柴油机智能故障诊断系统研究
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摘要
柴油机是一种复杂的往复式动力机械,由于结构复杂,运动部件多,使柴油机的故障诊断十分困难,对柴油机故障诊断技术的研究一直是人们潜心研究的一个难题。柴油机故障诊断技术是利用柴油机的状态信息和历史状况,通过分析和处理来定量识别实时技术状态,并预测异常故障的未来技术状态的一门建立在多学科基础上的综合技术。采用何种理论方法能够更有效更快捷地提取柴油机的故障特征信息,以及如何构造故障判别关系是柴油机智能故障振动诊断技术研究的核心工作。目前对发动机工作状态的监测的手段包括性能参数监测、振动噪声监测以及油液分析技术等。然而单个的监测手段缺乏对多源多维信息的协同处理和综合利用,因而在准确性、可靠性和实用性等方面存在着不同程度的缺陷。本文提出采用信息融合技术,将发动机的性能参数、振动噪声参数以及油液分析参数等多维信息进行融合,并进行相应的处理,以便更准确、可靠地掌握军车发动机技术状态。
本文首先叙述了论文的研究目的和意义,综述了柴油机智能故障诊断技术的研究现状、存在问题和发展趋势,分析了柴油机智能故障诊断技术的主要研究内容;然后以康明斯柴油机状态监测整体台架实验系统为基础,对康明斯柴油发动机进行了故障诊断实验测试系统的理论设计研究,具体设计并实施了康明斯柴油发动机的测试实验,采集了大量的测试数据;其次在实验数据分析的基础上,选择了铁谱、振动和性能参数中灵敏度高,与发动机的状态有较好对应关系的特征参数作为评判模型的各因素。确定了每个因素在综合评判中的权重,用数理统计和模糊数学理论建立了各因素对发动机不同状态的隶属函数。最终为发动机状态评判建立了基于铁谱、振动和性能参数的模糊推理综合决策的模型。用具体的数据检验了该模型的实用性及准确性。同时将神经网络理论与模糊理论相结合,应用BP神经网络改进算法以及Elman过程神经网络学习算法,进行了基于神经网络的发动机状态分类器的设计。最后,将模糊聚类分析技术与人工神经网络相结合,研究了柴油机故障模糊聚类分析诊断过程,设计了基于粗糙集理论的柴油机故障神经网络诊断系统的开发。
A diesel engine is a type of complicated reciprocating power machine. Its complicated structure and components make fault diagnosis very difficult. Therefore, study in diesel engine diagnostic techniques have being concentrated on. Diesel engine diagnostic techniques, based on many subjects, are synthesis techniques, which can help identify the real-time technique state of the recognition unit and predict the abnormal breakdown through analyzing and processing. How to collect the fault tag information more effectively and quickly and how to establish the fault criterion have become the essential parts of the research work.
At present, performance parameter, vibration noise parameter and oil analysis parameter are used to monitor the engine conditions. However, each individual monitoring method can’t manage or utilize information from multiple sources and dimensions synthetically. As a result, any motoring means, to some degree, lacks accuracy, reliability and practicability. In this paper, the writer just suggests that the engine performance parameter, vibration noise parameter and oil analysis should be taken into consideration together and then corresponding measures be adopted in order to bring the engines into full play.
The paper begins with an introduction to the purpose of the present study and its significance. And then it provides an overview of current situations, existing problems and developing tendency of diesel engine diagnostic techniques. The paper goes on with the primary coverage of the research work. After that, Cummins Diesels are taken as examples. Based on the bench testing system about condition monitoring, design theory research on fault diagnosis testing system has been done. Testing experiments on Cummins Diesel engines have been made, during which quantities of data have been collected. Then, based on the analysis of the test data, characteristic parameters with high sensitivity are chosen as main factors to score models, which have got better corresponding relationships with engine conditions. The weight of each factor is fixed in the comprehensive evaluation and membership function between each factor and different engine state is built on the basis of mathematics statistics and fuzzy theory. Thus, based on integrated decisions in fuzzy inference, models to evaluate engine conditions are set up. The accuracy and practicability of the model is tested with specific data. Meanwhile, neural network theory is combined with fuzzy theory; algorithm is improved with the application of BP neural network and Elman neural network. Engine condition classifier design is just based on neural networks. At the end of the paler, with the combination of fuzzy clustering analysis techniques with artificial neural networks ,the diesel engine fault diagnosis on fuzzy clustering analysis is studied and the fault diagnosis system on neural network is developed which is based on the rough set theory.
