基于小波分析与神经网络滚动轴承故障诊断方法的研究
|
摘要
滚动轴承是旋转机械中重要的零部件之一,但由于加工工艺、工作环境等原因造成损坏率高、寿命的随机性较大。旋转机械故障种类繁多,但由滚动轴承的故障引起的大约占三分之一,所以掌握滚动轴承的工作状态以及故障的形成和发展,是目前机械故障诊断领域中所研究的重要课题之一
本论文通过分析滚动轴承振动机理、失效原因和信号特征,对轴承振动信号的采集方法进行了改进,采用无线传感器网络技术降低故障诊断系统的复杂性、提升诊断系统的效率。利用滚动轴承振动信号实现其故障检测与诊断,目前主要有机理分析和智能诊断两条途径。机理分析常用方法有随机共振和小波分析等；智能诊断常用方法有神经网络和支持向量机等。但以上各方法在实际应用中均存在其不足之处,从而影响到轴承故障检测与诊断的效果。为此,本文认为非常有必要立足于不断发展的新理论和新方法,紧紧围绕滚动轴承故障机理分析与智能诊断现有方法存在的问题与不足展开研究与探讨。
(1)针对传统有线传感器网络信息采集灵活性差、故障率高的问题,本文在分析滚动轴承振动机理、失效原因和振动信号特征的基础上,设计了滚动轴承振动信号无线采集网络,以802.15.4和ZigBee协议为标准,采用250kbps(?)勺传输速率和无线部署的方式,降低系统复杂性和故障率,为后续轴承故障诊断方法提供基础原理性的支持。
(2)针对噪声较强有用信号较弱环境下的轴承故障问题,研究了一种基于遗传免疫优化粒子群算法的随机共振方法。该方法不仅实现了强噪声背景下的微弱信号提取,而且解决了基本随机共振理论只能处理微弱的小参数信号、不能处理轴承振动这类大参数信号问题。通过展开深入的研究,提出了一种基于遗传免疫的粒子群优化算法,并将其应用于随机共振的关键参数寻优过程,由此进一步提出了基于遗传免疫粒子群优化的自适应随机共振算法,并采用轴承故障实验数据进行了分析与验证。
(3)针对小波理论实际应用过程中存在难以构造理想小波基函数的问题,研究了基于第二代小波变换的滚动轴承故障诊断方法。该方法利用第二代小波变换将滚动轴承故障振动信号分解到不同尺度,提取出共振频带,然后利用Hilbert变换进行解调,再对解调后的信号进行频谱分析得到小波包络谱,从包络谱上获取轴承故障特征信息。通过轴承实验数据应用与分析表明,该方法准确地提取了滚动轴承不同损伤程度故障的特征频率,实现了轴承故障的定量诊断。
(4)针对神经网络本身性能难以继续提高的问题,研究了基于第二代小波变换与神经网络的滚动轴承智能诊断方法。本文从提高神经网络输入端的信号质量入手,利用第二代小波变换与特征评估方法,提出了一种基于第二代小波与神经网络相结合的滚动轴承智能诊断模型,并将该模型应用于实验分析与工程实践中。结果表明从第二代小波分解后信号中提取的联合特征能够揭示更多的故障信息；特征评估方法能够针对诊断对象的健康状态分类选择其相应的敏感特征,大大提高了BP神经网络分类的准确率,验证了本文所建立的智能诊断模型的有效性。
(5)针对滚动轴承故障属于典型小样本的特征,研究了基于参数优化支持向量机的滚动轴承智能诊断方法。基本支持向量机方法存在模型参数不易合理选取而影响到算法性能的问题,本文在详细分析各参数对分类模型的影响的基础上,建立了参数优化模型,并采用遗传免疫粒子群算法作为优化方法,建立了基于遗传免疫粒子群和支持向量机的智能诊断模型,最后将该模型用于轴承故障诊断中。结果表明,该算法不但实现了对SVM分类模型参数的自动优化,提高了SVM分类模型的故障诊断精度,而且对分散程度较大、聚类性较差的故障样本分类有较强的适用性。
通过论文上述内容研究,优化了目前的应用于滚动轴承不同故障条件下的诊断算法,并进行了实验验证,为旋转机械的故障诊断提供了新方向。
Rolling bearing is one of the most significant elements in rotating machines yet with the characteristics of high damage rate and large lifetime randomness caused by process and environmental factors. The malfunction of rotating machine is various whereas about one third is resulted from rolling bearing fault. Therefore, to handle the working status and the fault formation as well as development of rolling bearing is currently important topic in the area of mechanical fault diagnosis.
In this paper, the bearing signal acquisition method is improved through analyzing the vibration principles, fault causes and signal features of the bearing architecture. The ZigBee technique is adopted to reduce the complexity of the fault diagnosis system, hence to improve the efficiency. Mechanism analysis and intelligence diagnosis are the current two ways to detect and diagnose the rolling bearing fault from vibrate signals. The common methods for mechanism analysis are stochastic resonance and wavelet analysis, and for intelligence diagnosis are neural networks and support vector machine. However, these methods above cannot conceal their disadvantages in practical application which will largely influent the effectiveness of bearing fault detection and diagnosis. Hence, it is considered of great significance to make a further research and exploration on the problems and disadvantages of current methods, mainly focused on the mechanism analysis and intelligent diagnosis of rolling bearing fault and stood on the developing theories and methods.
(1) Aimed to solve the problem of the less flexibility and high fault rate in the traditional signal acquisition system, a wireless acquisition network of rolling bearing vibration signal has been designed based on the analysis of the vibration principles, fault causes and signal features. The acquisition network adopted802.15.4and ZigBee as standard protocol. With the transmission rate of250kbps and a mode of wireless deployment, it reduces the complexity of a system and its fault rate, supporting the followed diagnosing method of bearing fault in principle as well.
(2) This paper proposes a stochastic resonance method based on inheritance immune partial swarm optimization concentrated on the problem that happens where useful messages are often flooded by the noises. This method not only realize the successful extraction of weak signals in large noises background, but also solve the confinement that the basic stochastic cannot work in processing large parameters signals but only small ones. Also a PSO based inheritance immune method is put forward after further experiment which is applied in the key parameter optimization of stochastic resonance. At last, a self-adapted stochastic resonance method based on inheritance immune partial swarm optimization is raised and corresponding analysis and test are conducted with practical bearing fault data.
(3) A diagnosis method of rolling bearing fault based on the second generation wavelet transform (SGWT) is proposed used to solve the difficulties in constructing ideal wavelet basis functions in the practical application of wavelet theories. This method use SGWT to decompose the vibration signals of rolling bearing faults to different scales, and then extract the resonance frequency band. Then, the Hilbert transform is used to demodulate the signals, and the frequency analysis of the signals demodulated has been done to obtain the wavelet spectra from which the fault characteristic information of rolling bearing are obtained. Practice and analysis on bearing data manifest that this method extract the feature frequencies of damaged bearings on all ranks respectively and realize the quantitative diagnosis of rolling bearing fault successfully.
