摘要
我国当前使用的桥式起重机大部分为20世纪80年代以前生产的,除少量退役报废外,大部分仍在继续使用,老化问题日益突出,超期使用依据不足,风险性较大。随着我国现代化建设事业的发展和改革的不断深入,生产应用中对桥式起重机的承载能力和可靠性提出了更高的要求。因此,如何科学评估起重机主梁结构的实际性态,是保障桥式起重机的安全使用,预防和控制重大事故,确保企业安全、高效生产的重要问题。同时,齿轮减速器因传动力矩大、工作环境恶劣等,导致其成为起重机最容易出现故障的部分,其运行状态往往直接影响到起重机是否正常工作。为了评估桥式起重机使用的安全可靠性,建立一套桥式起重机主梁及减速器的健康评估方法就显得尤为重要。论文重点开展下列研究工作:
(1)桥式起重机主梁有限元模型修正技术的研究针对桥式起重机主梁实际结构与有限元计算模型之间存在的差异,研究了有限元模型修正的一般理论,提出了通过灵敏度分析来实现对桥式起重机有限元模型修正的方法。在结构有限元模型建模过程中,结合实测频率确定修正目标函数,对结构模型进行修正,以得出修正后结构模型的参数,最后根据结构模型修正所得到的参数,获得修正模型。修正后的有限元模型更能反映结构的实际动力特性,为主梁损伤评价方法的提出奠定了坚实的基础。
(2)基于动力指纹的起重机主梁损伤分析为获得一种对主梁损伤较为敏感的动力指纹,着重研究了基于频率变化、基于振型变化、基于曲率模态、基于柔度差曲率和基于改进刚度指标五种动力指纹的特点,明确了基于动力指纹的主梁损伤分析步骤并将五种指纹用于桥式起重机主梁的损伤评定。通过将五种指标用于主梁损伤定位分析,发现曲率模态和改进刚度指标在结构前三阶的定位效果最好。为此,将曲率模态和改进刚度指标用于损伤程度识别,发现基于改进刚度指标指纹的程度识别能力较曲率模态指纹的程度识别能力更为敏感。说明改进刚度指标是一种优秀的损伤识别动力指纹。这为验证提出的主梁损伤评价方法提供了保证。
(3)基于支持向量机的起重机主梁损伤评价通过对支持向量机理论和具体实现算法的分析,成功地将改进刚度指标(SVI)引入支持向量机(SVM),确定了SVM用于损伤评价的最佳参数,实现了对桥式起重机主梁损伤的识别。为进一步提高评价精度与速率,对粒子群算法进行了改进,并用改进的算法对最小二乘支持向量机(LSSVM)的正则化参数和核函数参数进行了优化,优化后的方法被用到起重机损伤诊断中。结果证实该方法具有比传统分类算法更出色的分辨能力。
(4)基于EMD与ARMA模型倒谱分析的桥式起重机减速器故障诊断简明阐述了桥式起重机减速器故障诊断的基本原理,并总结了齿轮和滚动轴承发生故障时出现的故障特征频率。针对桥式起重机减速器振动信号混杂的特点,提出了EMD与ARMA模型参数化倒双谱分析相结合的诊断方法。首先,对提取的实测截取信号进行AR模型延拓,抑制EMD分解的边界效应;然后,借助ARMA模型对延拓分解得到的各平稳信号序列建立精确模型,并对模型实施参数化切片倒谱运算,从而实现有效地提取故障特征信息的目的。
(5)桥式起重机主梁及减速器的动力测试为获得用于主梁模型修正的实测频率数据和用于减速器诊断的实测故障信号,对桥式起重机主梁及减速器进行了动力学实验研究。通过对桥式起重机主梁金属骨架的模态测试,获得了主梁激振实验的振动衰减信号,并对测取的信号进行了传函分析,经参数识别方法确定了主梁振动的前三阶频率,为主梁的仿真模型修正提供了基础和支持。同时,通过对主梁起升机构的齿轮减速器的故障诊断测试,获取了在役减速器振动信号,从而为判断机器内部的故障原因及其故障的性质提供条件,为桥式起重机的实际性态作出客观判断与评价提供了依据。
At present, most of overhead travlling cranes, used in our country, are produced beforethe1980s. They are servicing with the exception of that a few are scraped. Aging has becomean increasingly important issue for the serviced and the risk is becoming greater and greater.However, with the development of chinese construction industry and the deepening of thereform, higher request in practice is proposed for the bearing capacity and reliability ofoverhead travlling cranes. So, how to scientific evaluate the real state on girder of overheadtravlling crane is the key to avoid and control catastrophe failure in enterprise production.Meanwhile, gear reducer has a big transmission torque and poor working conditions, etc, itcauses reducer become the part of the most prone to go wrong. So reducer’s running statedirectly affects crane’s normal work. In order to assess the reliability of overhead travllingcrane so as to make scientific decision about normal use, downgraded or scraped and so on,dynamic evaluation is played in the thesis.The main research work is conducted as follows:
(1) Research on the girder finite element model updating techniques.
