, machinery diagnosis and electricity diagnosis, and realizes double fault diagnosis of machinery and electricity diagnosis. According to the diagnosis logic structure of the blackboard multi-experts coordination diagnosis system is divided to 8 layers, each layer contains corresponding diagnosis condition, diagnosis method and diagnosis conclusion. A corresponding blackboard supervision mechanism is built and multi-experts fusion diagnosis algorithm is designed.
On the decision level of information fusion theory, a multi-sensor weighted incentive fusion method is designed, which imitates diagnosis experts to consider many sensors diagnosis information synthetically, and realizes several sensor diagnosis results comparing and certificating mutually, and according to the weighted stimulates fusion modulus matrix between each two sensors, analyzes stimulating and strengthening degree, and calculates weighted fusion result, fuses and normalizes synthetically all two sensors weighted fusion results, and comes to a conclusion of multi-sensor fault fusion diagnosis, and aimed at the local diagnosis results of multi-method of diagnosis adopts D-S evidence theory to make global fusion and come to decision-making fusion conclusion.
Adopting the signal treatment and fault diagnosis methods which are researched in this dissertation, according to the actual state of smoke exhauster fan, two kind monitoring and fault diagnosis systems of smoke exhauster fan are developed: PC concentrated and DSP distributed, which already have been successfully applied to the smoke exhauster fan of several enterprise, and realized condition real-time monitoring and fault diagnosis.
信号预处理方法中,在提升小波信号分析的基础上,设计了改进型小波去噪阈值函数和平滑递变的自适应提升小波函数,提出了基于信号局部特征的自适应提升小波信号去噪方法,该方法在大型排烟风机故障信号去噪处理中取得了良好的应用效果。
在故障源数不确定情况下的动态源数估计中设计了引入拓展四阶累积量矩阵的盲源分离动态源数估计算法,并研究了根据源数与传感器数的关系(正定、超定、欠定),选择相应分离算法的自适应盲源分离故障诊断方法,该方法在数据融合层面能有效地识别和诊断排烟风机动态故障。
在特征融合层,研究了综合BP网络与ART2自适应共振网络二者优点的改进型BP-ART2神经网络故障诊断方法,在ART2结构的输入层增加非线性映射隐层,通过非线性映射降低输入特征的维数,从而提高ART2神经网络的诊断效率。在故障聚类中,提出了ART2警戒阈值的局部自适应调整算法,对每个聚类设置各自的警戒阈值,并根据聚类结果与期望值的误差来调整隐层映射权值;在聚类评判指标上,采用双重评判指标,将信号与相应聚类中心的幅值差,与警戒阈值一起作为判断聚类的评判指标,当两者同时满足时聚类成功。从而提高了排烟风机故障诊断的效率和准确性。
针对排烟风机转子故障诊断、电气故障诊断以及机电耦合故障诊断等各种诊断方法,在排烟风机机械诊断与电气诊断的基础上,研究了综合时域诊断与频域诊断相融合、机械诊断与电气诊断相融合的黑板型多专家协同诊断系统,实现了机械与电气双重角度的故障诊断。并将多专家诊断黑板结构按照诊断逻辑划分为8个信息层,分别包含相应的诊断条件、诊断方法和诊断结论,并建立了相应的黑板监督机制,设计了多专家融合诊断算法。
在决策融合层,模仿诊断专家综合考虑多个传感器诊断信息,设计了多传感器加权激励融合方法,实现多个传感器诊断结果相互比较与应证,根据两两传感器诊断故障之间的相关加权激励系数矩阵,分析各故障的相互激励与增强程度,计算加权融合结果,最后将所有两两传感器加权融合结果进行综合融合并归一化,得出多传感器故障融合诊断结果。并对多种诊断方:法得到的局部诊断结果,采用D-S证据理论决策融合得到全局诊断结论。
在本文所研究的信号处理与故障诊断方法的基础上,结合排烟风机的力学分析与针对现场干扰信号的信号处理以及故障诊断的要求,研究开发了排烟风机运行状态监测与故障诊断微机集中式监测和DSP分布式监测两种系统,并已成功应用于生产实践的风机监测现场,实现了大型排烟风机状态实时监测与故障诊断。
Smoke exhauster fan is important equipment in economic construction. It is of great significance to research intelligent fault diagnosis method. At present, the main fault diagnosis method of smoke exhauster fan is frequency spectrum diagnosis. As the development of intelligence diagnosis technology, diagnosis expert system have appeared consecutively, specifically for single fields, for instance, rotator fault diagnose system, motor fault diagnose system etc.. Since the effect factor of machinery structure difference and adverse circumstances, especially in metallurgy, mine etc., therefore, diagnosis effect is relatively poor. In order to improve accuracy of monitoring and diagnose of smoke extaction fan, the author apply the advanced theory and algorithm synthetically such as information fusion theory, signal pretreatment method of lifting wavelet, blind source separation, BP-ART2 neural networks fault diagnose, multi-experts collaborative diagnose theory, and so on, to fuse multi-sensor information by multi-diagnosis method on multi-level structure, on basis of research of theory, and develop two kind monitorings and fault diagnosis systems of smoke extaction fan: PC concentrated and DSP distributed.
In signal pretreatment of information fusion theory, a kind improved wavelet threshold function and splitting changing function of lifting wavelet is designed, and an adaptive de-noising method of lifting wavelet on basis of local feature of signals is brought forward , which gain fine signal treatment effect.
On the data level of information fusion theory, under uncertain situation of number of fault source in dynamic estimation of source number, a source number estimation algorithm based on exhibition fourth-order cumulants is designed, and an adaptive fault diagnosis method of blind source separation which chooses the relactive algorithm such as determine, over-determined and Under-determined, according to the relation of the source number and the sensor number is researched. This method is able to distinguish and diagnose of the dynamic fault of smoke extaction fan effectively.
On the feature level of information fusion theory, an improved BP-ART2 neural network fault diagnosis method which utilizes synthetically both benefit of BP neural network and ART2 adaptive resonance theory is researched. On the iput layer of ART2 structure an nonlinear hidden layer is adden to reduce the dimension of input feature and inprove the diagnosis efficiency of ART2 neural network . In the fault cluster of ART2 an local adaptivly adjustive algrithm based on warning threshod of ART2 is designed, and respective threshod is interposed to every cluster , and the hidden layer mapping weight is adjusted according to the error between clustering result and the expected value. On the index of clustering deciding double indexes is adopt to judge the threshod, the first one is the amplitude error between the signal and corresponding clustering centre, the second one is the warning threshod . When the both indexes are satisfied clustering is successful.
Secondly, On the feature level of information fusion theory, aimed at various diagnose method of smoke exhauster fan such as rotor fault diagnosis, electricity fault diagnosis, electromechanical coupling fault diagnosis, and so on, on the basis of machinery diagnosis and electricity diagnosis, a multi-experts coordination diagnosis system based on blackboard is researched which fuses synthetically time-domain diagnosis and frequency-domain diagnosis, machinery diagnosis and electricity diagnosis, and realizes double fault diagnosis of machinery and electricity diagnosis. According to the diagnosis logic structure of the blackboard multi-experts coordination diagnosis system is divided to 8 layers, each layer contains corresponding diagnosis condition, diagnosis method and diagnosis conclusion. A corresponding blackboard supervision mechanism is built and multi-experts fusion diagnosis algorithm is designed.
On the decision level of information fusion theory, a multi-sensor weighted incentive fusion method is designed, which imitates diagnosis experts to consider many sensors diagnosis information synthetically, and realizes several sensor diagnosis results comparing and certificating mutually, and according to the weighted stimulates fusion modulus matrix between each two sensors, analyzes stimulating and strengthening degree, and calculates weighted fusion result, fuses and normalizes synthetically all two sensors weighted fusion results, and comes to a conclusion of multi-sensor fault fusion diagnosis, and aimed at the local diagnosis results of multi-method of diagnosis adopts D-S evidence theory to make global fusion and come to decision-making fusion conclusion.