本文首先叙述了论文的研究目的和意义,综述了柴油机智能故障诊断技术的研究现状、存在问题和发展趋势,分析了柴油机智能故障诊断技术的主要研究内容;然后以康明斯柴油机状态监测整体台架实验系统为基础,对康明斯柴油发动机进行了故障诊断实验测试系统的理论设计研究,具体设计并实施了康明斯柴油发动机的测试实验,采集了大量的测试数据;其次在实验数据分析的基础上,选择了铁谱、振动和性能参数中灵敏度高,与发动机的状态有较好对应关系的特征参数作为评判模型的各因素。确定了每个因素在综合评判中的权重,用数理统计和模糊数学理论建立了各因素对发动机不同状态的隶属函数。最终为发动机状态评判建立了基于铁谱、振动和性能参数的模糊推理综合决策的模型。用具体的数据检验了该模型的实用性及准确性。同时将神经网络理论与模糊理论相结合,应用BP神经网络改进算法以及Elman过程神经网络学习算法,进行了基于神经网络的发动机状态分类器的设计。最后,将模糊聚类分析技术与人工神经网络相结合,研究了柴油机故障模糊聚类分析诊断过程,设计了基于粗糙集理论的柴油机故障神经网络诊断系统的开发。
A diesel engine is a type of complicated reciprocating power machine. Its complicated structure and components make fault diagnosis very difficult. Therefore, study in diesel engine diagnostic techniques have being concentrated on. Diesel engine diagnostic techniques, based on many subjects, are synthesis techniques, which can help identify the real-time technique state of the recognition unit and predict the abnormal breakdown through analyzing and processing. How to collect the fault tag information more effectively and quickly and how to establish the fault criterion have become the essential parts of the research work.
At present, performance parameter, vibration noise parameter and oil analysis parameter are used to monitor the engine conditions. However, each individual monitoring method can’t manage or utilize information from multiple sources and dimensions synthetically. As a result, any motoring means, to some degree, lacks accuracy, reliability and practicability. In this paper, the writer just suggests that the engine performance parameter, vibration noise parameter and oil analysis should be taken into consideration together and then corresponding measures be adopted in order to bring the engines into full play.
The paper begins with an introduction to the purpose of the present study and its significance. And then it provides an overview of current situations, existing problems and developing tendency of diesel engine diagnostic techniques. The paper goes on with the primary coverage of the research work. After that, Cummins Diesels are taken as examples. Based on the bench testing system about condition monitoring, design theory research on fault diagnosis testing system has been done. Testing experiments on Cummins Diesel engines have been made, during which quantities of data have been collected. Then, based on the analysis of the test data, characteristic parameters with high sensitivity are chosen as main factors to score models, which have got better corresponding relationships with engine conditions. The weight of each factor is fixed in the comprehensive evaluation and membership function between each factor and different engine state is built on the basis of mathematics statistics and fuzzy theory. Thus, based on integrated decisions in fuzzy inference, models to evaluate engine conditions are set up. The accuracy and practicability of the model is tested with specific data. Meanwhile, neural network theory is combined with fuzzy theory; algorithm is improved with the application of BP neural network and Elman neural network. Engine condition classifier design is just based on neural networks. At the end of the paler, with the combination of fuzzy clustering analysis techniques with artificial neural networks ,the diesel engine fault diagnosis on fuzzy clustering analysis is studied and the fault diagnosis system on neural network is developed which is based on the rough set theory.