Targeting the difference between the finite element model and real girder structure, thetraditional theory of finite element model updating is analysed and a few kinds of methods,commonly used, are compared.Then the method, based on sensitivity analysis, is proposed toupdate the girder finite element model of overhead travlling crane. In the process of modeling,modification of the objective function is on the basis of the measured frequency. Aftermodifying the structural model, optimized calculating parameters are obtained, then themodel is updated. The updated finite element model reflected actual dynamic characteristicsin an even better fashion. It lays solid foundation for further analysis.
(2) Damage analysis on modal dynamic fingerprint of the girder.
The dynamic fingerprints, based on frequency variation, mode variation, curvature modevariation, flexibility variation curvature and stiffness variation index, are introduced and thecomputational method of dynamic fingerprints are expounded. These indexes are used on anoverhead travlling crane to compare damage identification capability. It is found that theperformance of curvature mode variation fingerprint and stiffness variation index fingerprintis the best when those dynamic fingerprints are used to locate damage. Then curvature modevariation fingerprint and stiffness variation index fingerprint are used to calculate damagelevels and the results show stiffness variation index fingerprint has a more accurateidentification capability. So, stiffness variation index is an excellent fingerprint.
(3) Damage evaluation of crane’s girder based on support vector machine.
The algorithm of support vector machine(SVM) and the principle of the kernel functionare analysed in theory. Then SVIs are input into SVM and appropriate kernel function isselected. With test, the best parameters are calculated to identify gieder damage of crane.Tofurther improve the identification accuracy and convergence rate, particle swam optimization(PSO) algorithm is introduced and improved. The improved PSO is used to optimize theparameters of least square support vector machine(LSSVM). After that, the method is used toidentify girder damage of overhead travlling crane.The result demonstrates IPSO-LSSVM hasbetter identifition ability than traditional classification algorithm.
(4) Research on the application of EMD and ARMA bi-cepstrum fault diagnosismethod in gearbox of overhead traveling crane.
Basic principle of fault diagnosis about crane gear reducer is concisely expounded andpossible characteristic frequency when gear and rolling bearing occurs fault is summarized.Targeting the time varying of signals measured on the surface of gear reducer, empirical modedecomposition(EMD) is occupied to modulate the signals; auto-regressive movingaverage(ARMA) method,which has higher accuracy and better applicable condition, is usedto establish model for the signal principal components of intrinsic mode functions from theEMD; then parametric bi-cepstrum estimation is implemented and accurate fault informationsare extracted. The method is applied in fault diagnosis of crane gear reducer, the resultsdemonstrate that: the presented method, combining EMD with parametric bi-cepstrum ofARMA model, can obtain the fault quefrency accurately to determine the actual conditions ofcrane gear reducer, and it is an effective health assessment method.
(5) Dynamics test for girder and gearbox of overhead traveling crane
Through the modal test for the girder metal frame, fading signals of exciter test on girdermetal frame are collected. Frequency response analysis is conducted for the collected signalsand the informations of the first three modes are obtained with structural modal parameter id-entification method.This provides foundation and support for updating simulation model ofgirder metal frame.Otherwise, with fault diagnosis test on gear reducer of gieder lifting mech-anism, vibration signals are collected from reducer. This provides condition for deteminingthe failure causes or the fault nature.