Adopting the signal treatment and fault diagnosis methods which are researched in this dissertation, according to the actual state of smoke exhauster fan, two kind monitoring and fault diagnosis systems of smoke exhauster fan are developed: PC concentrated and DSP distributed, which already have been successfully applied to the smoke exhauster fan of several enterprise, and realized condition real-time monitoring and fault diagnosis.
引文
[1]徐敏.设备故障诊断手册--机械设备状态监测与故障诊断.西安:西安交通大学出版社,1998
[2]虞和济,韩庆大,李沈,等.设备故障诊断工程.北京:冶金工业出版社,2001
[3]钟秉林,黄仁.机械故障珍断学.北京:机械工业出版社,1997
[4]屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1986
[5]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用.北京:化学工业出版社,2003
[6]何正嘉,訾艳阳,孟庆丰,等.机械设备非平稳信号的故障诊断原理及应用.北京:高等教育出版社,2001
[7]J.Sohre.Fault Causes and Treats of High Turbo-machinery.ASME petrol- machinery Annual Meeting,1968
[8]D.E.Bently,A.Muszyuska.Detection of Rotor Cracks.Proc.of the Fifteenth Turbo Machinery Symposium,Texas A&M University,1988,129-136
[9]E.Downham.Vibration in Rotating Machinery Malfunction Diagnosis Art and Science.the First Conf.on Vibration in Rotating Machinery,University of Cambridge,1976
[10]R.A.Collacott.Mechanical Condition Monitoring and Fault Diagnosis.London:Chapman and Hall,1977
[11]P.M.McGuire,D.W.Price.The Development and Practical Application of Rotor Dynamic Analysis Techniques for Large Steam Turbine Generators.Int.Vibra- tion in Rotating Machinery,Heriot-Watt University,1988
[12]白木万博,机械振动讲演文集,机械工业部郑州机械研究所振动室,1984
[13]R.Isermannn.Integration of fault detection and diagnosis methods.Preprints IFAC Symposium on Fault Detection,Supervision and Safety for Technical Processes,1994:597-612
[14]D.A.Linkens,H.Wang.Fault diagnosis based on a qualitative bond graph model with emphasis of fault localization.Proceeding of IEE International Conference on Control'94,1994:1329-1334
[15]S.M.A.Cruz,A.J.M.Cardoso.Rotor cage fault diagnosis in three-phase induction motors by extended park's vector approach.Electric Machines and Power Systems,2000,28(3):289-299
[16]W.T.Thomson.Theory of Vibration With Application.Prentice,1972
[17]K.J.Walker,A.Shirkhodaie.Development of a virtual environment for fault diagnosis in rotary machinery.Proceedings of the 33rd Southeastern Symposium on System Theory,IEEE,Piscataway,NJ,USA,2001:99-103
[18]Tim Toutountzakis,M.David.Observations of acoustic emission activity during gear defect diagnosis.NDT & E International,2003,36:471-477
[19]K.R.AlBalushi,B.Samanta.Gear diagnosis using energy-based features of acoustic emission.J Syst Control Engng.2002,216:249-263
[20]S.Edwards,A.W.Lees,M.I.Friawell.Fault Diagnosis of Rotating Machinery.Shock and Vibration Digest,1998,30(1):4-13
[21]M.Tsuchiya,M.Takagi.Vibration and Acoustic fault Diagnosis of Rotational Machine(Application of Wavelets to Diagnostic System for Rotational Machinery).日本机械学会论文集(C编),1998-2,64(618):465-472
[22]史铁林,杨叔子,师汉民,等.机械设备诊断策略的若干问题探讨.华中理工大学学报,1991,8:1-8
[23]盛兆顺,尹琦岭.设备状态检测与故障诊断技术及应用.北京:化学工业出版社,2003
[24]何勇,李增芳.智能化故障诊断技术的研究与应用.浙江大学学报,2003,29(2):119-124
[25]丁康,朱小勇,陈亚华.齿轮箱典型故障振动特征与诊断策略.振动与冲击,2001,20(3)
[26]高金吉.高速涡轮机械振动故障机理及诊断方法的研究[博士学位论文].北京:清华大学,1993
[27]李建国.转子系统稳定性和非线性以及油膜失稳控制研究[博士学位论文].西安:西安交通大学,1992
[28]欧阳平超.风机工况监测与故障诊断研究[博士学位论文].沈阳:东北大学,2004
[29]陈长征.旋转机械故障智能诊断方法研究[博士学位论文].徐州:中国矿业大学,1998
[30]许伯强,李和明,孙丽玲,等.小波分析应用于笼型异步电动机转子断条在线检测初探.中国电机工程学报,2006,21(11):24-28
[31]马宏忠,李训铭,王刘芳.高压异步电机转子绕组故障诊断系统的研究.高电压技术,2004,30(4):31-33
[32]赵桂林,詹渝明,陈明涛.基于傅里叶变换的异步电机转子故障检测.东北电力技术,2004,7:9-11
[33]刘同明,夏祖勋,解洪成.数据融合技术及其应用.国防工业出版社,1998
[34]杨靖宇.战场数据融合技术.兵器工业出版社,1994
[35]滕召胜,罗隆福,章调生.智能检测系统与数据融合.机械工业出版社,2000
[36]P.Grossmann.Multisensor data fusion.GEC Journal of Technology,1998.15(1):27-37
[37]E.L.Waltz,M.Buede.Data fusion and decision support for commmad and control.IEEE trans.on syst.man and cyberm,1986,16(6):865-879
[38]E.L.Waltz,J.Linas.Multi-sensor data fusion.Artech,Norwood,Masschusstts,1990:175-85
[39]R.C.Luo,M.G.Kay.Multi-sensor Integration and fusion in intelligent systems.IEEE Transaction on systems,Man and cybernetics,1989,19(5):901-931
[40]R.C.Luo,G.Michael.Multisensor integration and fusion for intelligent machines and systems.Norwood N J:Ablex Publishing Corporation,1995:1-19
[41]D.L.Hall.Mathematical Techniques in Multi-Sensor Data Fusion.Artech House,1992:10-15
[42]R.Mamlook,W.E.Thompson.A Multi-sensor Fusion Algorithm for Multi-target.Multi-background Classification.SPIE 1992,1699:72-82
[43]V.Gorodetsky,O.Karsaev,V.Samoilov.Multi-Agent Data and Information Fusion:Architecture,Methodology,Technology and Sotware Tool.Data Fusion for Situation Monitoring,Incident Detection,Alert and Response Management,ISO Press,2005:308-339
[44]Y.Shi,C.Z.Hu,H.M.Tan.Multisensor battlefield monitoring system and its signal processing simulation.International Symposium on Test and Measurement,Proceedings,International Academic Publishers,1997.6:474-477
[45]戴箔.C~3I中的多传感器数据融合技术.兵工学报,1998,4:356-360
[46]赵宗贵,耿立贤,孔祥忠.三军联合作战C~3I中的数据融合.现代电子工程,1993:16-26
[47]杨杰,陆正刚,黄欣.基于多传感器数据融合的目标识别和跟踪.上海交通大学学报,1999,9:1107-1110
[48]郁文贤.多传感器信息融合技术评述.国防科技大学学报,1994,3:1-11
[49]袁小勇,屈梁生.机械故障诊断中的信息融合利用问题研究.振动、测试与诊断,1999,9:187-192
[50]张彦铎,姜兴渭.多传感器信息融合及在智能故障诊断中的应用.传感器技术,1999,2:18-22
[51]晋风华,李录平.多传感器信息融合技术及其在旋转机械振动故障诊断中的应用.热力发电,2004,5:45-48
[52]谭逢友,卢宏伟等.信息融合技术在机械故障诊断中的应用.重庆大学学报,2006,29(1):15-18
[53]孙卫祥,陈进,伍星等.基于信息融合的支撑座早期松动故障诊断.上海交通大学学报,2006,2:239-242
[54]R.R.Yager,D.P.Filev.Induced ordered weighted averaging operators.IEEE Transactions on Systems,Man and Cybernetics,Part B,1999,29(2):141-150
[55]李宏,刘江涛,安玮,等.主观Bayes方法与神经网络相结合的多传感器数据融合空间点目标识别方法.红外与毫米波学报,1997,16(6):448-454.