引文
[1]曹龙汉.柴油机智能化故障诊断技术.北京:国防工业出版社. 2005.2~25
[2]王晓俊.康明斯发动机磨合期状态监测方法的研究[硕士学位论文] :徐州:徐州空军学院,2005
[3]邹小明,屠卫星.汽车检测诊断技术.北京:人民交通出版社.,2005.57~108
[4]王江萍.机械设备故障诊断技术及应用.西安:西北工业大学出版社,2001.35~40
[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,2003.1～7
[6]刘建敏,乔新勇,安钢.柴油机技术状况的评估参数研究.车用发动机,2004(1):6～7
[7]任国全,张培林张英堂.装备油液智能监控原理.北京:国防工业出版社,2006.10~25
[8]李柱国.机械润滑与诊断.北京:化学工业出版社,2005.25~45
[9]宋建社,曹小平等.装备维修信息化工程.北京:国防工业出版社,2005,45~50
[10]杨军,冯振声,黄考利等.装备智能故障诊断技术.北京:国防工业出版社. 2004. 2~28
[11]杨其明,严新平,贺石中.油液监测分析现场实用技术.北京:机械工业出版社,2006.153~176
[12]金敬强,武富春.信息融合技术的发展现状与展望.电脑开发与应用,2006,01
[13]范新南,苏丽媛,郭建甲.多传感器信息融合综述.河海大学常州分校学报,2005,(01)
[14]何友,关欣,王国宏.多传感器信息融合研究进展与展望.宇航学报,2005,(04):15~18
[15]张菊秀.多传感器信息融合技术和发展.电子世界,2005,(04):27~32
[16]郭惠勇.多传感器信息融合技术的研究与进展.中国科学基金,2005,01
[17]周芳,韩立岩.多传感器信息融合技术综述.遥测遥控,2006,03
[18]郭戈,罗志刚.多传感器数据融合方法的研究与进展.机电一体化,2003,05:12~17
[19]潘泉,于昕,程永梅,张洪才.信息融合理论的基本方法与进展.自动化学报,2003,4(29):599~615
[20]范新南,苏丽媛,郭建甲.多传感器信息融合综述.河海大学常州分校学报,2005,(01) :35~45
[21]燕颢.信息融合几种算法研究. [硕士学位论文].南京:南京理工大学,2003
[22]杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社,2005.45~55 [23 ]施小成,谢睿,丁宗华.一种基于模糊神经网络的信息融合技术.自动化技术与应用,2006,01
[24]戴亚平,刘征,郁光辉.多传感器数据融合理论及应用.北京;北京理工大学出版社,2004.123~145
[25]王新洲,史文中,王树良.模糊空间信息处理.武汉:武汉大学出版社,2003.88~89
[26]史文中.空间数据与空间分析不确定性原理.北京:科学出版社,2005.51~55
[27]戴亚平,刘征,郁光辉.多传感器数据融合理论及应用.北京;北京理工大学出版社,2004.123~145
[28]王新洲,史文中,王树良.模糊空间信息处理.武汉:武汉大学出版社,2003.88~89
[29]徐晓滨.多源信息的描述与建模方法研究:[硕士学位论文] .郑州:河南大学,2006.