引文
[1]郑炯辉.主梁结构维护及起重机操作运行的安全措施[J].科技资讯,2009,(28):26
[2]王金诺,于兰峰.起重运输机金属结构[M].北京:中国铁道出版社,2002
[3]张昌勤.桥式起重机箱形主梁结构疲劳性能分析[J].大众科学·科学研究与实践,2008,(6):16-17
[4]胡宗武,阎以诵.起重机动力学[M].北京:机械工业出版社,1988
[5]张亚辉,林家浩.结构力学基础[M].大连:大连理工出版社,2007
[6] Cveticanin L. Dynamic behavior of the lifting crane mechanism[J]. Mechanism andMachine Theory,1995,30(1):141-151
[7]肖汉斌.起重机卷筒强度和稳定性理论分析与试验研究[D].武汉理工大学博士学位论文.武汉:武汉理工大学,2003
[8]王崎峰.集装箱桥式起重机主梁应力的动力学分析[J].起重运输机械,2007,(4):40-43
[9]程文明,王金诺.门式起重机结构祸合系统的动态仿真[J].铁道学报,2001,23(4):51-54
[10]王贡献,沈荣瀛.起重机臂架在起升冲击载荷作用下动态特性研究[J].机械强度,2005,27(5):561-566
[11]张氢,杨林.65t集装箱桥吊动力仿真建模及其在起升工况分析中的应用[J].机械设计,2007,24(5):40-43
[12]杨巧萍,刘延雷.国内起重机事故统计分析与预防对策[J].机械管理开发,2011,(2):140-141
[13]郭寒竹.起重机械事故统计分析与风险控制[J].建筑机械化,2007,(1):17-20
[14]郑栋梁,李中付,华宏星.结构早期损伤识别技术的现状和发展趋势[J].振动与冲击,2002,21(2):1-8
[15]Popovics J.S. A survey of developments in ultrasonic NDE of concrete[J]. IEEETransactions on Ultrasonic Ferroelectrics and Frequency Control,1994,41(1):140-143
[16]Jenks W.G. Squids for nondestructive evaluation[J]. Journal of Physics&Applied Physics,1997,30(3):293-323
[17]王德昌,张富胜.无损探伤在起重机检测中的应用[J].起重运输机械,1995,(8):30-31
[18]Ditchburn R.J., Burke S.K., Scala C.M. NDT of welds: state of the art[J]. NDT&EInternational,1996,29(2):111-117
[19]邵泽波.无损检测技术[M].北京:化工工业出版社,2003
[20]周正干,刘斯明.非线性无损检测技术的研究、应用和发展[J].机械工程学报,2011,47(8):2-9
[21]Ren Wei-Xin, De Roeck G. Structural damage identification using modal data I:simulation verification[J]. Journal of structural engineering,2002,128(1):87-95
[22]Sara Casciati. Stiffness identification and damage localization via differential evolutionalgorithms[J]. Structural Control and Health Monitoring,2008,15(3):436-449
[23]蒋华.基于静力测试数据的桥梁结构损伤识别与评定理论研究[D].成都:西南交通大学,2005
[24]Zhao X.L., Gao H.D., Zhang G.F., et al. Active health monitoring of an aircraft wing withembedded piezoelectric sensor/actuator network I: defect detection, localization andgrowth monitoring[J]. Smart Materials And Structures,2007,16(4):1208-1217
[25]高维成,刘伟,邹经湘.基于结构振动参数变化的损伤探测方法综述[J].振动与冲击,2004,23(4):1-7
[26]胡志强,法庆衍,洪宝林,等.随机振动试验应用技术[M].北京:中国计量出版社,1996
[27]Huang M.L., Wang Y.P., Chang J.R., et al. Physical system identification of an isolatebridge using seismic response data[J]. Structural Control and Health Monitoring,2009,16(2):241-265
[28]黄民水,郭文增,朱宏平,等.基于环境激励的桥梁结构动力测试及模型修正[J].华中科技大学学报(城市科学版),2006,23(4):57-60
[29]Doebling S.W., Farrar C.R., Cornwell P.J. A Computer Toolbox for Damage Identificati-on Based on Changes in Vibration Characteristics[C]. Structural Health Monitoring,Current Statusand Perspective, Stanford University, CA,1997, pp241-254
[30]Choi M.Y., Kwon I.B. Damage Detection System of a Real Steel Truss Bridge by NeuralNetworks[C]. Proceedings of SPIE, Newport Beach, CA,2000,3988:295-306