[56]P.L.Bogler.Dempster-Shafer Reasoning with Applications to Multi-sensor Target Identification Systems.IEEE Transactions on systems,Man,and cybernetics,1989,17(6):901-930
[57]H.M.Ertunc,K.A.Loparo.A decision fusion algorithm for tool wear condition monitoring in drilling.International Journal of Machine Tools & Manufacture,2001,41:1347-1362
[58]E.Blasch.Information Fusion for Decision Making Designing Realizable Information Fusion Systems.Data Fusion for Situation Monitoring,Incident Detection,Alert and Response Management,ISO Press,2005:3-22
[59]H.Liang.Small target detection in multisensor system based on Demopster- Shafter evidence theory.The International Society for Optical Engineering,2001,9:272-279
[60]G.Niu,T.Han,B.S.Yang,et al.Multi-agent decision fusion for motor fault diagnosis.Mechanical Systems and Signal Processing,2007,21:1285-1299
[61]B.S.Yang,J.K.Kim.Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals.Mechanical Systems and Signal Processing,2006,20(2):403-420
[62]李斌,章卫国,宁东方,等.基于神经网络信息融合的智能故障诊断方法.计算 机仿真,2008,25(6):35-37
[63]孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[64]杨建国.小波分析及其工程应用.北京:机械工业出版社,2005
[65]S.Mallat.A theory for multi-resolution signal decomposition:the wavelet representation.Pattern Analysis and Machine Intelligenee,1989,11(7):674-693
[66]I.Daubechies.Orthonomal bases of compactly supported wavelets.Comm,pure Appl.Math,1988,41:909-996
[67]A.Cohen.I.Daubechies.J.Feauveau.Bi-orthogonal bases of compaetly suppor- ted wavelets.Comm.Pure Appl.Math,1992,45:485-560
[68]Kaiser Gerald.A friendly guide to wavelets.Bostion:MIT Press,1994
[69]C.K.Chui.Wavelets,theory,logarirhms and applications:wavelets analysis and its application.Texas:A&M University Press,1994
[70]Y.Nievergelt.Wavelets made easy.Botston:Chenery,1999
[71]I.Daubechies.Ten Lectures on wavelets.Philadelphia:Society for Industrial and Applied Mathematics,1992
[72]S.Mallat.A wavelet tour of signal Proeessing.SanDiego:Academic Press,1998
[73]W.Sweldens,P.Schroder.Building your own wavelets at home.URL:http://cm.bell-labs.com/who/wire/papers/papers.html#athome,1996
[74]I.Daubechies.Ten Lectures on Wavelets.CBMS-NSF Regional Conf.Series in Appl.Math.,vol.61.Society for Industrial and Applied Mathematics,Philadelphia,PA.1992
[75]A.Grossmann,J.Morlet.Decomposition of hardy functions into square integra- ble wavelets of constant shape.SIAM J.Math.Anal,1984,15(4),723-736
[76]T.G.Marshall.A fast wavelet transform based upon the Euclidean algorithm.Conference on Informa-tion Science and Systems,Johns Hopkins,Maryland,1993
[77]W.Sweldens.The lifting scheme:A construction of Second generation wavelets.SIAM J.Math.Anal,1996,29(2):511-546
[78]W.Sweldens.The lifting scheme:A new philosophy in biorthogonal wavelet constructions,in Proceedings of SPIE,1995,2569:68-79
[79]W.Sweldens.The lifting scheme:A custom-design construction of biorthogonal wavelets.Appl.Comput.Harmon.Anal.,1996,3(2):186-200
[80]W.Sweldens.The lifting scheme:A construction of second generation wavelets.SIAM J.Math.Anal.,1997,29(2):511-546.
[81]W.Sweldens,P.Schroder.Building your own wavelets at home.Computer Graphics,1996:15-87
[82]I.Daubechies,W.Sweldens.Factoring wavelet transforms into lifting steps.Fourier Analysis and Applications,1998,4(3):247-269
[83]H.J.A.M.Heijmans,B.Pesquet-Popescu,G.Piella.Building nonredundant adap- tive wavelets by update lifting.Applied and Computational Harmonic Analysis,2005,8(3):252-281
[84]S.Mallat,W.L.Hwang.Singularity detection and processing with wavelets.IEEE Transactions on Information Theory.1992,38(2):617-643.
[85]Q.Shen,X.Y.Liu.Using modulus maximum pair of wavelet transform to detect spike wave of epileptic EEG.Engineering in Medicine and Biology Society,Proceedings of the 20th Annual International Conference of the IEEE,1998,3:1543-1545
[86]D.L.Donoho,I.M.Johnstone.Threshold selection for wavelet shrinkage of noisy data.New Opportunities for Biomedical Engineers.,Proceedings of the 16th Annual International Conference of the IEEE,1994,1:A24-A25
[87]A.G.Bruce,D.L.Donoho.Denoising and robust nonlinear wavelet analysis.Wavelet Applications Meeting,SPIE Proceedings,1994,2242,Orlando,FL,USA,325-336
[88]H.Krim,S.Mallat,D.L.Donoho.On denoising and best signal representation.IEEE Transactions on Information Theory,1999,45(7):2225-2238
[89]Hierehoren,A.Gustavo.Estimation of Fractal Signals Using Wavelets and Filter Banks.IEEE Transactions on Signal proeessing,1998,46(6):1624-1630
[90]N.Juluri,S.Swarnamani.Improved accuracy of fault diagnosis of rotating machinery using wavelet denoising and feature selection.2003ASME(American Society of Mechanical Engineers) Turbo Expo,Atlanta,Georgia,2003:563-571
[91]I.N.Tansel,C.Mekdeci,C.Mclaughlin.Detection of tool failure in end milling with wavelet transformations and neural networks(WT-NN).Tools Manufact,1995,35(8):1137-1147
[92]何岭松,吴波,等.小波分析及其在设备故障诊断中的应用.华中理工大学学报,1993,21(1):82-87
[93]陈涛,屈梁生,耿中行.小波分析及其在机械诊断中的应用.机械工程学报,1997,33(3):76-79
[94]张梅军,何世平,谭华,徐文明.小波分析在信号预处理中的应用研究.振动、测试与诊断,2000,20(3):211-215
[95]袁小宏,屈梁生.机器振动诊断中信号处理方法的研究.西安交通大学学报,2001,35(7):714-717
[96]周洋,肖蕴诗,何斌,等.基于小波变换的机械振动故障诊断系统的研究.华东交通大学学报,2006,23(4):105-107.