[30]张远.基于信息融合技术的故障诊断模型和方法研究:[硕士学位论文].武汉:中南大学,2003
[31]蔡兴国,马平.基于信息融合的并发故障诊断的研究.中国电机工程学报,2003,5(23):107~111
[32]秦志强.数据融合技术及其应用.兵工自动化.,2003,5(23):25~28
[33]林雪霞.信息融合技术在舰艇作战系统中的应用[J]舰船电子工程,2003,(04) :54~57
[34]朱玉鹏.信息融合的神经网络原理及应用研究: [硕士学位论文].长沙:国防科技大学,2004
[35]马平,吕锋,杜海莲.多传感器信息融合基本原理及应用.控制工程,2006,01
[36] A. Jain and A. Ross.“Information fusion in biometrics”. Pattern Recognition Letters,24:2115~2125,2003
[37] B. V. Dasarathy.“Intrusion detection”. Information Fusion, 4:243~245,2005
[38] Shozo Mori, Random Sets in Data Fusion: Formalism to New Algorethms,ISIF, 2004,24~31
[39] R. Mahler.“Statistics 101”for multisensor, miltitarget data fusion. IEEE AES Mag., Part 2: Tutorials, 19(1):53~64,2004
[40] R. Mahler. Multisensor-multitarget sensor management-a unified Bayesian approach. SPIE Proc. Vol. 5096,222~233,2003
[41]王海涛. RBF神经网络与证据理论相结合的特征集信息融合方法研究: [硕士学位论文].哈尔滨:哈尔滨工程大学,2004
[42]赵新宇.基于多传感器信息融合技术的车辆运行状况监测理论与方法研究: [硕士学位论文].长沙:湖南大学,2005
[43] Robb Wilocx, Mark Burrows, Sujit Ghosh and Bilal M. Ayyub, Risk-based technology method for the safety assessment of marin compressed natural gas furel system, Marin Technology ,2001,Vol.38 No3,pp:193~207
[44] Zheng, H.Z. Li, and X. Chen, Gear fault diagnosis based on continuous wavelet transform. Mechanical System and Signal Processing, 2002,16(2-3):447~457
[45] Nikolaou, N.G. and I.A.Antoniadis, Rolling element bearing fault diagnosis using wavelet packets. Ndt& E International,2002,35(3):197~205
[46] Hai-Wen Chen and Teresa Olson, Integrated Spatio-temopral Mutiple Sensor System Design, SPIE, 2002, Vol: 4731, 204~215
[47] Jones N B, Li Yu-hua , Review of conditon monitoring and fauit diagnosis for diesel engines. University of Leicester, Leaf Coppin Publ Ltd,2000,6:267~292
[48] Twiddle, J.A, and N.B Jones, Fuzzy model–based condition monitoring and fault diagnosis of a diesel engine cooling system, Procceedings of the Institution of Mechanical Engineers Part 1-Journal of Systems and Control Engineering ,2002,216(13):215~224
[49] Zweiri, Y. H.,J. F. Whidbone, and L. D.Seneviratne, Detailed analytical model of a single-cylinder diesel endgin in the crank angle domain, Processings of the Institution of MechanicalEngineers Part D-Journal of Automobile Engineering ,2001, 215(D11):1197~1216