(4) As for the self-limitation in neutral networks, an intelligent diagnosis method of rolling bearing based on SGWT and neural network is under experimenting. Staring from the quality improvement of neutral network import signals, this paper proposes an intelligent diagnosis model of rolling bearing based on SGWT together with neural network which takes the advantage of SGWT and feature assessment both. When put into experiment analysis and engineering practice, this model shows that,for one thing,more fault information can be revealed from the combined features extracted from SGWT decomposed signals, for another, aimed to properly classify the options on sensitive features based on various diagnosing object health status, feature assessment can largely increase the accuracy in BP (Back Propagation) neural network classification, which manifests the effectiveness of the intelligent diagnosis model set in this paper.
(5) As for the problem that rolling bearing fault is of typical small sampled feature, a parameter optimization support vector machine based rolling bearing intelligent diagnosis method is proposed. The influence on the efficiency of algorithm caused by the difficult in selecting proper model parameters in basic support vector machine can be removed by the proposal of genetic-immune particle swarm optimization (PSO) algorithm and support vector machine based intelligent diagnosis model, which is set on the analysis of parameter influences, model of parameter optimization and adoption of optimized genetic-immune particle swarm optimization (PSO) algorithm. Furthermore, the forecasting model is used to diagnose bearing fault. The results show that diagnosis model of SVM optimized by genetic-immune PSO algorithm can achieve automatic optimization of parameters, increase diagnosis accuracy than the conventional cross-validation algorithm, and is more fitting to classify the faulty samples scattered greatly.
Based on the varied shortcomings of current rolling bearing fault diagnosing algorithm, this paper proposes a series of optimization algorithm. Verified by experiment, the algorithms all get a good result. So the research in this paper provides an orientation for Rotating machinery fault diagnosing.
本论文通过分析滚动轴承振动机理、失效原因和信号特征,对轴承振动信号的采集方法进行了改进,采用无线传感器网络技术降低故障诊断系统的复杂性、提升诊断系统的效率。利用滚动轴承振动信号实现其故障检测与诊断,目前主要有机理分析和智能诊断两条途径。机理分析常用方法有随机共振和小波分析等；智能诊断常用方法有神经网络和支持向量机等。但以上各方法在实际应用中均存在其不足之处,从而影响到轴承故障检测与诊断的效果。为此,本文认为非常有必要立足于不断发展的新理论和新方法,紧紧围绕滚动轴承故障机理分析与智能诊断现有方法存在的问题与不足展开研究与探讨。
(1)针对传统有线传感器网络信息采集灵活性差、故障率高的问题,本文在分析滚动轴承振动机理、失效原因和振动信号特征的基础上,设计了滚动轴承振动信号无线采集网络,以802.15.4和ZigBee协议为标准,采用250kbps(?)勺传输速率和无线部署的方式,降低系统复杂性和故障率,为后续轴承故障诊断方法提供基础原理性的支持。
(2)针对噪声较强有用信号较弱环境下的轴承故障问题,研究了一种基于遗传免疫优化粒子群算法的随机共振方法。该方法不仅实现了强噪声背景下的微弱信号提取,而且解决了基本随机共振理论只能处理微弱的小参数信号、不能处理轴承振动这类大参数信号问题。通过展开深入的研究,提出了一种基于遗传免疫的粒子群优化算法,并将其应用于随机共振的关键参数寻优过程,由此进一步提出了基于遗传免疫粒子群优化的自适应随机共振算法,并采用轴承故障实验数据进行了分析与验证。
(3)针对小波理论实际应用过程中存在难以构造理想小波基函数的问题,研究了基于第二代小波变换的滚动轴承故障诊断方法。该方法利用第二代小波变换将滚动轴承故障振动信号分解到不同尺度,提取出共振频带,然后利用Hilbert变换进行解调,再对解调后的信号进行频谱分析得到小波包络谱,从包络谱上获取轴承故障特征信息。通过轴承实验数据应用与分析表明,该方法准确地提取了滚动轴承不同损伤程度故障的特征频率,实现了轴承故障的定量诊断。
(4)针对神经网络本身性能难以继续提高的问题,研究了基于第二代小波变换与神经网络的滚动轴承智能诊断方法。本文从提高神经网络输入端的信号质量入手,利用第二代小波变换与特征评估方法,提出了一种基于第二代小波与神经网络相结合的滚动轴承智能诊断模型,并将该模型应用于实验分析与工程实践中。结果表明从第二代小波分解后信号中提取的联合特征能够揭示更多的故障信息；特征评估方法能够针对诊断对象的健康状态分类选择其相应的敏感特征,大大提高了BP神经网络分类的准确率,验证了本文所建立的智能诊断模型的有效性。
(5)针对滚动轴承故障属于典型小样本的特征,研究了基于参数优化支持向量机的滚动轴承智能诊断方法。基本支持向量机方法存在模型参数不易合理选取而影响到算法性能的问题,本文在详细分析各参数对分类模型的影响的基础上,建立了参数优化模型,并采用遗传免疫粒子群算法作为优化方法,建立了基于遗传免疫粒子群和支持向量机的智能诊断模型,最后将该模型用于轴承故障诊断中。结果表明,该算法不但实现了对SVM分类模型参数的自动优化,提高了SVM分类模型的故障诊断精度,而且对分散程度较大、聚类性较差的故障样本分类有较强的适用性。
通过论文上述内容研究,优化了目前的应用于滚动轴承不同故障条件下的诊断算法,并进行了实验验证,为旋转机械的故障诊断提供了新方向。
Rolling bearing is one of the most significant elements in rotating machines yet with the characteristics of high damage rate and large lifetime randomness caused by process and environmental factors. The malfunction of rotating machine is various whereas about one third is resulted from rolling bearing fault. Therefore, to handle the working status and the fault formation as well as development of rolling bearing is currently important topic in the area of mechanical fault diagnosis.
In this paper, the bearing signal acquisition method is improved through analyzing the vibration principles, fault causes and signal features of the bearing architecture. The ZigBee technique is adopted to reduce the complexity of the fault diagnosis system, hence to improve the efficiency. Mechanism analysis and intelligence diagnosis are the current two ways to detect and diagnose the rolling bearing fault from vibrate signals. The common methods for mechanism analysis are stochastic resonance and wavelet analysis, and for intelligence diagnosis are neural networks and support vector machine. However, these methods above cannot conceal their disadvantages in practical application which will largely influent the effectiveness of bearing fault detection and diagnosis. Hence, it is considered of great significance to make a further research and exploration on the problems and disadvantages of current methods, mainly focused on the mechanism analysis and intelligent diagnosis of rolling bearing fault and stood on the developing theories and methods.
(1) Aimed to solve the problem of the less flexibility and high fault rate in the traditional signal acquisition system, a wireless acquisition network of rolling bearing vibration signal has been designed based on the analysis of the vibration principles, fault causes and signal features. The acquisition network adopted802.15.4and ZigBee as standard protocol. With the transmission rate of250kbps and a mode of wireless deployment, it reduces the complexity of a system and its fault rate, supporting the followed diagnosing method of bearing fault in principle as well.
(2) This paper proposes a stochastic resonance method based on inheritance immune partial swarm optimization concentrated on the problem that happens where useful messages are often flooded by the noises. This method not only realize the successful extraction of weak signals in large noises background, but also solve the confinement that the basic stochastic cannot work in processing large parameters signals but only small ones. Also a PSO based inheritance immune method is put forward after further experiment which is applied in the key parameter optimization of stochastic resonance. At last, a self-adapted stochastic resonance method based on inheritance immune partial swarm optimization is raised and corresponding analysis and test are conducted with practical bearing fault data.