[97]Y.Wu,R.Du.Feature extraction and assessment using wavelet packets for monitoring of machining processes.Mechanical System and Signal Process.1996,10(1):29-53
[98]C.Z.Chen,C.T.Mo.A method for intelligent fault diagnosis of rotating machinery.Digital Signal Processing,2004,14(3):203-17
[99]J.Zou,J.Chen.A comparative study on time-frequency feature of cracked rotor by Winge-ville disrtibution and wavelet transform.Journal of Sound and Vibration,2004,276(1):1-11
[100]S.Y.Cao,X.P.Chen.The Second-generation Wavelet Transform and its Application in Denoising of Seismic Data.Applied Geophysics,2005,2(2):70-75
[101]王娜,贾传荧,贾银山.小波提升格式的研究.信号处理,2003,19(3):269-273
[102]段晨东,何正嘉.基于提升模式的特征小波构造及其应用.振动工程学报,2007,20(1):85-89
[103]段晨东,李凌均,何正嘉.第二代小波变换在旋转机械故障诊断中的应用.机械科学与技术,2004,23(2):224-226
[104]段晨东,何正嘉.第2代小波变换及其在机电设备状态监测中的应用.西安交通大学学报,2003,37(7):695-698
[105]段晨东,何正嘉.第二代小波降噪及其在故障诊断系统中的应用.小型微型计算机系统,2004,25(7):1341-1343
[106]段晨东,何正嘉.一种基于提升小波变换的故障特征提取方法及其应用.振动与冲击,2007,26(2):10-15
[107]张金玉,张优去,谢友柏.时频分析方法在冲击故障早期诊断中的应用研究.振动工程学报,2000,13(2):222-228
[108]赵婷婷,苑惠娟,邹纯宏.基于小波分析的阈值去噪改进算法.理论与方法,2007,26(3):12-13
[109]李玉,于凤芹,杨慧中.基于新的阈值函数的小波阈值去噪方法.江南大学学报,2006,5(4):476-479
[110]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪.西安电子科技大学学报,2004,31(2):296-299
[111]朱云芳,戴朝华,陈维荣.小波消噪阈值选取的一种改进方法.电测与仪表,2005,42(475):4-6
[112]付炜,许山川.一种改进的小波域阈值去噪算法.传感技术学报,2006,19(2):534-537
[113]李庆武,陈小刚.小波阈值去噪的一种改进方法.光学技术,2006,32(6):831-833
[114]杨建国,夏松波,须根法,等.小波分解与重建中产生频率混淆的原因与消除算法.哈尔滨工业大学学报,1999,31(2):61-65
[115]杨建国.基于小波包的滚动轴承故障特征提取.中国机械工程,2002,13(11):935-938
[116]王海清,宋执环,李平.改进小波包分频算法及在故障检测中的应用.浙江大学学报,2001,35(3):307-311
[117]焦李成.神经网络系统理论.西安:西安电子科技大学出版社,1992
[118]胡手仁,神经网络应用技术.北京:国防科技大学出版社,1993
[119]杜华英,赵跃龙.人工神经网络典型模型的比较研究.计算机技术与发展,2006,12(5):97-99
[120]J.J.Hopfield.Neural Networks and physical Systems with Emergent Collective Computational Abilities.Proceedings of the National academy of Science,1982,79:2554-2558
[121]J.J.Hopfield.Neurons With Graded Response Have Collective mutational Properties Like of Two-state Neurons.Proceedings of the National Academy of Science,1984,81:3088-3092
[122]张淑清,靳世久,吕江涛.基于神经网络的旋转机械监测参数的信息融合技术.电子测量与仪器学报,2005,19(3):15-17
[123]黎文锋,邓继忠,沈雷.神经网络在电机故障诊断中的应用综述.电气应用,2006,25(3):45-47
[124]G.A.Carpenter,S.Grosssberg.ART2:Self-organization of Stable Category Recognition Codes for Analog Input Patterns.Applied Optics,1987,26(23):4919-4930
[125]G.A.Carpenter,S.Grosssberg,D.B.Rosen.ART2-A:An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition.Neural Network,1991,4:493-504
[126]S.K.Ganapathy,A.H.Titus.Toward an Analog VLSI Implementation of Adaptive Resonance Theory(ART2).Neural Networks,Proceedings of the International Joint Conference on Volume 2,2003:936-941
[127]R.J.Kuo,J.L.Liao,C.Tu.Integration of ART2 neural network and genetic K- means algorithm for analyzing Web browsing paths in electronic commerce.Decision Support Systems,2005,40:355-374
[128]陈众,蔡自兴,叶青.基于ART2网络的彩色像素分析及其应用.中国图象图形学报,2008,13(4):634-641
[129]李战明,张保梅.基于ART2网络聚类分析的数据融合算法研究.计算机工程与应用,2005,16:182-184
[130]吕秀江,赵研,姚光顺.ART2神经网络调整子系统结构的改进.机械与电子,2001,1:62-64
[131]艾矫燕,朱学锋.ART2网络结构与算法的改进.计算机工程与应用,2003,33:110-112
[132]顾民,葛良全.一种ART2神经网络的改进算法.计算机应用,2007,27(4):945-947
[133]徐艺萍,邓辉文,李阳旭.一种改进的ART2网络学习算法.计算机应用,2006,26(3):659-662
[134]张明书,王伟.并行BP与ART-2复合神经网络诊断应用研究.农机使用与维修,2007,6:23-25
[135]杨尔辅,张振鹏,刘国球,等.应用BP-ART混合神经网络的推进系统状态监控实时系统.推进技术,1999,20(6):10-15
[136]刘康,余玲.BP网络与ART网络的机械设计分类决策及表达对比研究.机械设计,1998,11:14-17
[137]景敏卿,张晓丽.基于ART-并行BP神经网络的柴油机故障诊断研究.机械科学与技术,2007,26(4):412-416
[138]P.Comon.Independent Component Analysis:a new concept? Signal Processing,1994,36(3):287-314
[139]S.Amari,A.Cichocki,H.H.Yang,et al.A new learning algorithm for blind signal separation.Advance in Neural Information Processing Systems 8,MIT Press,Cambridge,1996:757-763
[140]J.F.Cardoso.Blind Signal Separation:Statistical Principles.proc.IEEE,1998,9:2009-2025
[141]J.F.Cardoso.Infomax and Maximum Likelihood for Source Separation.IEEE Leffers on Signal Pmcessing,1997,4(4):112-114
[142]S.Amari.Natural Gradient Works Efficiently in Learning.Neural Computation,1998,10:251-276
[143]T.W.Lee,M.Girolami,T.J.Sejnowski.Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources.Neural Computation,1999,11:417-441
[144]A.Hyvafinen.The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis.Neural Processing,1999,10:1-5
[145]A.J.Bell,T.J.Sejnowski.An Information Maximisation Approach to Blind Separation and Blind Deconvolution.Neural Computation,1995,7:1129-1159
[146]J.F.Cardoso,Super-symetric decomposition of the fourth-order cumulant tensor.Blind identification of moresources thansensors.ICASSP'91,Toronto,Canada,1991:3109-3112
[147]J.C.Pesquet,E.Moreau.Cumulant-Based Independence Measures for Linear Mixtures.IEEE Trnasactions on Information Theory,2001,47(5):1947-1956
[148]J.Vandewalle,L.D.Lathauwer,P.Comon.The Generalized Higher Order SVD and the Oriented Signal to Signal Ratios of Pairs of Signal Tensors.ECCTD'03:European Conference on Circuit Theory and Design,2003,Cracow,Poland:389-392
[149]J.F.Cardoso.High-order contrasts for independent component analysis.Neural Comput,1999,11:157-192
[150]何振亚,杨绿溪,刘琚.一类基于多变量密度估计的盲源分离方法.电子与信息学报,2001,23(4):345-353
[151]张贤达,保铮.盲信号分离.电子学报,2001,29(12A):1766-1771
[152]张贤达.盲信号处理几个关键问题的研究.深圳大学学报理工版,2004,21(3):196-200
[153]岳博,焦李成.一种新的联合对角化算法.电子与信息学报,2003,25(7):892-895
[154]L.Q.Zhang,A.Cichocki,S.Amari.Natural gradient algorithm for blind separa- tion of overdetermined mixture with additive noise.IEEE Signal Processing Letters,1999,6(11):293-295