[50] K. Logan, B.Inozu, P.Roy, J.F.Helet,P. Chesse, and X. Tauzia, Real-time marine diesel engine simulation for fault diagnosis, Marie Technology and Sname News, 2002, 39:21~28
[51] N. Lawrence and H. Y. P. Kortekaas, DECSIM-A PC-based diesel engine cycle and cooling system simulation program, Mathematical and Computer Modeling ,2001,33:65~575 [52 ]王江萍.基于信息融合技术的柴油机供油系统故障诊断方法研究:[硕士学位论文].陕西:西安交通大学,2001
[53]吴超仲,严新平,周新聪.基于遗传算法的信息融合在摩擦学组合故障诊断中的应用.摩擦学学报,2001,04
[54]谭逢友,卢宏伟,刘成俊,等.信息融合技术在机械故障诊断中的应用.重庆大学学报(自然科学版),2006,01
[55]王奉涛,马孝江,朱泓,等.基于Dempster-Shafer证据理论的信息融合在设备故障诊断中的应用.大连理工大学学报,2003,4(43):470~474
[56]严志军,朱新河,程东,刘一梅.基于信息融合技术的柴油机磨损模式识别方法.大连海事大学学报,2002,2(58):53~62
[57]李宏坤.基于信息融合技术船舶柴油机故障诊断方法的研究与应用: [博士学位论文].大连:大连理工大学,2003121~124
[58]施小成,谢睿,丁宗华.一种基于模糊神经网络的信息融合技术.自动化技术与应用,2006,01
[59]曾庆茂.基于神经网络和模糊推理的信息融合技术:[硕士学位论文].西安:西安科技大学,2005
[60]刘兆阳.基于信息融合技术的旋转机械故障诊断.起重运输机械,2006,04
[61]王海涛,刘群,邹启杰.设备故障诊断中神经网络与证据推理结合的信息融合方法[J]计算机工程与应用,2004,(22):29~32
[62]李茹,李弼程. D-S证据理论的改进算法在时-空信息融合中的应用.数据采集与处理,2005,(02):58~62
[63]张池平,张英俊,苏小红,马培军.一种基于神经网络和证据理论的信息融合算法.计算机工程与应用,2006,(01):55~58
[64]宏坤,马孝江,王珍.基于多征兆信息融合理论的柴油机故障诊断.农业机械学报,2004,1(35):121~124
[65]罗跃纲,陈长征,曾海泉,等.基于信息融合的集成小波神经网络故障诊断.东北大学学报,2002,8(23):802~805
[66]王树亮,王东,冯珍.基于小波包-神经网络故障诊断系统研究.南京理工大学学报(自然科学版) ,2004,(04):54~58
[67]欧建圣,闻朝中,秦岭.基于小波分析的旋转机械故障诊断系统实现方案.武汉工程职业技术学院学报,2005,(03) :25~28
[68]江磊,江凡.基于小波神经网络的旋转机械故障诊断.汽轮机技术,2004,(03):63~64
[69]马洪超,郭丽艳.人工神经网络信息融合及其在机场识别中的应用研究.武汉大学学报(信息科学版) ,2005,(08) :23~26
[70]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,2005,3(19):15~17
[71]栗青,陈长征.集成神经网络在旋转机械故障诊断中的应用.沈阳工业大学学报,2001,4(23):339~341
[72]雷艳敏.基于神经网络的组合导航系统故障诊断技术研究:[硕士学位论文] .哈尔滨:哈尔滨工程大学,2006
[73]于刚.基于粗糙集约简的信息融合故障诊断研究.汽轮机技术,2003,05:32~34
[74]董广军,张永生,戴晨光,等.基于粗糙集的多源信息融合处理技术.仪器仪表学报,2005,8(260):570~571
[75]王刚,魏守智,赵海.基于模糊评判的决策级信息融合算法的研究.计算机工程与应用, 2002, (15):56~58
[76]陈红武,郎静,田洪祥,等.基于信息融合的柴油机磨损监测研究.内燃机,2004,4:23~26
[77]潘伟,王汉功.基于多传感器信息融合的工程机械液压系统在线状态监测与故障诊断.工程机械,2004,07:56~59
[78]江恒,王友钊.旋转机械在线状态监测与故障诊断系统开发研究.石油矿场机械,2005,(03):54~57
[79]彭志凌,潘宏侠.基于信息融合的机械故障诊断技术研究.机械管理开发,2006,01
[80]汪伟.基于信息融合的发动机故障诊断技术研究.公路与汽运,2005,5(110)19~20
[81]许宁,黄之初.神经网络在旋转机械故障诊断中的应用研究.矿山机械,2005,(08) :55~56
[82]陈文颉,窦丽华,陈杰,杜丽辉.融合多特征信息的模式识别方法.北京理工大学学报,2002,(02):43~47
[83]陈天璐,阙沛文.信息融合多传感器可信度的确定方法及应用.测试技术学报,2005,(01):32~34
[84]陈艳琴,罗大庸.多信息融合技术及在无损检测中的应用.电子科技大学学报,2003,(06):39~41
[85]陈志刚.基于模型的舰艇信息融合系统的研究[J]情报指挥控制系统与仿真技术,2004,(02) .