[31]Zhang Dewen, Zhang Lingmi. Matrix transformation method for updating dynamicmodel[J]. AIAA Journal,1992,30(5):1440-1443
[32]彭晓洪,丁锡洪.用模态参数识别结果对实际结构有限元动力模型的修正[J].振动与冲击,1984,3(1):8-15
[33]Kabe A.M. Stiffness matrix adjustment using mode data[J]. AIAA Journal,1985,23(9):1431-1436
[34]Lim T.W. Analytical model improvement using measured modes and submatrix[J]. AIAAJournal,1991,29(6):1015-1020
[35]Zhang O.Q., Zerval A., Zhang D.W. Stiffness matrix adjustment using incompletemeasured modes[J]. AIAA Journal,1996,25(5):917-919
[36]Fox R.L., Kapoor M.P. Rates of change of eigenvalues and eigenvectors[J]. AIAA Journal,1968,6(12):2426-2429
[37]Nelson R.B. Simplified calculation of eigenvector derivatives[J]. AIAA Journal,1976,14(9):1201-1205
[38]Sutter T.R., Camarda C.J., Walsh J.L., et al. Comparison of several methods forcalculating vibration mode shape derivatives[J]. AIAA Journal,1998,26(12):1506-1511
[39]Tan S.C.E., Andrew A.L. Computing derivatives of eigenvalues and eigenvetors: instituteof mathematics and its applications[J]. Journal of Numerical Analysis,1989,9(1):111-122
[40]Dailey R.L. Eigenvector derivatives with repeated eigenvalues[J]. AIAA Journal,1989,27(4):486-491
[41]冯新,李国强,范颖芳.几种常用损伤动力指纹的适用性研究[J].振动、测试与诊断,2004,24(4):277-280
[42]谢强,薛松涛.土木工程结构健康监测的研究状况与进展[J].中国科学基金,2001,15(5):285-288
[43]Cawley P., Adams R.D. The location of defects in structures from measurements ofnatural frequencies[J]. Journal of Strain Analysis,1979,14(2):49-57
[44]George Hearn, Rene B.Testa. Modal Analysis for Damage Detection in Structures[J].Journal of Structural Engineering, ASCE,1991,117(10):3042-3061
[45]Norris Stubbs, Tafe H.Broome, Roberto Osegueda. Nondestructive construction errordetection in large space structures[J]. AIAA,1990,28(11):146-152
[46]谢俊,韩大建.一种改进的基于频率测量的结构损伤识别方法[J].工程力学,2004,21(1):21-25
[47]Salawu O.S. Detection of structural damage through changes in frequency: a review[J].Engineering Structures,1997,19(9):718-723
[48]Allemany R.J., Brown D.L. A correction coefficient for modal vector analysis[A].Proceeding of the1st International Modal Analysis Conference, USA,1982, pp110-116
[49]Lieven N.A.J., Ewins D.J. Spatial correlation of modal shapes, the coordinate modalassurance criterion(COMAC)[A]. Proceeding of the4th International Modal AnalysisConference, USA,1988, pp1-5
[50]于菲,刁延松,佟显能,等.基于振型差值曲率与神经网络的海洋平台结构损伤识别研究[J].振动与冲击,2010,30(10):183-187
[51]Jenks W.G. Squids for nondestructive evaluation[J]. Journal of Physics&AppliedPhysics,1997,30(3):293-323
[52]郭杏林,高海洋.一种基于振型正交化的元素型模型修正方法[J].振动与冲击,2011,30(6):5-9
[53]秦权,张卫国.悬索桥的损伤识别[J].清华大学学报(自然科学版),1998,38(12):44-47
[54]臧献国,于德介,姚凌云,等.基于模态振型形状优化的结构声辐射控制[J].机械工程学报,2010,46(9):73-78
[55]Pandey A.K., Biswas M., Samman M.M. Damage detection from changes in curvaturemode shapes[J]. Journal of Sound and Vibration,1991,145(2):321-332