[155]S.Choi,A.Cichocki,L.Q.Zhang,et al.Approximate maximum likelihood source separation using natural gradient.3rd IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications,Taoyuan,Taiwan:2001:20-23
[156]朱孝龙,张贤达.基于奇异值分解的超定盲信号分离.电子与信息学报,2004,26(3):337-343
[157]T.Yokoi,H.Yanagimoto,S.Omatu.Information filtering using SVD and ICA.Artif Life Robotics,2006,10:116-119
[158]L.Albera,A.Ferreol,P.Comon,et al.Blind Identification of Overdetermined and under-determined Mixtures of sources(BIOME).Linear Algebra Appli- cations,2003
[159]P.Boll,M.Zibulevsky.Underdetermined Blind Source Separation Using Sparse Representations.Signal Process,2001,81:2353-2362
[160]O.Yilmaz,S.Rickard.Blind separation of speech mixtures via time-frequency masking.IEEE Trans.Signal Processing,2004,52(7):1830-1847
[161]P.Comon,M.Rajih.Blind Identification of Under-Determined Mixtures Based on the Characteristic Function.ICASSP'05,Philadelphia,2005:18-23
[162]P.Comon.Blind channel identification and extraction of more sources than sensors.SPIEConference,SanDiego,US,July,1998:2-13
[163]M,Babaie-Zadeh,C.Jutten,A.Mansour.Sparse ICA via Cluster-wise PCA.Neurocomputing,2006,69:1458-1466
[164]Z.S.He,S.L.Xie,Y.L.Fu.Sparsity analysis of signals.Progress in Natural Science,2006,16(8):879-884
[165]N.Trung,A.Belouchrani,K.Abed-Meraim,et al.Separating More Sources Than Sensors Using Time-Frequency Distributions.EURASIP Journal on Applied Signal Processing,2005,17:2828-2847
[166]焦卫东,杨世锡,吴昭同.基于源数估计的旋转机械源盲分离.中国机械工程,2003,14(14):1184-1187
[167]张洪渊,贾鹏,等.确定盲分离中未知信号源个数的奇异值分解法.上海交通大学学报,2001,35(8):1155-1158
[168]M.J.Roan,J.G.Erling,L.H.Sibul.A New,Non-linear,Adaptive,Blind Source Separation Approach to Gear Tooth Failure Detection and Analysis.Mechanical Systems and Signal Processing,2002,16(5):719-740
[169]IEEE Expert staff.Expert Focus-Expert System Tools:The Next Generation,IEEE Expert:Intelligent Systems and Their Applications,1989,4(1):75-76
[170]D.E.Deal,J.G.Chen,J.P.Ignizio.An expert system scheduler:some reflections on expert systems development,Computers and Operations Research,1990,17(6):571-580
[171]L.Davies.From expert to expertise:principles of expert system design.Knowledge based management support systems,1989:341-347
[172]L.T.Xiao,M.Y.Li.The fault diagnosis expert system for automatic control system.Proceedings of the third international conference on Young computer scientists,Beijing,China,1993:2153-2154
[173]A.F.R.Rahman,M.C.Fairhurst.Multiple expert classification:a new metho- dology for parallel decision fusion.Intemation Journal on Document Analysis and Recognition,2000,3:40-55
[174]F.T.S.Chan,K.L.Mak,F.S.M.Law.An expert system for maintenance sche- duling.International Journal of Computer Applications in Technology,1999,12(6):329-338
[175]廖瑞金,姚陈果,孙才新.多专家合作诊断变压器绝缘故障的黑板型专家系统.电工技术学报,2002,17(1):85-90
[176]张俊星,王培昌,雷冰.复杂系统故障诊断中一种多黑板问题求解模型的实现.化工自动化及仪表,2005,32(4):42-45
[177]樊亚文,徐立中.黑板型信息融合专家系统在水环境监测中的应用.水利水文自动化,2006,1:19-22
[178]高举红,徐燕申,单乐生.基于黑板结构CAPP专家系统的研究.天津大学学报,2000,33(3):298-302
[179]陈东锋,雷英杰.基于黑板模型的空袭目标识别专家系统.火力与指挥控制,2006,31(3):55-58
[2]虞和济,韩庆大,李沈,等.设备故障诊断工程.北京:冶金工业出版社,2001
[3]钟秉林,黄仁.机械故障珍断学.北京:机械工业出版社,1997
[4]屈梁生,何正嘉.机械故障诊断学.上海:上海科学技术出版社,1986
[5]盛兆顺,尹琦岭.设备状态监测与故障诊断技术及应用.北京:化学工业出版社,2003
[6]何正嘉,訾艳阳,孟庆丰,等.机械设备非平稳信号的故障诊断原理及应用.北京:高等教育出版社,2001
[7]J.Sohre.Fault Causes and Treats of High Turbo-machinery.ASME petrol- machinery Annual Meeting,1968
[8]D.E.Bently,A.Muszyuska.Detection of Rotor Cracks.Proc.of the Fifteenth Turbo Machinery Symposium,Texas A&M University,1988,129-136
[9]E.Downham.Vibration in Rotating Machinery Malfunction Diagnosis Art and Science.the First Conf.on Vibration in Rotating Machinery,University of Cambridge,1976
[10]R.A.Collacott.Mechanical Condition Monitoring and Fault Diagnosis.London:Chapman and Hall,1977
[11]P.M.McGuire,D.W.Price.The Development and Practical Application of Rotor Dynamic Analysis Techniques for Large Steam Turbine Generators.Int.Vibra- tion in Rotating Machinery,Heriot-Watt University,1988
[12]白木万博,机械振动讲演文集,机械工业部郑州机械研究所振动室,1984
[13]R.Isermannn.Integration of fault detection and diagnosis methods.Preprints IFAC Symposium on Fault Detection,Supervision and Safety for Technical Processes,1994:597-612
[14]D.A.Linkens,H.Wang.Fault diagnosis based on a qualitative bond graph model with emphasis of fault localization.Proceeding of IEE International Conference on Control'94,1994:1329-1334
[15]S.M.A.Cruz,A.J.M.Cardoso.Rotor cage fault diagnosis in three-phase induction motors by extended park's vector approach.Electric Machines and Power Systems,2000,28(3):289-299
[16]W.T.Thomson.Theory of Vibration With Application.Prentice,1972
[17]K.J.Walker,A.Shirkhodaie.Development of a virtual environment for fault diagnosis in rotary machinery.Proceedings of the 33rd Southeastern Symposium on System Theory,IEEE,Piscataway,NJ,USA,2001:99-103
[18]Tim Toutountzakis,M.David.Observations of acoustic emission activity during gear defect diagnosis.NDT & E International,2003,36:471-477
[19]K.R.AlBalushi,B.Samanta.Gear diagnosis using energy-based features of acoustic emission.J Syst Control Engng.2002,216:249-263
[20]S.Edwards,A.W.Lees,M.I.Friawell.Fault Diagnosis of Rotating Machinery.Shock and Vibration Digest,1998,30(1):4-13
[21]M.Tsuchiya,M.Takagi.Vibration and Acoustic fault Diagnosis of Rotational Machine(Application of Wavelets to Diagnostic System for Rotational Machinery).日本机械学会论文集(C编),1998-2,64(618):465-472
[22]史铁林,杨叔子,师汉民,等.机械设备诊断策略的若干问题探讨.华中理工大学学报,1991,8:1-8
[23]盛兆顺,尹琦岭.设备状态检测与故障诊断技术及应用.北京:化学工业出版社,2003
[24]何勇,李增芳.智能化故障诊断技术的研究与应用.浙江大学学报,2003,29(2):119-124