[86]高洪涛,王敏.证据理论在旋转机械综合故障诊断中应用.大连理工大学学报,2001,(04) :29~31
[87]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,(05):54~57
[88]王文斐.基于信息融合的雷达/红外复合制导目标跟踪方法研究. [硕士学位论文].哈尔滨:哈尔滨工业大学,2006
[89]孙红春,张洪亭.转子故障智能诊断与转子平衡集成系统的开发.振动与冲击,2001,(01) :27~29
[90]江红,张炎华,赵忠华.多传感器信息融合的时间不确定性.上海交通大学学报,2005,(03) :10~15
[91]何永勇,郭丹等.基于信息融合技术的发动机故障诊断研究.内燃机学报,2003,5(21):374~378
[2]王晓俊.康明斯发动机磨合期状态监测方法的研究[硕士学位论文] :徐州:徐州空军学院,2005
[3]邹小明,屠卫星.汽车检测诊断技术.北京:人民交通出版社.,2005.57~108
[4]王江萍.机械设备故障诊断技术及应用.西安:西北工业大学出版社,2001.35~40
[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,2003.1～7
[6]刘建敏,乔新勇,安钢.柴油机技术状况的评估参数研究.车用发动机,2004(1):6～7
[7]任国全,张培林张英堂.装备油液智能监控原理.北京:国防工业出版社,2006.10~25
[8]李柱国.机械润滑与诊断.北京:化学工业出版社,2005.25~45
[9]宋建社,曹小平等.装备维修信息化工程.北京:国防工业出版社,2005,45~50
[10]杨军,冯振声,黄考利等.装备智能故障诊断技术.北京:国防工业出版社. 2004. 2~28
[11]杨其明,严新平,贺石中.油液监测分析现场实用技术.北京:机械工业出版社,2006.153~176
[12]金敬强,武富春.信息融合技术的发展现状与展望.电脑开发与应用,2006,01
[13]范新南,苏丽媛,郭建甲.多传感器信息融合综述.河海大学常州分校学报,2005,(01)
[14]何友,关欣,王国宏.多传感器信息融合研究进展与展望.宇航学报,2005,(04):15~18
[15]张菊秀.多传感器信息融合技术和发展.电子世界,2005,(04):27~32
[16]郭惠勇.多传感器信息融合技术的研究与进展.中国科学基金,2005,01
[17]周芳,韩立岩.多传感器信息融合技术综述.遥测遥控,2006,03
[18]郭戈,罗志刚.多传感器数据融合方法的研究与进展.机电一体化,2003,05:12~17
[19]潘泉,于昕,程永梅,张洪才.信息融合理论的基本方法与进展.自动化学报,2003,4(29):599~615
[20]范新南,苏丽媛,郭建甲.多传感器信息融合综述.河海大学常州分校学报,2005,(01) :35~45
[21]燕颢.信息融合几种算法研究. [硕士学位论文].南京:南京理工大学,2003
[22]杨万海.多传感器数据融合及其应用.西安:西安电子科技大学出版社,2005.45~55 [23 ]施小成,谢睿,丁宗华.一种基于模糊神经网络的信息融合技术.自动化技术与应用,2006,01
[24]戴亚平,刘征,郁光辉.多传感器数据融合理论及应用.北京;北京理工大学出版社,2004.123~145
[25]王新洲,史文中,王树良.模糊空间信息处理.武汉:武汉大学出版社,2003.88~89
[26]史文中.空间数据与空间分析不确定性原理.北京:科学出版社,2005.51~55
[27]戴亚平,刘征,郁光辉.多传感器数据融合理论及应用.北京;北京理工大学出版社,2004.123~145
[28]王新洲,史文中,王树良.模糊空间信息处理.武汉:武汉大学出版社,2003.88~89
[29]徐晓滨.多源信息的描述与建模方法研究:[硕士学位论文] .郑州:河南大学,2006.