(3) A diagnosis method of rolling bearing fault based on the second generation wavelet transform (SGWT) is proposed used to solve the difficulties in constructing ideal wavelet basis functions in the practical application of wavelet theories. This method use SGWT to decompose the vibration signals of rolling bearing faults to different scales, and then extract the resonance frequency band. Then, the Hilbert transform is used to demodulate the signals, and the frequency analysis of the signals demodulated has been done to obtain the wavelet spectra from which the fault characteristic information of rolling bearing are obtained. Practice and analysis on bearing data manifest that this method extract the feature frequencies of damaged bearings on all ranks respectively and realize the quantitative diagnosis of rolling bearing fault successfully.
(4) As for the self-limitation in neutral networks, an intelligent diagnosis method of rolling bearing based on SGWT and neural network is under experimenting. Staring from the quality improvement of neutral network import signals, this paper proposes an intelligent diagnosis model of rolling bearing based on SGWT together with neural network which takes the advantage of SGWT and feature assessment both. When put into experiment analysis and engineering practice, this model shows that,for one thing,more fault information can be revealed from the combined features extracted from SGWT decomposed signals, for another, aimed to properly classify the options on sensitive features based on various diagnosing object health status, feature assessment can largely increase the accuracy in BP (Back Propagation) neural network classification, which manifests the effectiveness of the intelligent diagnosis model set in this paper.
(5) As for the problem that rolling bearing fault is of typical small sampled feature, a parameter optimization support vector machine based rolling bearing intelligent diagnosis method is proposed. The influence on the efficiency of algorithm caused by the difficult in selecting proper model parameters in basic support vector machine can be removed by the proposal of genetic-immune particle swarm optimization (PSO) algorithm and support vector machine based intelligent diagnosis model, which is set on the analysis of parameter influences, model of parameter optimization and adoption of optimized genetic-immune particle swarm optimization (PSO) algorithm. Furthermore, the forecasting model is used to diagnose bearing fault. The results show that diagnosis model of SVM optimized by genetic-immune PSO algorithm can achieve automatic optimization of parameters, increase diagnosis accuracy than the conventional cross-validation algorithm, and is more fitting to classify the faulty samples scattered greatly.
Based on the varied shortcomings of current rolling bearing fault diagnosing algorithm, this paper proposes a series of optimization algorithm. Verified by experiment, the algorithms all get a good result. So the research in this paper provides an orientation for Rotating machinery fault diagnosing.
引文
[1]张润林.旋转机械故障机理与诊断技术[M].北京：机械工业出版社,2002.
[2]虞和济,韩庆大,李沈等.设备故障诊断工程[M].北京：冶金工业出版社,2001.
[3]王江萍等.设备状态监测与故障诊断技术及应用[M].西安：西北工业大学出版社,2003.
[4]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005.
[5]万频.随机共振在在信号检测中的研究与应用[D].广东工业大学,2011.
[6]李颖琼.基于小波分形理论的振动信号分析系统研究[D].浙江理工大学硕士学位论文,2011.
[7]梅宏斌.滚动轴承振动检测与诊断[M].北京：机械工业出版社,1995.
[8]张优云,谢又柏.状态监测故障诊断与现代设计技术[J].中国机械工程,1997,8(5)：101-103.
[9]孔亚林.基于振动信号的滚动轴承故障诊断方法研究[D].大连理工大学硕士学位论文,2006.
[10]陈夔蛟.基于振动信号的滚动轴承故障诊断研究[D].西安电子科技大学硕士学位论文,2011.
[11]王国峰.轴承疲劳剥落的早期诊断方法[J].大连海事大学学报,2002.
[12]杨龙兴,贾民平,许飞云.齿轮裂纹故障的诊断[J].中国机械工程,2003,14(9)：1621-1623.
[13]滕丽丽,唐涛,王明锋.基于振动分析的滚动轴承故障诊断技术概述及发展趋势[J].机械与电子,2011,23：123.
[14]Xu Yinge,Yan Yuling. Rolling Bearing Fault Detection Using Correlation Technique[J]. Applied Mathematics and Mechanics,1992,13(7):617-622.
[15]Bo Li,Mo-Yuen Chow,Yodyium Tipsuwam.Neural-Network-Based Motor Rolling Bearing Fault Diagnosis[J]. IEEE Transactions on Industrial Electronics,2000,47(5):1060-1069.
[16]Harris,Tedric A, Michael N.Kotzalas. Rolling Bearing Analysis[M]. the 5th Edition. CRC.2006:193-201.
[17]N.Tandon, A.Choudhury.A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings[M]. Tribology International,1999,32(8): 469-480.
[18]Silva,J.L.H,Cardoso,A.J.M.Bearing failures diagnosis in three-phase induction motors by extended Park.s vector approach[C]. Industrial Electronics Society,31st Annual Conference of IEEE, Nov.2005:2585-2590.
[19]苏文胜,郭正刚,张志新,李宏坤,王奉涛.基于虚拟仪器的汽车后桥性能检测系统的设计[J].仪器仪表学报08年增刊,2008,29(4)：594-597.主办单位：中国仪器仪表学会.
[20]陆爽,张子达,李萌.现代信号分析在滚动轴承故障诊断中的应用[J]——2004全国博士生学术论坛论文集(全国第2届博士论坛).2004.8哈尔滨.
[21]赵纪元,何正嘉,孟庆丰等.小波包-自回归谱分析在振动诊断中的应用[J].振动工程学报,1995,8(3)：198-203.
[22]祝海龙,屈梁生.自组织包络解调及其在轴承诊断中的应用[J].西安交通大学学报,2000,34(7)：77-81.
[23]张中明,卢文祥,杨叔子等.基于小波系数包络谱的滚动轴承故障诊断.振动工程学报,1998,11(3)：65-69.
[24]何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[25]唐英,孙巧.滚动轴承振动信号的小波奇异性故障检测研究.振动工程学报[J].2002, 15(1)：111-113.
[26]徐玉秀,原培新,邢钢.极大熵谱法及其在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[27]朱利民,钟秉林,贾民平.振动信号短时分析方法及在机械故障诊断中的应用[J].振动工程学报,2000,13(3)：400-405.
[28]徐刚,骆志高,李明义等.变工况运行的机械故障诊断研究[J].机械工程学报,2001,37(12)：104-107.
[29]林京,屈梁生.基于连续小波变换的信号检测技术与故障诊断[J].机械工程学报,2000,36(12)：95-100
[30]傅勤毅,张易程,应力军等.滚动轴承故障特征的小波提取方法[J].机械工程学报,2001,37(2)：30-32.
[31]胡岗.随机力与非线性系统[M].上海：上海科学教育出版社,1994.
[32]Gammaitoni L. Hanggi P. Jung P. Marchesoni F. Stochastic resonance[J]. Rev. Mod. Phys. 1998,70(1):223-287.
[33]Fauve S., Heslot F., Stochastic resonance in a bistable system[J]. Phys.Lett.,1983,97A:5-7.