[25]丁康,朱小勇,陈亚华.齿轮箱典型故障振动特征与诊断策略.振动与冲击,2001,20(3)
[26]高金吉.高速涡轮机械振动故障机理及诊断方法的研究[博士学位论文].北京:清华大学,1993
[27]李建国.转子系统稳定性和非线性以及油膜失稳控制研究[博士学位论文].西安:西安交通大学,1992
[28]欧阳平超.风机工况监测与故障诊断研究[博士学位论文].沈阳:东北大学,2004
[29]陈长征.旋转机械故障智能诊断方法研究[博士学位论文].徐州:中国矿业大学,1998
[30]许伯强,李和明,孙丽玲,等.小波分析应用于笼型异步电动机转子断条在线检测初探.中国电机工程学报,2006,21(11):24-28
[31]马宏忠,李训铭,王刘芳.高压异步电机转子绕组故障诊断系统的研究.高电压技术,2004,30(4):31-33
[32]赵桂林,詹渝明,陈明涛.基于傅里叶变换的异步电机转子故障检测.东北电力技术,2004,7:9-11
[33]刘同明,夏祖勋,解洪成.数据融合技术及其应用.国防工业出版社,1998
[34]杨靖宇.战场数据融合技术.兵器工业出版社,1994
[35]滕召胜,罗隆福,章调生.智能检测系统与数据融合.机械工业出版社,2000
[36]P.Grossmann.Multisensor data fusion.GEC Journal of Technology,1998.15(1):27-37
[37]E.L.Waltz,M.Buede.Data fusion and decision support for commmad and control.IEEE trans.on syst.man and cyberm,1986,16(6):865-879
[38]E.L.Waltz,J.Linas.Multi-sensor data fusion.Artech,Norwood,Masschusstts,1990:175-85
[39]R.C.Luo,M.G.Kay.Multi-sensor Integration and fusion in intelligent systems.IEEE Transaction on systems,Man and cybernetics,1989,19(5):901-931
[40]R.C.Luo,G.Michael.Multisensor integration and fusion for intelligent machines and systems.Norwood N J:Ablex Publishing Corporation,1995:1-19
[41]D.L.Hall.Mathematical Techniques in Multi-Sensor Data Fusion.Artech House,1992:10-15
[42]R.Mamlook,W.E.Thompson.A Multi-sensor Fusion Algorithm for Multi-target.Multi-background Classification.SPIE 1992,1699:72-82
[43]V.Gorodetsky,O.Karsaev,V.Samoilov.Multi-Agent Data and Information Fusion:Architecture,Methodology,Technology and Sotware Tool.Data Fusion for Situation Monitoring,Incident Detection,Alert and Response Management,ISO Press,2005:308-339
[44]Y.Shi,C.Z.Hu,H.M.Tan.Multisensor battlefield monitoring system and its signal processing simulation.International Symposium on Test and Measurement,Proceedings,International Academic Publishers,1997.6:474-477
[45]戴箔.C~3I中的多传感器数据融合技术.兵工学报,1998,4:356-360
[46]赵宗贵,耿立贤,孔祥忠.三军联合作战C~3I中的数据融合.现代电子工程,1993:16-26
[47]杨杰,陆正刚,黄欣.基于多传感器数据融合的目标识别和跟踪.上海交通大学学报,1999,9:1107-1110
[48]郁文贤.多传感器信息融合技术评述.国防科技大学学报,1994,3:1-11
[49]袁小勇,屈梁生.机械故障诊断中的信息融合利用问题研究.振动、测试与诊断,1999,9:187-192
[50]张彦铎,姜兴渭.多传感器信息融合及在智能故障诊断中的应用.传感器技术,1999,2:18-22
[51]晋风华,李录平.多传感器信息融合技术及其在旋转机械振动故障诊断中的应用.热力发电,2004,5:45-48
[52]谭逢友,卢宏伟等.信息融合技术在机械故障诊断中的应用.重庆大学学报,2006,29(1):15-18
[53]孙卫祥,陈进,伍星等.基于信息融合的支撑座早期松动故障诊断.上海交通大学学报,2006,2:239-242
[54]R.R.Yager,D.P.Filev.Induced ordered weighted averaging operators.IEEE Transactions on Systems,Man and Cybernetics,Part B,1999,29(2):141-150
[55]李宏,刘江涛,安玮,等.主观Bayes方法与神经网络相结合的多传感器数据融合空间点目标识别方法.红外与毫米波学报,1997,16(6):448-454.
[56]P.L.Bogler.Dempster-Shafer Reasoning with Applications to Multi-sensor Target Identification Systems.IEEE Transactions on systems,Man,and cybernetics,1989,17(6):901-930
[57]H.M.Ertunc,K.A.Loparo.A decision fusion algorithm for tool wear condition monitoring in drilling.International Journal of Machine Tools & Manufacture,2001,41:1347-1362
[58]E.Blasch.Information Fusion for Decision Making Designing Realizable Information Fusion Systems.Data Fusion for Situation Monitoring,Incident Detection,Alert and Response Management,ISO Press,2005:3-22
[59]H.Liang.Small target detection in multisensor system based on Demopster- Shafter evidence theory.The International Society for Optical Engineering,2001,9:272-279
[60]G.Niu,T.Han,B.S.Yang,et al.Multi-agent decision fusion for motor fault diagnosis.Mechanical Systems and Signal Processing,2007,21:1285-1299
[61]B.S.Yang,J.K.Kim.Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals.Mechanical Systems and Signal Processing,2006,20(2):403-420
[62]李斌,章卫国,宁东方,等.基于神经网络信息融合的智能故障诊断方法.计算 机仿真,2008,25(6):35-37
[63]孙延奎.小波分析及其应用.北京:机械工业出版社,2005
[64]杨建国.小波分析及其工程应用.北京:机械工业出版社,2005
[65]S.Mallat.A theory for multi-resolution signal decomposition:the wavelet representation.Pattern Analysis and Machine Intelligenee,1989,11(7):674-693
[66]I.Daubechies.Orthonomal bases of compactly supported wavelets.Comm,pure Appl.Math,1988,41:909-996
[67]A.Cohen.I.Daubechies.J.Feauveau.Bi-orthogonal bases of compaetly suppor- ted wavelets.Comm.Pure Appl.Math,1992,45:485-560
[68]Kaiser Gerald.A friendly guide to wavelets.Bostion:MIT Press,1994
[69]C.K.Chui.Wavelets,theory,logarirhms and applications:wavelets analysis and its application.Texas:A&M University Press,1994
[70]Y.Nievergelt.Wavelets made easy.Botston:Chenery,1999
[71]I.Daubechies.Ten Lectures on wavelets.Philadelphia:Society for Industrial and Applied Mathematics,1992
[72]S.Mallat.A wavelet tour of signal Proeessing.SanDiego:Academic Press,1998
[73]W.Sweldens,P.Schroder.Building your own wavelets at home.URL:http://cm.bell-labs.com/who/wire/papers/papers.html#athome,1996
[74]I.Daubechies.Ten Lectures on Wavelets.CBMS-NSF Regional Conf.Series in Appl.Math.,vol.61.Society for Industrial and Applied Mathematics,Philadelphia,PA.1992
[75]A.Grossmann,J.Morlet.Decomposition of hardy functions into square integra- ble wavelets of constant shape.SIAM J.Math.Anal,1984,15(4),723-736
[76]T.G.Marshall.A fast wavelet transform based upon the Euclidean algorithm.Conference on Informa-tion Science and Systems,Johns Hopkins,Maryland,1993
[77]W.Sweldens.The lifting scheme:A construction of Second generation wavelets.SIAM J.Math.Anal,1996,29(2):511-546
[78]W.Sweldens.The lifting scheme:A new philosophy in biorthogonal wavelet constructions,in Proceedings of SPIE,1995,2569:68-79
[79]W.Sweldens.The lifting scheme:A custom-design construction of biorthogonal wavelets.Appl.Comput.Harmon.Anal.,1996,3(2):186-200
[80]W.Sweldens.The lifting scheme:A construction of second generation wavelets.SIAM J.Math.Anal.,1997,29(2):511-546.