[30]张远.基于信息融合技术的故障诊断模型和方法研究:[硕士学位论文].武汉:中南大学,2003
[31]蔡兴国,马平.基于信息融合的并发故障诊断的研究.中国电机工程学报,2003,5(23):107~111
[32]秦志强.数据融合技术及其应用.兵工自动化.,2003,5(23):25~28
[33]林雪霞.信息融合技术在舰艇作战系统中的应用[J]舰船电子工程,2003,(04) :54~57
[34]朱玉鹏.信息融合的神经网络原理及应用研究: [硕士学位论文].长沙:国防科技大学,2004
[35]马平,吕锋,杜海莲.多传感器信息融合基本原理及应用.控制工程,2006,01
[36] A. Jain and A. Ross.“Information fusion in biometrics”. Pattern Recognition Letters,24:2115~2125,2003
[37] B. V. Dasarathy.“Intrusion detection”. Information Fusion, 4:243~245,2005
[38] Shozo Mori, Random Sets in Data Fusion: Formalism to New Algorethms,ISIF, 2004,24~31
[39] R. Mahler.“Statistics 101”for multisensor, miltitarget data fusion. IEEE AES Mag., Part 2: Tutorials, 19(1):53~64,2004
[40] R. Mahler. Multisensor-multitarget sensor management-a unified Bayesian approach. SPIE Proc. Vol. 5096,222~233,2003
[41]王海涛. RBF神经网络与证据理论相结合的特征集信息融合方法研究: [硕士学位论文].哈尔滨:哈尔滨工程大学,2004
[42]赵新宇.基于多传感器信息融合技术的车辆运行状况监测理论与方法研究: [硕士学位论文].长沙:湖南大学,2005
[43] Robb Wilocx, Mark Burrows, Sujit Ghosh and Bilal M. Ayyub, Risk-based technology method for the safety assessment of marin compressed natural gas furel system, Marin Technology ,2001,Vol.38 No3,pp:193~207
[44] Zheng, H.Z. Li, and X. Chen, Gear fault diagnosis based on continuous wavelet transform. Mechanical System and Signal Processing, 2002,16(2-3):447~457
[45] Nikolaou, N.G. and I.A.Antoniadis, Rolling element bearing fault diagnosis using wavelet packets. Ndt& E International,2002,35(3):197~205
[46] Hai-Wen Chen and Teresa Olson, Integrated Spatio-temopral Mutiple Sensor System Design, SPIE, 2002, Vol: 4731, 204~215
[47] Jones N B, Li Yu-hua , Review of conditon monitoring and fauit diagnosis for diesel engines. University of Leicester, Leaf Coppin Publ Ltd,2000,6:267~292
[48] Twiddle, J.A, and N.B Jones, Fuzzy model–based condition monitoring and fault diagnosis of a diesel engine cooling system, Procceedings of the Institution of Mechanical Engineers Part 1-Journal of Systems and Control Engineering ,2002,216(13):215~224
[49] Zweiri, Y. H.,J. F. Whidbone, and L. D.Seneviratne, Detailed analytical model of a single-cylinder diesel endgin in the crank angle domain, Processings of the Institution of MechanicalEngineers Part D-Journal of Automobile Engineering ,2001, 215(D11):1197~1216
[50] K. Logan, B.Inozu, P.Roy, J.F.Helet,P. Chesse, and X. Tauzia, Real-time marine diesel engine simulation for fault diagnosis, Marie Technology and Sname News, 2002, 39:21~28