[34]McNamara B., Wiesenfeld K., Roy R.. Observation of stochastic resonance in a ring laser[J]. Phys.Rev.Lett.,1988,60(25):2626-2629.
[35]李强.机械设备早期故障预示中的微弱信号检测技术研究[D].天津：天津大学,2008.
[36]杨定新,胡笃庆.随机共振在微弱信号检测中的数值仿真[J].国防科技大学学报,2003,25(6)：91—94.
[37]Dykman M.I., Mannella R..Giant nonlinearity in the low-frequency response of a fluctuating bistable system[J]. Phys.Rev.E.,1993,47(3):1629-1632.
[38]Mitain S., Kosko B..Neural fuzzy stochastic resonance[C]. IEEE International Conference on System,Man and Cybernetics,1998,3:2237-2242.
[39]Ye Q.H.,Huang H.N.,et al. A study on the parameters of bistable stochastic resonance systems and adaptive stochastic resonance[J]. Proceedings of the 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing,Changsha,2003:484-488.
[40]冷永刚.大信号变尺度随机共振的机理分析及其工程应用研究[D].天津：天津大学,2004.
[41]张彦周.基于支持向量机的测井曲线预测储层参数方法[D].西安：西安科技大学理学院,2006.
[42]肖健华,樊可清,吴今培等.应用于故障诊断的SVM理论研究[J].振动、测试与诊断,2001,21(4)：258-262.
[43]翟津.基于支持向量机的内模控制算法研究及应用[D].河北：华北电力大学,2008
[44]袁胜发,褚福磊.支持向量机及其在机械故障诊断中的应用[J].振动与冲击,2007,11：29-34.
[45]Alessandra Flammini, Paolo Ferrari, DanieleMarioli,et al. Wiredand Wireless Sensor Networks for Industrial Applieations[J].MieroelectroniesJoumal,2009,40:1322-1336.
[46]Wade R., Miehel W. M., PeterF.Ten Emerging Technologies that Will Change the world[J]. Teehnology Review,2003,V106(1):33-4.
[47]汤宝平,贺超.基于无线传感器网络的机械振动监测模式[J].中国机械工程,2009,20(22)：2737-2741.
[48]崔逊学,赵湛,王成.无线传感器网络的领域应用与设计技术[M].北京：国防工业出版社,2009.
[49]张少军.无线传感器网络技术及应用[M].北京：中国电力出版社,2010.
[50]杨祥龙,汪乐宇.随机共振技术在弱信号检测中的应用[J].电路与系统学报,2001,6(2)：94-97.
[51]陈帅,岳迎春,徐巍,等.小波时间序列对非平稳信号中突变点的辨识与处理[J].测绘科学,2012.
[52]郑建国,石智,权豫西.非平稳信号的小波包阈值去噪方法[J].信息技术,2007,3：21-23.
[53]高艳丽,刘诗斌.基于PSO的神经网络在传感器数据融合中的应用[J].传感技术学报,2006,19(4)：1284-1289.
[54]Liqun Hou,Neil W.Bergmann. System Requirements for Industrial Wireless Sensor Networks [A].2010 IEEE Conference on Emerging Teehnologies and Factory Automation (ETFA)[C]. SPain.2010:1-8.
[55]潘俊林,樊可清,李炎华.基于小波神经网络的振动信号消噪[J].科技信息(科学教研),2008,2：32-38.
[56]陈伟根,范海炉,王有元,等.基于小波能量与神经网络的断路器振动信号识别方法[J].电力自动化设备,2008,2：33-36.
[57]杨文位.基于振动信号的柴油机神经网络故障诊断研究[D].西北农林科技大学,2005.
[58]丁世飞,齐丙娟,谭红艳.支持向量机理论与算法综述[J].电子科技大学学报,2011,1：4-12.
[59]周俊丽,周久华.滚动轴承故障机理与诊断策略[J].四川兵工学报,2012,33(4)：56-60.
[60]杨国安.机械设备故障诊断实用技术[M].北京：中国石化出版社,2007.
[61]宋晓美,孟繁超,张玉.基于包络解调分析的滚动轴承故障诊断研究[J].仪器仪表与分析监测.2012(1).
[62]周瑞峰.滚动轴承故障智能诊断方法的研究与实现[D].大连理工大学,2009.
[63]孟涛.齿轮与滚动轴承故障的振动分析与诊断[D].西北工业大学,2003.
[64]Youhong Lu and JoeM. Morris. Gabor Expasion for Adaptive Eeho Cancellation IEEE Signal Proeessing Magazine, Mar.1999:68-80.
[65]Guanghong Gai. The proeessing of rotor startup signals based on empirical mode decomposition. MeChaniealSystems and Signal Proeessing,2006,20:222-235.
[66]潘青旺.冲击脉冲法在滚动轴承故障诊断中的应用[J].中国设备工程,2008,8：48-49.
[67]陈恩利,张玺,申永军,曹轩铭.基于SVD降噪和盲信号分离的滚动轴承故障诊断[J].振动与冲击,2012,31(23)：185-190.
[68]陈恩利,吴勇军,申永军.基于改进奇异值分解技术的齿轮调制故障特征提取[J].振动工程学报,2008,21(5)：530-534.
[69]陈亚农,郜普刚,何田,等.局部均值分解在滚动轴承故障综合诊断中的应用[J].振动与冲击,2012,31(3)：73-78.
[70]李艳妮.旋转机械故障机理与故障特征提取技术研究[D].北京化工大学硕士论文,2007.
[71]温志渝,温中泉,贺学锋,等.振动式压电发电机及其在无线传感器网络中的应用[J].机械工程学报,2008,44(11)：75-79.
[72]Beeby S P, Toran R N,Tudor M J, et al. A micro electromagnetic generator for vibration energy harvesting[J]. Journal of Micromechanics and Mieroengineering,2007,17:1257-1265.
[73]张安华.机电设备状态监测与故障诊断技术[M].西安：西北工业大学出版社,1995.8.
[74]何斌,戚佳杰.小波分析在滚动轴承故障诊断中的应用研究[A].第九届全国振动理论及应用学术会议论文集[C],2007.
[75]Fang H B, Liu J Q, Xu Z Y, et al. Fabrication and performace of MEMS-based Piezoelectric power generator for vibration energy harvesting[J]. Microelectronics Journal, 2006,37:1280-1284.
[76]陈安都,刘少强.振动信号在线检测的超低功耗无线传感器节点设计[J].计算机工程与科学,2008,30(4)：95-97.
[77]韩旭东,曹建海.基于IEEE802.15.4无线智能化传感器网络研究及其性能分析[J].电工 技术杂志,2004(9)：63-68.
[78]徐剑平,耿世均,马廷风,等.超低功耗电子电路系统设计原则[J].电子技术应用,2003,29(2)：78-80.
[79]胡大可.MSP430系列FLASH型超低功耗16位单片机[M].北京：北京航空航天大学出版社,2001.
[80]喻言,李宏伟,欧进萍.结构监测的无线加速度传感器设计与制作[J].传感器技术学报,2004,17(3)：463-466.
[81]申飞,吴仲城,孟明,等.网络化传感器节点的低功耗设计[J]仪器仪表学报,2004,25(21)：259-260.