[81]W.Sweldens,P.Schroder.Building your own wavelets at home.Computer Graphics,1996:15-87
[82]I.Daubechies,W.Sweldens.Factoring wavelet transforms into lifting steps.Fourier Analysis and Applications,1998,4(3):247-269
[83]H.J.A.M.Heijmans,B.Pesquet-Popescu,G.Piella.Building nonredundant adap- tive wavelets by update lifting.Applied and Computational Harmonic Analysis,2005,8(3):252-281
[84]S.Mallat,W.L.Hwang.Singularity detection and processing with wavelets.IEEE Transactions on Information Theory.1992,38(2):617-643.
[85]Q.Shen,X.Y.Liu.Using modulus maximum pair of wavelet transform to detect spike wave of epileptic EEG.Engineering in Medicine and Biology Society,Proceedings of the 20th Annual International Conference of the IEEE,1998,3:1543-1545
[86]D.L.Donoho,I.M.Johnstone.Threshold selection for wavelet shrinkage of noisy data.New Opportunities for Biomedical Engineers.,Proceedings of the 16th Annual International Conference of the IEEE,1994,1:A24-A25
[87]A.G.Bruce,D.L.Donoho.Denoising and robust nonlinear wavelet analysis.Wavelet Applications Meeting,SPIE Proceedings,1994,2242,Orlando,FL,USA,325-336
[88]H.Krim,S.Mallat,D.L.Donoho.On denoising and best signal representation.IEEE Transactions on Information Theory,1999,45(7):2225-2238
[89]Hierehoren,A.Gustavo.Estimation of Fractal Signals Using Wavelets and Filter Banks.IEEE Transactions on Signal proeessing,1998,46(6):1624-1630
[90]N.Juluri,S.Swarnamani.Improved accuracy of fault diagnosis of rotating machinery using wavelet denoising and feature selection.2003ASME(American Society of Mechanical Engineers) Turbo Expo,Atlanta,Georgia,2003:563-571
[91]I.N.Tansel,C.Mekdeci,C.Mclaughlin.Detection of tool failure in end milling with wavelet transformations and neural networks(WT-NN).Tools Manufact,1995,35(8):1137-1147
[92]何岭松,吴波,等.小波分析及其在设备故障诊断中的应用.华中理工大学学报,1993,21(1):82-87
[93]陈涛,屈梁生,耿中行.小波分析及其在机械诊断中的应用.机械工程学报,1997,33(3):76-79
[94]张梅军,何世平,谭华,徐文明.小波分析在信号预处理中的应用研究.振动、测试与诊断,2000,20(3):211-215
[95]袁小宏,屈梁生.机器振动诊断中信号处理方法的研究.西安交通大学学报,2001,35(7):714-717
[96]周洋,肖蕴诗,何斌,等.基于小波变换的机械振动故障诊断系统的研究.华东交通大学学报,2006,23(4):105-107.
[97]Y.Wu,R.Du.Feature extraction and assessment using wavelet packets for monitoring of machining processes.Mechanical System and Signal Process.1996,10(1):29-53
[98]C.Z.Chen,C.T.Mo.A method for intelligent fault diagnosis of rotating machinery.Digital Signal Processing,2004,14(3):203-17
[99]J.Zou,J.Chen.A comparative study on time-frequency feature of cracked rotor by Winge-ville disrtibution and wavelet transform.Journal of Sound and Vibration,2004,276(1):1-11
[100]S.Y.Cao,X.P.Chen.The Second-generation Wavelet Transform and its Application in Denoising of Seismic Data.Applied Geophysics,2005,2(2):70-75
[101]王娜,贾传荧,贾银山.小波提升格式的研究.信号处理,2003,19(3):269-273
[102]段晨东,何正嘉.基于提升模式的特征小波构造及其应用.振动工程学报,2007,20(1):85-89
[103]段晨东,李凌均,何正嘉.第二代小波变换在旋转机械故障诊断中的应用.机械科学与技术,2004,23(2):224-226
[104]段晨东,何正嘉.第2代小波变换及其在机电设备状态监测中的应用.西安交通大学学报,2003,37(7):695-698
[105]段晨东,何正嘉.第二代小波降噪及其在故障诊断系统中的应用.小型微型计算机系统,2004,25(7):1341-1343
[106]段晨东,何正嘉.一种基于提升小波变换的故障特征提取方法及其应用.振动与冲击,2007,26(2):10-15
[107]张金玉,张优去,谢友柏.时频分析方法在冲击故障早期诊断中的应用研究.振动工程学报,2000,13(2):222-228
[108]赵婷婷,苑惠娟,邹纯宏.基于小波分析的阈值去噪改进算法.理论与方法,2007,26(3):12-13
[109]李玉,于凤芹,杨慧中.基于新的阈值函数的小波阈值去噪方法.江南大学学报,2006,5(4):476-479
[110]张维强,宋国乡.基于一种新的阈值函数的小波域信号去噪.西安电子科技大学学报,2004,31(2):296-299
[111]朱云芳,戴朝华,陈维荣.小波消噪阈值选取的一种改进方法.电测与仪表,2005,42(475):4-6
[112]付炜,许山川.一种改进的小波域阈值去噪算法.传感技术学报,2006,19(2):534-537
[113]李庆武,陈小刚.小波阈值去噪的一种改进方法.光学技术,2006,32(6):831-833
[114]杨建国,夏松波,须根法,等.小波分解与重建中产生频率混淆的原因与消除算法.哈尔滨工业大学学报,1999,31(2):61-65
[115]杨建国.基于小波包的滚动轴承故障特征提取.中国机械工程,2002,13(11):935-938
[116]王海清,宋执环,李平.改进小波包分频算法及在故障检测中的应用.浙江大学学报,2001,35(3):307-311
[117]焦李成.神经网络系统理论.西安:西安电子科技大学出版社,1992
[118]胡手仁,神经网络应用技术.北京:国防科技大学出版社,1993
[119]杜华英,赵跃龙.人工神经网络典型模型的比较研究.计算机技术与发展,2006,12(5):97-99
[120]J.J.Hopfield.Neural Networks and physical Systems with Emergent Collective Computational Abilities.Proceedings of the National academy of Science,1982,79:2554-2558
[121]J.J.Hopfield.Neurons With Graded Response Have Collective mutational Properties Like of Two-state Neurons.Proceedings of the National Academy of Science,1984,81:3088-3092
[122]张淑清,靳世久,吕江涛.基于神经网络的旋转机械监测参数的信息融合技术.电子测量与仪器学报,2005,19(3):15-17
[123]黎文锋,邓继忠,沈雷.神经网络在电机故障诊断中的应用综述.电气应用,2006,25(3):45-47
[124]G.A.Carpenter,S.Grosssberg.ART2:Self-organization of Stable Category Recognition Codes for Analog Input Patterns.Applied Optics,1987,26(23):4919-4930
[125]G.A.Carpenter,S.Grosssberg,D.B.Rosen.ART2-A:An Adaptive Resonance Algorithm for Rapid Category Learning and Recognition.Neural Network,1991,4:493-504
[126]S.K.Ganapathy,A.H.Titus.Toward an Analog VLSI Implementation of Adaptive Resonance Theory(ART2).Neural Networks,Proceedings of the International Joint Conference on Volume 2,2003:936-941
[127]R.J.Kuo,J.L.Liao,C.Tu.Integration of ART2 neural network and genetic K- means algorithm for analyzing Web browsing paths in electronic commerce.Decision Support Systems,2005,40:355-374
[128]陈众,蔡自兴,叶青.基于ART2网络的彩色像素分析及其应用.中国图象图形学报,2008,13(4):634-641
[129]李战明,张保梅.基于ART2网络聚类分析的数据融合算法研究.计算机工程与应用,2005,16:182-184
[130]吕秀江,赵研,姚光顺.ART2神经网络调整子系统结构的改进.机械与电子,2001,1:62-64
[131]艾矫燕,朱学锋.ART2网络结构与算法的改进.计算机工程与应用,2003,33:110-112
[132]顾民,葛良全.一种ART2神经网络的改进算法.计算机应用,2007,27(4):945-947
[133]徐艺萍,邓辉文,李阳旭.一种改进的ART2网络学习算法.计算机应用,2006,26(3):659-662
[134]张明书,王伟.并行BP与ART-2复合神经网络诊断应用研究.农机使用与维修,2007,6:23-25
[135]杨尔辅,张振鹏,刘国球,等.应用BP-ART混合神经网络的推进系统状态监控实时系统.推进技术,1999,20(6):10-15