[56]李德葆,陆秋海,秦权.承弯结构的曲率模态分析[J].清华大学学报(自然科学版),2002,42(2):22-37
[57]孙宗光,高赞明,倪一清,等.斜拉桥桥面结构损伤位置识别的指标比较[J].工程力学,2003,20(1):27-31
[58]王柏生,倪一清,高赞明.青马大桥桥板结构损伤位置识别的数值模拟[J].土木工程学报,2001,34(3):67-73
[59]马立英,周铉,彭晓俊.曲率模态及其在汽车后桥损伤识别中的应用[J].同济大学学报(自然科学版),2011,39(8):1208-1211
[60]徐华东,王立海,胡志栋.运用曲率模态技术的木梁损伤定量识[J].振动、测试与诊断,2011,31(1):110-114
[61]胡业平,张成海,屠义强.基于曲率模态的结构损伤定位[J].解放军理工大学学报(自然科学版),2009,10:57-63
[62]杜永峰,张冬兵.曲率模态和神经网络在损伤识别中的应用[J].公路交通科技,2007,124(11):77-80
[63]Pandey A.K., Biswas M. Damage detection in structures using changes in flexibility[J].Journal of Sound and Vibration,1994,169(1):3-17
[64]蔡贤辉.一种桁架结构损伤识别的柔度阵法[J].计算力学学报,2001,18(1):42-47
[65]韩西,钟厉.用矩阵结构模型进行结构损伤检测和故障诊断[J].重庆交通学院学报,2002,21(2):108-110
[66]Zhao J., Dewolf J.T. Sensitivity study for vibrational parameters used in damagedetection[J]. Journal of Structural Engineering,1999,125(4):410-416
[67]Farrar C.R., Jauregui D.V. Damage detect on algorithms applied to experimental andnumerical modatl data from the I-40bridge[R]. Los Alamos National Laboratory reportLA-13074-MS,1996
[68]马立元,李世龙,李永军,等.基于子结构及模态柔度曲率差的某框架结构损伤识别[J].海军工程大学学报,2011,23(5):21-26
[69]马骏,陈立,赵德有.基于柔度曲率矩阵的加筋板结构损伤识别方法[J].船舶力学,2011,15(8):881-891
[70]杨秋伟,孙斌祥.结构损伤识别的改进柔度灵敏度方法研究[J].振动与冲击,2011,30(5):27-31
[71]Chen J.C., Garba J.A. On-Orbit Damage Assessment for Large Space Structures[J].AIAA Journal,1988,26(9):1098-1126
[72]史治宇.结构破损定位的单元模态应变能变化率法[J].振动工程学报,1998,11(3):357-360
[73]唐小兵,陈定方,沈成武.结构裂纹位置识别的模态分析[J].武汉理工大学学报,2001,23(5):71-74
[74]Shi Z.Y., Law S.S., Zhang L.M. Structural damage detection from modal strain energychange[J]. Journal of Engineering Mechanics,2000,126(12):1216-1223
[75]Shi Z.Y., Law S.S., Zhang L.M. Improved damage quantification from elemental modalstrain energy change[J]. Journal of Engineering Mechanics,2002,128(5):521-529
[76]颜王吉,黄天立,任伟新.基于单元模态应变能灵敏度的结构损伤统计识别[J].振动与冲击,2011,42(1):152-157
[77]郑飞,许金余,颜祥程.利用单元模态应变能法的地下框架结构损伤诊断[J].振动、测试与诊断,2010,30(6):642-645
[78]李弋,唐天国,刘浩吾.基于模态应变能法的钢筋混凝土梁裂缝损伤检测试验[J].应用基础与工程科学学报,2010,18(1):40-49
[79]Lew J.S. Using Transfer Function Parameter Changes for Damage Detection ofStructures[J]. AIAA Journal,1995,33(11):2189-2193
[80]Maia N.M. Location of Damage Using Curvature of the Frequency ResponseFunctions[C]. Proceedings of15th International Modal Analysis Conference, Florida,1997:179-184
[81]Mark J.S. Detecting Structural Damage Using Transmittance Function[C]. Proceedings of15th IMAC, Florida,1997:638-644
[82]张慕宇,杨智春,丁燕.采用主成分分析与最邻近法的复合材料板损伤检测实验[J].西北工业大学学报,2010,28(5):786-791
[83]王莹,李兆霞,钱方.结构连接刚度损伤的识别方法[J].东南大学学报(自然科学版),2011,41(4):829-835
[84]游春华.基于模态技术的损伤识别[D].武汉:武汉大学,2005
[85]Agneni A., Balis Crema L., Mastroddi F. Damage Detection from Truncated FrequencyResponse Function[C]. European COST F3Conference on System Identification andStructural Health Monitoring, Madrid, Spain,2000:137-146