[51] N. Lawrence and H. Y. P. Kortekaas, DECSIM-A PC-based diesel engine cycle and cooling system simulation program, Mathematical and Computer Modeling ,2001,33:65~575 [52 ]王江萍.基于信息融合技术的柴油机供油系统故障诊断方法研究:[硕士学位论文].陕西:西安交通大学,2001
[53]吴超仲,严新平,周新聪.基于遗传算法的信息融合在摩擦学组合故障诊断中的应用.摩擦学学报,2001,04
[54]谭逢友,卢宏伟,刘成俊,等.信息融合技术在机械故障诊断中的应用.重庆大学学报(自然科学版),2006,01
[55]王奉涛,马孝江,朱泓,等.基于Dempster-Shafer证据理论的信息融合在设备故障诊断中的应用.大连理工大学学报,2003,4(43):470~474
[56]严志军,朱新河,程东,刘一梅.基于信息融合技术的柴油机磨损模式识别方法.大连海事大学学报,2002,2(58):53~62
[57]李宏坤.基于信息融合技术船舶柴油机故障诊断方法的研究与应用: [博士学位论文].大连:大连理工大学,2003121~124
[58]施小成,谢睿,丁宗华.一种基于模糊神经网络的信息融合技术.自动化技术与应用,2006,01
[59]曾庆茂.基于神经网络和模糊推理的信息融合技术:[硕士学位论文].西安:西安科技大学,2005
[60]刘兆阳.基于信息融合技术的旋转机械故障诊断.起重运输机械,2006,04
[61]王海涛,刘群,邹启杰.设备故障诊断中神经网络与证据推理结合的信息融合方法[J]计算机工程与应用,2004,(22):29~32
[62]李茹,李弼程. D-S证据理论的改进算法在时-空信息融合中的应用.数据采集与处理,2005,(02):58~62
[63]张池平,张英俊,苏小红,马培军.一种基于神经网络和证据理论的信息融合算法.计算机工程与应用,2006,(01):55~58
[64]宏坤,马孝江,王珍.基于多征兆信息融合理论的柴油机故障诊断.农业机械学报,2004,1(35):121~124
[65]罗跃纲,陈长征,曾海泉,等.基于信息融合的集成小波神经网络故障诊断.东北大学学报,2002,8(23):802~805
[66]王树亮,王东,冯珍.基于小波包-神经网络故障诊断系统研究.南京理工大学学报(自然科学版) ,2004,(04):54~58
[67]欧建圣,闻朝中,秦岭.基于小波分析的旋转机械故障诊断系统实现方案.武汉工程职业技术学院学报,2005,(03) :25~28
[68]江磊,江凡.基于小波神经网络的旋转机械故障诊断.汽轮机技术,2004,(03):63~64
[69]马洪超,郭丽艳.人工神经网络信息融合及其在机场识别中的应用研究.武汉大学学报(信息科学版) ,2005,(08) :23~26
[70]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,2005,3(19):15~17
[71]栗青,陈长征.集成神经网络在旋转机械故障诊断中的应用.沈阳工业大学学报,2001,4(23):339~341
[72]雷艳敏.基于神经网络的组合导航系统故障诊断技术研究:[硕士学位论文] .哈尔滨:哈尔滨工程大学,2006
[73]于刚.基于粗糙集约简的信息融合故障诊断研究.汽轮机技术,2003,05:32~34
[74]董广军,张永生,戴晨光,等.基于粗糙集的多源信息融合处理技术.仪器仪表学报,2005,8(260):570~571
[75]王刚,魏守智,赵海.基于模糊评判的决策级信息融合算法的研究.计算机工程与应用, 2002, (15):56~58
[76]陈红武,郎静,田洪祥,等.基于信息融合的柴油机磨损监测研究.内燃机,2004,4:23~26
[77]潘伟,王汉功.基于多传感器信息融合的工程机械液压系统在线状态监测与故障诊断.工程机械,2004,07:56~59
[78]江恒,王友钊.旋转机械在线状态监测与故障诊断系统开发研究.石油矿场机械,2005,(03):54~57
[79]彭志凌,潘宏侠.基于信息融合的机械故障诊断技术研究.机械管理开发,2006,01
[80]汪伟.基于信息融合的发动机故障诊断技术研究.公路与汽运,2005,5(110)19~20
[81]许宁,黄之初.神经网络在旋转机械故障诊断中的应用研究.矿山机械,2005,(08) :55~56
[82]陈文颉,窦丽华,陈杰,杜丽辉.融合多特征信息的模式识别方法.北京理工大学学报,2002,(02):43~47
[83]陈天璐,阙沛文.信息融合多传感器可信度的确定方法及应用.测试技术学报,2005,(01):32~34
[84]陈艳琴,罗大庸.多信息融合技术及在无损检测中的应用.电子科技大学学报,2003,(06):39~41
[85]陈志刚.基于模型的舰艇信息融合系统的研究[J]情报指挥控制系统与仿真技术,2004,(02) .
[86]高洪涛,王敏.证据理论在旋转机械综合故障诊断中应用.大连理工大学学报,2001,(04) :29~31
[87]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,(05):54~57
[88]王文斐.基于信息融合的雷达/红外复合制导目标跟踪方法研究. [硕士学位论文].哈尔滨:哈尔滨工业大学,2006
[89]孙红春,张洪亭.转子故障智能诊断与转子平衡集成系统的开发.振动与冲击,2001,(01) :27~29
[90]江红,张炎华,赵忠华.多传感器信息融合的时间不确定性.上海交通大学学报,2005,(03) :10~15
[91]何永勇,郭丹等.基于信息融合技术的发动机故障诊断研究.内燃机学报,2003,5(21):374~378