[82]阳建宏,杨涛,杨德斌.无线传感器网络智能振动监测节点设计[J].传感器与仪器仪表,2008,24(7)：153-155.
[83]蔡巍巍,汤宝平,黄庆卿.面向机械振动信号采集的无线传感器网络节点设计[J].振动与冲击,2013,32(1)：73-77.
[84]陈敏,胡茑庆,秦国军等.参数调节随机共振在机械系统早期故障检测中的应用[J].机械工程学报,2009,45(4)：131-135.
[85]孙迎利,刘东升,米双山.基于变参数随机共振的齿轮早期故障诊断[J].仪表技术,2010,1：27-29,32.
[86]江波.自适应随机共振系统及小信号检测方法的研究[D].浙江：浙江大学,2003.
[87]杨宁,张培林,马乔,王江涛.自适应随机共振在微弱信号检测中的应用[J].机械强度,2012,06.
[88]Kennedy J, Eberhart R C. Particle swarm optimization[C]. IEEE International conference on neural networks. Perth Piscataway NJ Australia:IEEE Service Center,1995,4.1942-1948.
[89]蔺媛媛.蚁群算法的参数优化[J].天津工程师范学院学报,2009,19(3)：30-33.
[90]王联国,洪毅,赵付青,余冬梅.一种简化的人工鱼群算法[J].小型微型计算机系统,2009,30(8)：1663-1667.
[91]王培崇,钱旭,王月.差分进化计算研究综述[J].计算机工程与应用,2009,45(28)：13-16.
[92]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究[D].中北大学,2009.
[93]张兴华.基于粒子群优化的模糊神经网络的柴油机故障诊断[M].中北大学,2012.
[94]赵艳菊.强噪声背景下机械设备微弱信号的提取与检测技术研究[D].天津：天津大学,2008.
[95]Shi Y, Eberhart R.C. A modified particle swarm optimizer[A].IEEE Int. Conference on evolutionary computation[C], Piscataway, NJ, IEEE Service Center,1998,69-73.
[96]王学峰,王文峰.基于免疫网络算法的SVM参数选择[J].计算机应用与软件,2009,26(9)：266-268.
[97]陈果.航空器检测与诊断技术导论[M].北京：中国民航出版社,2007.
[98]Qingbo He, JunWang, Yongbin Liu, et al. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines[J]. Mechanical Systems and Signal Processing,2012,28:443-457.
[99]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.
[100]Liu B.Selection of wavelet packet basis for rotating machinery fault diagnosis[J] Journal of Sound and Vibration,2005,(284):567-582.
[101]C.Smith, C.M. Akujuobi, P. Hamory. An approach to vibration analysis using wavelets in an application of aircraft health monitoring[J]. Mechanical Systems and Signal Processing,2007,21(3):1255-1272.
[102]A.K. Darpe.A novel way to detect transverse surface crack in a rotating shaft [J].Journal of Sound and Vibration,2007,305(1-2):151-171.
[103]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安：西安交通大学出版社,2007.
[104]W Sweldens.The lifting scheme:A construction of second generation wavelets[J]. SIAM J. Math.Aanal,1997,29(2):511-546.
[105]Sweldens W. The Construction and Application of Wavelets in Numerical analysis[D].Belgium:Katholieke Universiteit Leuven,1995.
[106]C.D. Duan, Z.J. He, H K. Jiang. A sliding window feature extraction method for rotating machinery based on the lifting scheme[J]. Journal of Sound and Vibration 2007,299(4-5): 774-785
[107]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[J].振动与冲击.2007,26(2)：1013,32.
[108]R. L. Claypoole.Adaptive wavelet transform via lifting [D].Deportment of electrical and computer engineering, Rice University, Houston, Texas, October 1999.
[109]Jawerth B, Sweldens W. An overview of wavelet based multiresulotion analysis.URL: http://cm.bell-labs.com/cm/ms/who/wim/.
[110]段晨东.基于第二代小波变换的故障诊断技术研究[D].西安：西安交通大学,2005.
[111]Y. Meyer. Wavelets:Algorithms and Applications[M]. SIAM.1993:24-368.
[112]X. Li, L. Qu, G. Wen, et al. Application of wavelet packet analysis for fault detection in electro-mechanical systems based on torsional vibration measurement[J]. Mechanical Systems and Signal Processing.2003,17(6):1219-1235.
[113]X.F. Fan, M.J. Zuo. Gearbox fault detection using Hilbert and wavelet packet transform[J]. Mechanical Systems and Signal Processing.2006,20(4):966-982.
[114]J. Rafiee, F. Arvani, A. Harifi, M.H. Sadeghi. Intelligent condition monitoring of a gearbox using artificial neural network[J]. Mechanical Systems and Signal Processing.2007,21(4):1746-1754.
[115]Q.S.Xu,Z.G.Lia. Recognition of wear mode using multi-variable synthesis approach based on wavelet packet and improved three-line method[J]. Mechanical Systems and Signal Processing.2007,21(8):3146-3166.
[116]段晨东.基于第二代小波变换的故障诊断技术研究[D].西安交通大学博士学位论文.2005：40-60.
[117]姜洪开,王仲生.第二代小波包构造及发动机微弱损伤识别[J].北京航空航天大学学报.2007,33(7)：777-780.
[118]傅勤毅,傅俭毅,王峰林.一种无频带错位的小波包算法.振动工程学报[J].1999,12(3)：423-428.
[119]丁康,陈建林,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.
[120]陈树越.余红英,刘广璞.BP网络算法及其在故障诊断中的应用述评[J].振动、测试与诊断,2001.
[121]颜延虎,钟秉林,黄仁.神经网络技术及其在旋转机械故障诊断中的应用[J].振动工程学报,1993.
[122]吴今培.智能故障诊断与神经网络[M].北京：科学出版社,1997.
[123]Yang B S, Han T, An J L. ART-KOHONEN neural network for fault diagnosis of rotating machinery [J].Mechanical Systems and Signal Processing,2004,18 (3):645-657.
[124]朱博,胡艳,赵永标.人工神经网络在故障诊断系统中的应用[J].舰船电子工程,2005,(1)：91-95.
[125]Rumelhart D. E., Meclelland J. L. Parallel Distributed Processing [M], MIT,1986.
[126]雷亚国,混合智能技术及其在故障诊断中的应用研究[D],西安交通大学博士学位论 文.2007
[127]Guo H,Jack L B,Nandi A K. Feature generation using genetic programming with application to fault classification[J].IEEE Transaction on System, Man, and Cybernetics,2005,35(1):89-99.
[128]Samanta B. Artificial neural networks and genetic algorithms for gear fault detection[J].Mechanical System and Signal Processing,2004,18(5):1273-1282.
[129]Jack L B,Nandi A K. Fault detection using support vector machines and artificial neural networks augmented by genetic algorithms[J].Mechanical System and Signal Processing,2002,16(3):373-390.
[130]Yang B S, Han T, Hwang W W. Fault diagnosis of rotating machinery based on multi-class support vector machines [J]. Journal of Mechanical Science and Technology,2005, 19(3):846-859.
[131]关惠玲,韩捷.设备故障诊断专家系统原理及实践[M].北京：机械工业出版社,2000.