[136]刘康,余玲.BP网络与ART网络的机械设计分类决策及表达对比研究.机械设计,1998,11:14-17
[137]景敏卿,张晓丽.基于ART-并行BP神经网络的柴油机故障诊断研究.机械科学与技术,2007,26(4):412-416
[138]P.Comon.Independent Component Analysis:a new concept? Signal Processing,1994,36(3):287-314
[139]S.Amari,A.Cichocki,H.H.Yang,et al.A new learning algorithm for blind signal separation.Advance in Neural Information Processing Systems 8,MIT Press,Cambridge,1996:757-763
[140]J.F.Cardoso.Blind Signal Separation:Statistical Principles.proc.IEEE,1998,9:2009-2025
[141]J.F.Cardoso.Infomax and Maximum Likelihood for Source Separation.IEEE Leffers on Signal Pmcessing,1997,4(4):112-114
[142]S.Amari.Natural Gradient Works Efficiently in Learning.Neural Computation,1998,10:251-276
[143]T.W.Lee,M.Girolami,T.J.Sejnowski.Independent Component Analysis Using an Extended Infomax Algorithm for Mixed Subgaussian and Supergaussian Sources.Neural Computation,1999,11:417-441
[144]A.Hyvafinen.The Fixed-Point Algorithm and Maximum Likelihood Estimation for Independent Component Analysis.Neural Processing,1999,10:1-5
[145]A.J.Bell,T.J.Sejnowski.An Information Maximisation Approach to Blind Separation and Blind Deconvolution.Neural Computation,1995,7:1129-1159
[146]J.F.Cardoso,Super-symetric decomposition of the fourth-order cumulant tensor.Blind identification of moresources thansensors.ICASSP'91,Toronto,Canada,1991:3109-3112
[147]J.C.Pesquet,E.Moreau.Cumulant-Based Independence Measures for Linear Mixtures.IEEE Trnasactions on Information Theory,2001,47(5):1947-1956
[148]J.Vandewalle,L.D.Lathauwer,P.Comon.The Generalized Higher Order SVD and the Oriented Signal to Signal Ratios of Pairs of Signal Tensors.ECCTD'03:European Conference on Circuit Theory and Design,2003,Cracow,Poland:389-392
[149]J.F.Cardoso.High-order contrasts for independent component analysis.Neural Comput,1999,11:157-192
[150]何振亚,杨绿溪,刘琚.一类基于多变量密度估计的盲源分离方法.电子与信息学报,2001,23(4):345-353
[151]张贤达,保铮.盲信号分离.电子学报,2001,29(12A):1766-1771
[152]张贤达.盲信号处理几个关键问题的研究.深圳大学学报理工版,2004,21(3):196-200
[153]岳博,焦李成.一种新的联合对角化算法.电子与信息学报,2003,25(7):892-895
[154]L.Q.Zhang,A.Cichocki,S.Amari.Natural gradient algorithm for blind separa- tion of overdetermined mixture with additive noise.IEEE Signal Processing Letters,1999,6(11):293-295
[155]S.Choi,A.Cichocki,L.Q.Zhang,et al.Approximate maximum likelihood source separation using natural gradient.3rd IEEE Signal Processing Workshop on Signal Processing Advances in Wireless Communications,Taoyuan,Taiwan:2001:20-23
[156]朱孝龙,张贤达.基于奇异值分解的超定盲信号分离.电子与信息学报,2004,26(3):337-343
[157]T.Yokoi,H.Yanagimoto,S.Omatu.Information filtering using SVD and ICA.Artif Life Robotics,2006,10:116-119
[158]L.Albera,A.Ferreol,P.Comon,et al.Blind Identification of Overdetermined and under-determined Mixtures of sources(BIOME).Linear Algebra Appli- cations,2003
[159]P.Boll,M.Zibulevsky.Underdetermined Blind Source Separation Using Sparse Representations.Signal Process,2001,81:2353-2362
[160]O.Yilmaz,S.Rickard.Blind separation of speech mixtures via time-frequency masking.IEEE Trans.Signal Processing,2004,52(7):1830-1847
[161]P.Comon,M.Rajih.Blind Identification of Under-Determined Mixtures Based on the Characteristic Function.ICASSP'05,Philadelphia,2005:18-23
[162]P.Comon.Blind channel identification and extraction of more sources than sensors.SPIEConference,SanDiego,US,July,1998:2-13
[163]M,Babaie-Zadeh,C.Jutten,A.Mansour.Sparse ICA via Cluster-wise PCA.Neurocomputing,2006,69:1458-1466
[164]Z.S.He,S.L.Xie,Y.L.Fu.Sparsity analysis of signals.Progress in Natural Science,2006,16(8):879-884
[165]N.Trung,A.Belouchrani,K.Abed-Meraim,et al.Separating More Sources Than Sensors Using Time-Frequency Distributions.EURASIP Journal on Applied Signal Processing,2005,17:2828-2847
[166]焦卫东,杨世锡,吴昭同.基于源数估计的旋转机械源盲分离.中国机械工程,2003,14(14):1184-1187
[167]张洪渊,贾鹏,等.确定盲分离中未知信号源个数的奇异值分解法.上海交通大学学报,2001,35(8):1155-1158
[168]M.J.Roan,J.G.Erling,L.H.Sibul.A New,Non-linear,Adaptive,Blind Source Separation Approach to Gear Tooth Failure Detection and Analysis.Mechanical Systems and Signal Processing,2002,16(5):719-740
[169]IEEE Expert staff.Expert Focus-Expert System Tools:The Next Generation,IEEE Expert:Intelligent Systems and Their Applications,1989,4(1):75-76
[170]D.E.Deal,J.G.Chen,J.P.Ignizio.An expert system scheduler:some reflections on expert systems development,Computers and Operations Research,1990,17(6):571-580
[171]L.Davies.From expert to expertise:principles of expert system design.Knowledge based management support systems,1989:341-347
[172]L.T.Xiao,M.Y.Li.The fault diagnosis expert system for automatic control system.Proceedings of the third international conference on Young computer scientists,Beijing,China,1993:2153-2154
[173]A.F.R.Rahman,M.C.Fairhurst.Multiple expert classification:a new metho- dology for parallel decision fusion.Intemation Journal on Document Analysis and Recognition,2000,3:40-55
[174]F.T.S.Chan,K.L.Mak,F.S.M.Law.An expert system for maintenance sche- duling.International Journal of Computer Applications in Technology,1999,12(6):329-338
[175]廖瑞金,姚陈果,孙才新.多专家合作诊断变压器绝缘故障的黑板型专家系统.电工技术学报,2002,17(1):85-90
[176]张俊星,王培昌,雷冰.复杂系统故障诊断中一种多黑板问题求解模型的实现.化工自动化及仪表,2005,32(4):42-45
[177]樊亚文,徐立中.黑板型信息融合专家系统在水环境监测中的应用.水利水文自动化,2006,1:19-22
[178]高举红,徐燕申,单乐生.基于黑板结构CAPP专家系统的研究.天津大学学报,2000,33(3):298-302
[179]陈东锋,雷英杰.基于黑板模型的空袭目标识别专家系统.火力与指挥控制,2006,31(3):55-58
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