[86]Pandy P.C., Barais S.V. Multiplayer perception in damage detection of bridge structures[J]. Computers and Structures,1995,54(4):597-608
[87]Yeung W.T., Smith J.W. Damage detection in bridges using neural networks for patternrecognition of vibration signatures[J]. Engineering Structures,2005,27(3):685-698
[88]熊仲明,王超,林涛.基于神经网络的大跨钢结构缺陷损伤的定位研究[J].振动与冲击,2011,30(9):191-196
[89]孙建平,王逢瑚,胡英成.基于声发射和神经网络的木材受力损伤过程检测[J].仪器仪表学报,2011,32(2):342-347
[90]杨晓楠,姜绍匕,土金鱼.基于能量特征的小波概率神经网络损伤识别方法[J].兰州理工大学学报,2005,31(3):123-126
[91]焦莉,李宏男.结构损伤识别的耦合神经网络方法[J].沈阳建筑大学学报(自然科学版),2006,22(1):73-76
[92]李小平,郑世杰.基于遗传算法和拓扑优化的结构多孔洞损伤识别[J].振动与冲击,2011,30(7):201-204
[93]邹万杰,瞿伟廉.基于频晌函数和遗传算法的结构损伤识别研究[J].振动与冲击,2008,27(12):28-30
[94]Mares C., Surace C. An application of genetic algorithms to identifu damage in elasticstructures[J]. Sound and Vibration,1996,195(2):195-215
[95]Friswell M.L., Penny J.E.T., Garvey S.D. A combined genetic and eigensensitivityalgorithm for the location of damage in structures[J]. Computers and Structures,1998,69(5):547-556
[96]邹大力,屈福政.基于修正模态的混合遗传算法结构损伤识别[J].大连理工大学学报,2005,45(3):362-365
[97]程远胜,区达光,谭国焕,等.基于分级遗传算法的结构损伤识别方法[J].华中理工大学学报(自然科学版),2002,38(8):73-75
[98]BOSER B., GUYON L., VAPNIK V.N. A Training Algorithm for Optimal MarginClassifier[C]. In Proceedings of the Fifth Annual Workshop on Computational LearningTheory, Baltimore, Maryland, ACM Press,1992:144-152
[99]Cortes, Vapnik V. The Soft Margin Classifier Technical memorandum11359-931209-18TM[R], AT&T Bell Labs,1993
[100] VAPNIK V.N. The Nature of Statistical Learning Theory[M]. New York: Springer–Ve-rlag,1995
[101] VAPNIK V., GOKOWICH S., SMOLA.A. Support Vector Method for Function Appr-oximation, Regression Estimation and Signal[C]. Processing Advances in NeuralInformation Processing Systems9, NewYork: MIT Press,1997:281-287
[102] Vapnik V.N. Universal learning technology: Support Vector Machines[J]. NEC Journalof Advanced Technology,2005,2(2):137-144
[103]何浩祥,闫维明,张爱林.基于支持向量机的张弦梁损伤识别试验[J].振动、测试与诊断,2011,31(1):45-49
[104]付春雨,单德山,李乔.基于支持向量机的静力损伤识别方法[J].中国铁道科学,2010,31(5):47-53
[105]刘春城,刘佼,李宏男.基于支持向量机的大型输电铁塔损伤识别方法研究[J].应用基础与工程科学学报,2010,18(4):606-625
[106]于繁华,刘寒冰.基于支持向量机和粒子群算法的结构损伤识别[J].吉林大学学报:工学版,2008,38(2):434-438
[107] SAMANTA B. Gear Fault Detection Using Artificial Neural Networks and SupportVector Machines with Genetic Algorithms[J]. Mechanical Systems and Signal Processi-ng,2004,18(3):625-644
[108] MELGANI F., BRUZZONE L. Classification of Hyperspectral Remote Sensing Imageswith Support Vector Machines[J]. IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790
[109]徐启华,师军.应用SVM的发动机故障诊断若干问题研究[J].航空学报,2005,26(6):686-690
[110]钟秉林.机械故障诊断学(第3版)[M].北京:机械工业出版社,2007
[111]康海英,栾军英,崔清斌,等.基于时域平均的齿轮故障诊断[J].军械工程学院学报,2006,18(1):34-36
[112]王凯,张永祥,李军.齿轮裂纹故障的双谱分析[J].机械强度,2006,28(3):346-348