[132]Vladimir Vapnik. The Nature of Statistical Learning Theory[M]. NY:Springer,1995.
[133]李应红,尉询楷,刘建勋.支持向量机的工程应用[M].北京：兵器工业出版社,2004
[134]祝海生.统计学习理论的工程应用[D].西安：西安交通大学,2004.
[135]张艳秋,王蔚.利用遗传算法优化的支持向量机垃圾邮件分类[J].计算机应用,2009,29(10)：2755-2757.
[136]李应红,尉询楷,刘建勋.支持向量机的工程应用[M].北京：兵器工业出版社,2004.
[137]李巍华,史铁林,杨叔子.基于非线性判别分析的故障分类方法研究[J].建筑热能通风空调,2005,18(2)：133-138.
[138]王学峰,王文峰,基于免疫网络算法的SVM参数选择[J],计算机应用与软件,2009,26(9)：266-268.
[139]胡金海,谢寿生,骆广琦等.基于核函数Fisher鉴别分析的特征提取方法[J].振动、测试与诊断,2008,28(4)：322-326.
[2]虞和济,韩庆大,李沈等.设备故障诊断工程[M].北京：冶金工业出版社,2001.
[3]王江萍等.设备状态监测与故障诊断技术及应用[M].西安：西北工业大学出版社,2003.
[4]孙利民,李建中,陈渝,等.无线传感器网络[M].北京：清华大学出版社,2005.
[5]万频.随机共振在在信号检测中的研究与应用[D].广东工业大学,2011.
[6]李颖琼.基于小波分形理论的振动信号分析系统研究[D].浙江理工大学硕士学位论文,2011.
[7]梅宏斌.滚动轴承振动检测与诊断[M].北京：机械工业出版社,1995.
[8]张优云,谢又柏.状态监测故障诊断与现代设计技术[J].中国机械工程,1997,8(5)：101-103.
[9]孔亚林.基于振动信号的滚动轴承故障诊断方法研究[D].大连理工大学硕士学位论文,2006.
[10]陈夔蛟.基于振动信号的滚动轴承故障诊断研究[D].西安电子科技大学硕士学位论文,2011.
[11]王国峰.轴承疲劳剥落的早期诊断方法[J].大连海事大学学报,2002.
[12]杨龙兴,贾民平,许飞云.齿轮裂纹故障的诊断[J].中国机械工程,2003,14(9)：1621-1623.
[13]滕丽丽,唐涛,王明锋.基于振动分析的滚动轴承故障诊断技术概述及发展趋势[J].机械与电子,2011,23：123.
[14]Xu Yinge,Yan Yuling. Rolling Bearing Fault Detection Using Correlation Technique[J]. Applied Mathematics and Mechanics,1992,13(7):617-622.
[15]Bo Li,Mo-Yuen Chow,Yodyium Tipsuwam.Neural-Network-Based Motor Rolling Bearing Fault Diagnosis[J]. IEEE Transactions on Industrial Electronics,2000,47(5):1060-1069.
[16]Harris,Tedric A, Michael N.Kotzalas. Rolling Bearing Analysis[M]. the 5th Edition. CRC.2006:193-201.
[17]N.Tandon, A.Choudhury.A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings[M]. Tribology International,1999,32(8): 469-480.
[18]Silva,J.L.H,Cardoso,A.J.M.Bearing failures diagnosis in three-phase induction motors by extended Park.s vector approach[C]. Industrial Electronics Society,31st Annual Conference of IEEE, Nov.2005:2585-2590.
[19]苏文胜,郭正刚,张志新,李宏坤,王奉涛.基于虚拟仪器的汽车后桥性能检测系统的设计[J].仪器仪表学报08年增刊,2008,29(4)：594-597.主办单位：中国仪器仪表学会.
[20]陆爽,张子达,李萌.现代信号分析在滚动轴承故障诊断中的应用[J]——2004全国博士生学术论坛论文集(全国第2届博士论坛).2004.8哈尔滨.
[21]赵纪元,何正嘉,孟庆丰等.小波包-自回归谱分析在振动诊断中的应用[J].振动工程学报,1995,8(3)：198-203.
[22]祝海龙,屈梁生.自组织包络解调及其在轴承诊断中的应用[J].西安交通大学学报,2000,34(7)：77-81.
[23]张中明,卢文祥,杨叔子等.基于小波系数包络谱的滚动轴承故障诊断.振动工程学报,1998,11(3)：65-69.
[24]何晓霞,沈玉娣,张西宁.连续小波变换在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[25]唐英,孙巧.滚动轴承振动信号的小波奇异性故障检测研究.振动工程学报[J].2002, 15(1)：111-113.
[26]徐玉秀,原培新,邢钢.极大熵谱法及其在滚动轴承故障诊断中的应用[J].机械科学与技术,2001,20(4)：571-574.
[27]朱利民,钟秉林,贾民平.振动信号短时分析方法及在机械故障诊断中的应用[J].振动工程学报,2000,13(3)：400-405.
[28]徐刚,骆志高,李明义等.变工况运行的机械故障诊断研究[J].机械工程学报,2001,37(12)：104-107.
[29]林京,屈梁生.基于连续小波变换的信号检测技术与故障诊断[J].机械工程学报,2000,36(12)：95-100
[30]傅勤毅,张易程,应力军等.滚动轴承故障特征的小波提取方法[J].机械工程学报,2001,37(2)：30-32.
[31]胡岗.随机力与非线性系统[M].上海：上海科学教育出版社,1994.
[32]Gammaitoni L. Hanggi P. Jung P. Marchesoni F. Stochastic resonance[J]. Rev. Mod. Phys. 1998,70(1):223-287.
[33]Fauve S., Heslot F., Stochastic resonance in a bistable system[J]. Phys.Lett.,1983,97A:5-7.
[34]McNamara B., Wiesenfeld K., Roy R.. Observation of stochastic resonance in a ring laser[J]. Phys.Rev.Lett.,1988,60(25):2626-2629.
[35]李强.机械设备早期故障预示中的微弱信号检测技术研究[D].天津：天津大学,2008.
[36]杨定新,胡笃庆.随机共振在微弱信号检测中的数值仿真[J].国防科技大学学报,2003,25(6)：91—94.
[37]Dykman M.I., Mannella R..Giant nonlinearity in the low-frequency response of a fluctuating bistable system[J]. Phys.Rev.E.,1993,47(3):1629-1632.
[38]Mitain S., Kosko B..Neural fuzzy stochastic resonance[C]. IEEE International Conference on System,Man and Cybernetics,1998,3:2237-2242.
[39]Ye Q.H.,Huang H.N.,et al. A study on the parameters of bistable stochastic resonance systems and adaptive stochastic resonance[J]. Proceedings of the 2003 IEEE International Conference on Robotics, Intelligent Systems and Signal Processing,Changsha,2003:484-488.
[40]冷永刚.大信号变尺度随机共振的机理分析及其工程应用研究[D].天津：天津大学,2004.
[41]张彦周.基于支持向量机的测井曲线预测储层参数方法[D].西安：西安科技大学理学院,2006.
[42]肖健华,樊可清,吴今培等.应用于故障诊断的SVM理论研究[J].振动、测试与诊断,2001,21(4)：258-262.
[43]翟津.基于支持向量机的内模控制算法研究及应用[D].河北：华北电力大学,2008
[44]袁胜发,褚福磊.支持向量机及其在机械故障诊断中的应用[J].振动与冲击,2007,11：29-34.
[45]Alessandra Flammini, Paolo Ferrari, DanieleMarioli,et al. Wiredand Wireless Sensor Networks for Industrial Applieations[J].MieroelectroniesJoumal,2009,40:1322-1336.
[46]Wade R., Miehel W. M., PeterF.Ten Emerging Technologies that Will Change the world[J]. Teehnology Review,2003,V106(1):33-4.
[47]汤宝平,贺超.基于无线传感器网络的机械振动监测模式[J].中国机械工程,2009,20(22)：2737-2741.
[48]崔逊学,赵湛,王成.无线传感器网络的领域应用与设计技术[M].北京：国防工业出版社,2009.
[49]张少军.无线传感器网络技术及应用[M].北京：中国电力出版社,2010.
[50]杨祥龙,汪乐宇.随机共振技术在弱信号检测中的应用[J].电路与系统学报,2001,6(2)：94-97.
[51]陈帅,岳迎春,徐巍,等.小波时间序列对非平稳信号中突变点的辨识与处理[J].测绘科学,2012.
[52]郑建国,石智,权豫西.非平稳信号的小波包阈值去噪方法[J].信息技术,2007,3：21-23.
[53]高艳丽,刘诗斌.基于PSO的神经网络在传感器数据融合中的应用[J].传感技术学报,2006,19(4)：1284-1289.
[54]Liqun Hou,Neil W.Bergmann. System Requirements for Industrial Wireless Sensor Networks [A].2010 IEEE Conference on Emerging Teehnologies and Factory Automation (ETFA)[C]. SPain.2010:1-8.
[55]潘俊林,樊可清,李炎华.基于小波神经网络的振动信号消噪[J].科技信息(科学教研),2008,2：32-38.
[56]陈伟根,范海炉,王有元,等.基于小波能量与神经网络的断路器振动信号识别方法[J].电力自动化设备,2008,2：33-36.
[57]杨文位.基于振动信号的柴油机神经网络故障诊断研究[D].西北农林科技大学,2005.
[58]丁世飞,齐丙娟,谭红艳.支持向量机理论与算法综述[J].电子科技大学学报,2011,1：4-12.
[59]周俊丽,周久华.滚动轴承故障机理与诊断策略[J].四川兵工学报,2012,33(4)：56-60.
[60]杨国安.机械设备故障诊断实用技术[M].北京：中国石化出版社,2007.
[61]宋晓美,孟繁超,张玉.基于包络解调分析的滚动轴承故障诊断研究[J].仪器仪表与分析监测.2012(1).
[62]周瑞峰.滚动轴承故障智能诊断方法的研究与实现[D].大连理工大学,2009.
[63]孟涛.齿轮与滚动轴承故障的振动分析与诊断[D].西北工业大学,2003.
[64]Youhong Lu and JoeM. Morris. Gabor Expasion for Adaptive Eeho Cancellation IEEE Signal Proeessing Magazine, Mar.1999:68-80.
[65]Guanghong Gai. The proeessing of rotor startup signals based on empirical mode decomposition. MeChaniealSystems and Signal Proeessing,2006,20:222-235.
[66]潘青旺.冲击脉冲法在滚动轴承故障诊断中的应用[J].中国设备工程,2008,8：48-49.
[67]陈恩利,张玺,申永军,曹轩铭.基于SVD降噪和盲信号分离的滚动轴承故障诊断[J].振动与冲击,2012,31(23)：185-190.
[68]陈恩利,吴勇军,申永军.基于改进奇异值分解技术的齿轮调制故障特征提取[J].振动工程学报,2008,21(5)：530-534.
[69]陈亚农,郜普刚,何田,等.局部均值分解在滚动轴承故障综合诊断中的应用[J].振动与冲击,2012,31(3)：73-78.
[70]李艳妮.旋转机械故障机理与故障特征提取技术研究[D].北京化工大学硕士论文,2007.
[71]温志渝,温中泉,贺学锋,等.振动式压电发电机及其在无线传感器网络中的应用[J].机械工程学报,2008,44(11)：75-79.
[72]Beeby S P, Toran R N,Tudor M J, et al. A micro electromagnetic generator for vibration energy harvesting[J]. Journal of Micromechanics and Mieroengineering,2007,17:1257-1265.
[73]张安华.机电设备状态监测与故障诊断技术[M].西安：西北工业大学出版社,1995.8.
[74]何斌,戚佳杰.小波分析在滚动轴承故障诊断中的应用研究[A].第九届全国振动理论及应用学术会议论文集[C],2007.
[75]Fang H B, Liu J Q, Xu Z Y, et al. Fabrication and performace of MEMS-based Piezoelectric power generator for vibration energy harvesting[J]. Microelectronics Journal, 2006,37:1280-1284.
[76]陈安都,刘少强.振动信号在线检测的超低功耗无线传感器节点设计[J].计算机工程与科学,2008,30(4)：95-97.
[77]韩旭东,曹建海.基于IEEE802.15.4无线智能化传感器网络研究及其性能分析[J].电工 技术杂志,2004(9)：63-68.
[78]徐剑平,耿世均,马廷风,等.超低功耗电子电路系统设计原则[J].电子技术应用,2003,29(2)：78-80.
[79]胡大可.MSP430系列FLASH型超低功耗16位单片机[M].北京：北京航空航天大学出版社,2001.
[80]喻言,李宏伟,欧进萍.结构监测的无线加速度传感器设计与制作[J].传感器技术学报,2004,17(3)：463-466.
[81]申飞,吴仲城,孟明,等.网络化传感器节点的低功耗设计[J]仪器仪表学报,2004,25(21)：259-260.
[82]阳建宏,杨涛,杨德斌.无线传感器网络智能振动监测节点设计[J].传感器与仪器仪表,2008,24(7)：153-155.
[83]蔡巍巍,汤宝平,黄庆卿.面向机械振动信号采集的无线传感器网络节点设计[J].振动与冲击,2013,32(1)：73-77.
[84]陈敏,胡茑庆,秦国军等.参数调节随机共振在机械系统早期故障检测中的应用[J].机械工程学报,2009,45(4)：131-135.
[85]孙迎利,刘东升,米双山.基于变参数随机共振的齿轮早期故障诊断[J].仪表技术,2010,1：27-29,32.
[86]江波.自适应随机共振系统及小信号检测方法的研究[D].浙江：浙江大学,2003.
[87]杨宁,张培林,马乔,王江涛.自适应随机共振在微弱信号检测中的应用[J].机械强度,2012,06.
[88]Kennedy J, Eberhart R C. Particle swarm optimization[C]. IEEE International conference on neural networks. Perth Piscataway NJ Australia:IEEE Service Center,1995,4.1942-1948.
[89]蔺媛媛.蚁群算法的参数优化[J].天津工程师范学院学报,2009,19(3)：30-33.
[90]王联国,洪毅,赵付青,余冬梅.一种简化的人工鱼群算法[J].小型微型计算机系统,2009,30(8)：1663-1667.