[113] Qin S.R., Zhong Y.M. A new envelope algorithm of Hilbert-Huang transform[J].Mechnical Systems and Signal Processing,2006,20(8):1941-1952
[114]贾军峰,杨国安,李新华,等.基于小波包和包络分析的滚动轴承故障自动诊断方法[J].石油矿场机械,2006,35(5):18-22
[115]丁康,陈健林,苏向荣.平稳和非平稳振动信号的若干处理方法及发展[J].振动工程学报,2003,16(1):1-10
[116]丁康,孔正国,何志达.振动调幅信号的循环平稳解调原理与应用[J].振动工程学报,2005,18(3):304-308
[117]潘旭峰,谢波,李晓雷.小波变换理论及其在机械故障诊断中的应用[J].振动与冲击,1998,17(1):14-19
[118]胡子谷,石来德.故障振动信号的小波包分解与诊断[J].振动与冲击,1998,17(2):54-59
[119]赵学智,叶邦彦,陈统坚.多分辨奇异值分解理论及其在信号处理和故障诊断中的应用[J].机械工程学报,2010,46(20):64-75
[120]张中民,卢中祥,杨叔子,等.基于小波系数包络谱的滚动轴承故障诊断[J].振动工程学报,1998,11(1):65-69
[121] Li C.James, Jun Ma. Wavelet decomposition of vibration for detection of bearinglocalized defects[J]. NDT&E International,1997,30(3):143-149
[122]张进,冯志鹏,褚福磊.滚动轴承故障特征的时间一小波能量谱提取方法[J].机械工程学报,2011,47(17):44-49
[123] Changzheng Chen, changtao Mo. A method for intelligent fault diagnosis of rotatingmachinery[J]. Digital Signal Processing,2004,14(3):203-217
[124] Xingsheng Lou, Loparo K.A. Bearing fault diagnosis based on wavelet transform andfuzzy inference[J]. Mechanical Systems and Signal Processing,2004,18(5):1077-1095
[125] S.Jazebi, B.Vahidi, M.Jannati. A novel application of wavelet based SVM to transientphenomena identication of power transformers[J]. Energy Conversion and Management,2011,52(2):1354-1363
[126] Huang N.E., Shen Z., Long S.R., et al. The empirical mode decomposition and theHilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings ofthe Royal Society of London Series,1998,454:903-995
[127] Huang N.E., Shen Z., Long S.R., et al. A new view of nonlinear water waves: theHilbert spectrum[J]. Annual Review of Fluid Mechanics,1999,31(1):417-457
[128] Guo D., Peng Z.K. Vibration analysis of a cracked rotor using Hilbert-Huangtransform[J]. Mechanical Systems and Signal Processing,2007.21(8):3030-3041
[129]程军圣,于德介,杨宇. EMD方法在转子局部碰摩故障诊断中的应用[J].振动、测试与诊断,2006,26(1):24-27
[130] Gai G. The processing of rotor startup signals based on empirical modedecomposition[J]. Mechanical Systems and Signal Processing,2006,20(1):222-235
[131] Bassiuny A.M., Xiao L.L. Flute breakage detection during end milling usingHilbert-Huang transform and smoothed nonlinear energy operator[J]. International Journalof Machine Tools and Manufacture,2007,47(6): l0l l-1020
[132] Parey A., Tandon N. Impact velocity modelling and signal processing of spur gearvibration for the estimation of defect size[J]. Mechanical Systems and Signal Processing,2007,21(1):234-243
[133] YuYang, Yigang He, Junsheng Cheng, et al. A gear fault diagnosis using Hilbertspectrum based on MODWPT and a comparison with EMD approach[J]. Measurement,2009,42(4):542-551
[134] Du Q., Yang S. Application of the EMD method in the vibration analysis of ballbearings[J]. Mechanical Systems and Signal Processing,2007,21(6):2634-2644
[135] Fengtao Wang, Wensheng Su, Dong Wu, et al. Research on EMD noise reductionmethods applied in signal processing of rolling bearings[J]. Energy Procedia,2011,13:7583-7590
[136] Guo K. Application of EMD method to friction signal processing[J]. MechanicalSystems and Signal Processing,2008,22(1):248-259
[137] Mckeown M.J., Saab R., Abu-Gharbieh R. A Combined Independent ComponentAnalysis (ICA)/Empirical Mode Decomposition (EMD) Method to Infer CorticomuscularCoupling[C].2nd International IEEE EMBS Conference on Neural Engineering,2005
[138]王伟,魏洪兴.基于经验模分解和最小二乘支持矢量机的装载机载质量动态测量混合建模方法[J].机械工程学报,2008,44(2):87-93
[139] Houxi Cui, Laibin Zhang, Rongyu Kang, et al. Research on fault diagnosis forreciprocating compressor valve using information entropy and SVM method[J].2009,22(6):864-867
[140] Li K., Gao Z., Zhao X. Multiple scale analysis of complex networks using the empiricalmode decomposition method[J]. Physica A: Statistical Mechanics and its Applications,2008,387(12):2981-2986
[141]杨叔子,吴雅,轩建平.时间序列分析的工程应用(第二版)上、下册[M].武汉:华中科技大学出版社,2007
[142] Granpe D. Time series analysis: identification and adaptive filtering[M]. Malabar:Krieger Pub Co,1989
[143]黄建国,武延祥,杨世兴.现代谱估计原理及应用[M].北京:科学出版社,1994
[144]包陈,王呼佳,陈洪军,等. ANSYS工程分析进阶实例(修订版)[M].北京:中国水利水电出版社,2009
[145]张德文,魏阜旋.模型修正与破损检测[M].北京:科学出版社,1999
[146]曹树谦,张文德,啸龙翔.振动结构模态分析[M].天津:天津大学出版社,2001
[147] Maeck J. Damage assessment of civil engineering structure by vibration monitoring[D]. Belgium: Department of Civil Engineering, Katholieke Universiteit Leuven,2003
[148]苏成,徐郁峰,韩大建.有限元法及样条拟合技术在频率法测量索力中的应用[J].公路,2004,(12):28-31
[149]苏成,徐郁峰,韩大建.频率法测量索力中的参数分析与索抗弯刚度的识别[J].公路交通科技,2005,22(5):75-78
[150]谭林.基于动力指纹的结构损伤识别可靠度方法研究[D].广州:华南理工大学,2010
[151] Platt J. Prohahilistic outputs for support vector machines and comparison to regularizedlikelihood methods[C]. Advances in Large Margin Classifiers, Cambridge, MIT Press,2000
[152] Vladimir Cherkassky, Yunqian Ma. Practical selection of SVM regression parametersand noise estimation for SVM regression[J]. Neural Networks,2004,17(1):113-126
[153] Chang C.C., Lin C.J. LIBSVM: A Library for Support Vector Machines,2001[CP/OL].http://www.csie.ntu.edu.tw/~cjlin/libsvm
[154] SUYKENS J.A.K., VANDEWALLE J. Least squares support vector machines classif-iers[J]. Neural Processing Letters,1999,19(3):293-300
[155] Shi Y.H., Eberhart R.C. A modied particle swarm optimizer[C]. In Proceedings of theinternational conference on evolutionary computation, Washington, USA,1999, pp1945-1950
[156]屈梁生,何正嘉.机械故障诊断学[M].上海:上海科学技术出版社,1986
[157] Chunchieh Wang, Yuan Kang, Pingchen Shen, et al. Applications of fault diagnosis inrotating machinery by using time series analysis with neural network[J]. Expert Systemswith Applications,2010,37(2):1696-1702