[91]王培崇,钱旭,王月.差分进化计算研究综述[J].计算机工程与应用,2009,45(28)：13-16.
[92]魏秀业.基于粒子群优化的齿轮箱智能故障诊断研究[D].中北大学,2009.
[93]张兴华.基于粒子群优化的模糊神经网络的柴油机故障诊断[M].中北大学,2012.
[94]赵艳菊.强噪声背景下机械设备微弱信号的提取与检测技术研究[D].天津：天津大学,2008.
[95]Shi Y, Eberhart R.C. A modified particle swarm optimizer[A].IEEE Int. Conference on evolutionary computation[C], Piscataway, NJ, IEEE Service Center,1998,69-73.
[96]王学峰,王文峰.基于免疫网络算法的SVM参数选择[J].计算机应用与软件,2009,26(9)：266-268.
[97]陈果.航空器检测与诊断技术导论[M].北京：中国民航出版社,2007.
[98]Qingbo He, JunWang, Yongbin Liu, et al. Multiscale noise tuning of stochastic resonance for enhanced fault diagnosis in rotating machines[J]. Mechanical Systems and Signal Processing,2012,28:443-457.
[99]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.
[100]Liu B.Selection of wavelet packet basis for rotating machinery fault diagnosis[J] Journal of Sound and Vibration,2005,(284):567-582.
[101]C.Smith, C.M. Akujuobi, P. Hamory. An approach to vibration analysis using wavelets in an application of aircraft health monitoring[J]. Mechanical Systems and Signal Processing,2007,21(3):1255-1272.
[102]A.K. Darpe.A novel way to detect transverse surface crack in a rotating shaft [J].Journal of Sound and Vibration,2007,305(1-2):151-171.
[103]何正嘉,訾艳阳,张西宁.现代信号处理及工程应用[M].西安：西安交通大学出版社,2007.
[104]W Sweldens.The lifting scheme:A construction of second generation wavelets[J]. SIAM J. Math.Aanal,1997,29(2):511-546.
[105]Sweldens W. The Construction and Application of Wavelets in Numerical analysis[D].Belgium:Katholieke Universiteit Leuven,1995.
[106]C.D. Duan, Z.J. He, H K. Jiang. A sliding window feature extraction method for rotating machinery based on the lifting scheme[J]. Journal of Sound and Vibration 2007,299(4-5): 774-785
[107]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[J].振动与冲击.2007,26(2)：1013,32.
[108]R. L. Claypoole.Adaptive wavelet transform via lifting [D].Deportment of electrical and computer engineering, Rice University, Houston, Texas, October 1999.
[109]Jawerth B, Sweldens W. An overview of wavelet based multiresulotion analysis.URL: http://cm.bell-labs.com/cm/ms/who/wim/.
[110]段晨东.基于第二代小波变换的故障诊断技术研究[D].西安：西安交通大学,2005.
[111]Y. Meyer. Wavelets:Algorithms and Applications[M]. SIAM.1993:24-368.
[112]X. Li, L. Qu, G. Wen, et al. Application of wavelet packet analysis for fault detection in electro-mechanical systems based on torsional vibration measurement[J]. Mechanical Systems and Signal Processing.2003,17(6):1219-1235.
[113]X.F. Fan, M.J. Zuo. Gearbox fault detection using Hilbert and wavelet packet transform[J]. Mechanical Systems and Signal Processing.2006,20(4):966-982.
[114]J. Rafiee, F. Arvani, A. Harifi, M.H. Sadeghi. Intelligent condition monitoring of a gearbox using artificial neural network[J]. Mechanical Systems and Signal Processing.2007,21(4):1746-1754.
[115]Q.S.Xu,Z.G.Lia. Recognition of wear mode using multi-variable synthesis approach based on wavelet packet and improved three-line method[J]. Mechanical Systems and Signal Processing.2007,21(8):3146-3166.
[116]段晨东.基于第二代小波变换的故障诊断技术研究[D].西安交通大学博士学位论文.2005：40-60.
[117]姜洪开,王仲生.第二代小波包构造及发动机微弱损伤识别[J].北京航空航天大学学报.2007,33(7)：777-780.
[118]傅勤毅,傅俭毅,王峰林.一种无频带错位的小波包算法.振动工程学报[J].1999,12(3)：423-428.
[119]丁康,陈建林,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.
[120]陈树越.余红英,刘广璞.BP网络算法及其在故障诊断中的应用述评[J].振动、测试与诊断,2001.
[121]颜延虎,钟秉林,黄仁.神经网络技术及其在旋转机械故障诊断中的应用[J].振动工程学报,1993.
[122]吴今培.智能故障诊断与神经网络[M].北京：科学出版社,1997.
[123]Yang B S, Han T, An J L. ART-KOHONEN neural network for fault diagnosis of rotating machinery [J].Mechanical Systems and Signal Processing,2004,18 (3):645-657.
[124]朱博,胡艳,赵永标.人工神经网络在故障诊断系统中的应用[J].舰船电子工程,2005,(1)：91-95.
[125]Rumelhart D. E., Meclelland J. L. Parallel Distributed Processing [M], MIT,1986.
[126]雷亚国,混合智能技术及其在故障诊断中的应用研究[D],西安交通大学博士学位论 文.2007
[127]Guo H,Jack L B,Nandi A K. Feature generation using genetic programming with application to fault classification[J].IEEE Transaction on System, Man, and Cybernetics,2005,35(1):89-99.
[128]Samanta B. Artificial neural networks and genetic algorithms for gear fault detection[J].Mechanical System and Signal Processing,2004,18(5):1273-1282.
[129]Jack L B,Nandi A K. Fault detection using support vector machines and artificial neural networks augmented by genetic algorithms[J].Mechanical System and Signal Processing,2002,16(3):373-390.
[130]Yang B S, Han T, Hwang W W. Fault diagnosis of rotating machinery based on multi-class support vector machines [J]. Journal of Mechanical Science and Technology,2005, 19(3):846-859.
[131]关惠玲,韩捷.设备故障诊断专家系统原理及实践[M].北京：机械工业出版社,2000.
[132]Vladimir Vapnik. The Nature of Statistical Learning Theory[M]. NY:Springer,1995.
[133]李应红,尉询楷,刘建勋.支持向量机的工程应用[M].北京：兵器工业出版社,2004
[134]祝海生.统计学习理论的工程应用[D].西安：西安交通大学,2004.
[135]张艳秋,王蔚.利用遗传算法优化的支持向量机垃圾邮件分类[J].计算机应用,2009,29(10)：2755-2757.
[136]李应红,尉询楷,刘建勋.支持向量机的工程应用[M].北京：兵器工业出版社,2004.
[137]李巍华,史铁林,杨叔子.基于非线性判别分析的故障分类方法研究[J].建筑热能通风空调,2005,18(2)：133-138.
[138]王学峰,王文峰,基于免疫网络算法的SVM参数选择[J],计算机应用与软件,2009,26(9)：266-268.
[139]胡金海,谢寿生,骆广琦等.基于核函数Fisher鉴别分析的特征提取方法[J].振动、测试与诊断,2008,28(4)：322-326.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |