基于盲分离的空调机组故障振声诊断研究
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摘要
空调机组通常多台安装在同一空间内,各类信号之间互相干扰很难提取准确真实的信息,采用盲信号分离技术可以从纷繁的数据中提取有用信息,其优势在于无需掌握信号产生和传播的先验知识。本文在故障源数量未知和源信号未知等条件下,着重探讨了适于空调机组故障特征提取的盲分离算法和模型,以制冷机组和冷却塔为研究对象,通过分离故障振动源和噪声源两个途径提供多元化的诊断参数和更丰富的故障信息,能够解决实际运行的空调机组难以提取信号特征的难题,既提高诊断的效率和准确度,又为设备群故障诊断提供解决途径。
本文比较了改进的二阶统计量方法和传统的JADE算法,具有时序结构的源信号因其有不同的自相关函数或有非零时序相关数降低了对统计独立性的限制条件,能够较快地实现收敛,同时也能在噪声能量较大的情况下实现分离。在信噪比低于20dB的情况下改进算法性能指标小得多,而在信噪比高于20dB以后,两种算法趋于一致。改进了盲解卷算法以消除噪声影响,无监督的盲分离处理过程和有监督的噪声消除同时进行,通过相似系数来选取适合含噪信号盲解卷的非线性函数来最大化输出信号的广义能量。选择延迟系数综合考虑分离时间和收敛性能,随着延迟系数的增大,收敛误差函数值减小,收敛性能增强,当延迟系数增大到一定程度后,收敛性能的提高就不再显著,同时分离时间会相应的增长。
通过实验模拟了两个故障源和多故障源振动信号混合的情况,分别应用ICA算法、Bussgang算法和改进盲解卷算法提取出典型故障信号特征,验证了不同混合模型和迭代算法对振动信号分离结果会产生很大影响。比较后发现改进盲解卷算法的分离精度最高,说明卷积混合模型适合大型空调机组的振动诊断,这是由于大型空调机组的信号传播路径的不同,因而导致同一时刻的观测信号是源信号在不同时刻数据的叠加。最后,应用改进盲解卷算法提取了JZKA31.5型螺杆机组的磨损、气流撞击和啮合不良的振动故障特征,盲提取的白化预处理和对角化造成振动信号的幅值有所变化,但如果用信号波形表征故障特征,可不考虑幅值比例对诊断结果的影响。
在分析振动特性、频率特性和声辐射特性三者联系的基础上进行风机声学诊断实验,建立基于非线性混合的声学诊断模型。实验发现,风机转速越大,负载越大,低频部分的幅值差异就越明显。本文以冷却塔声学诊断为实例,初步测试噪声特性作为先验知识,将干扰噪声本身作为一个声源,基于非线性RBFN分离网络从观测信号中提取独立的声源信号特征来识别冷却塔的主要故障类型,并与基于卷积混合和BP模型的分离结果比较。仿真实验中发现采用四阶累积量估计方法比二阶累积量估计误差大,随着信号源个数增加,需要估计四阶以上的高阶累积量,导致算法性能变差,计算量也大幅度增加。因此基于二阶统计量的改进算法适用于声信号的非线性盲分离。
研究复杂的大空间背景环境中设备群振动噪声信号混合交叉的盲分离,从迹的概念出发验证算法的稳定性,将无用信号认为是干扰噪声分离出来,只提取期望的随机信号,再根据独立性测度关系依次提取最显著的故障特征,大大简化了计算过程。经过改进后的自然梯度算法仍满足正交约束,而且不依赖学习速率。本文对某会所地下空调机房实施振声诊断,在多台热泵机组和水泵设备集合的情况下通过空调机房噪声频谱的非线性盲分离确定了主要的故障源为螺杆压缩机,应用盲解卷算法提取不对中和碰磨故障振动信号特征,实现了大空间设备群的振声诊断。
It is very difficult to extract accurate signals when the air conditioning units are usually installed together because all kinds of complicate signals disturb mutually. Through the blind signal separation technology the useful information can be acquired from the complex data because it does not need the massive samples and priori knowledge of producing and dissemination of signals. In this paper, the mixing models and algorithm are emphatically discussed which are suitable for the fault feature extraction of air-conditioning unit on the condition that the ambient noise, the fault source and the prior knowledge are unknown. The multiplex diagnosis parameters and the richer fault information are supplied through the vibration source and the noise source for the study on the refrigeration unit and the cooling tower. We can solve the he difficult problem of the signal characteristics extraction of the air conditioning units using the method which can both enhance the diagnosis accuracy and provide the solution of failure diagnosis for all kinds of equipment group in spacious situation.
In this paper, the improved second-order statistics algorithm is compared by the traditional JADE algorithm because the different autocorrelation function or the non-zero time sequence correlation reduces the limiting condition of statistical independence which can realize quick convergence and also the separation in the noise energy big situation. When the signal noise ratio is lower than 20dB, the improved second-order statistics algorithm is better. But after the signal-to-noise ratio is higher than 20dB, two algorithms tended to be consistent. Blind deconvolution algorithm is improved to eliminate noise effect, and the non-surveillance's blind separation process and surveillance noise elimination are carried on simultaneously. The nonlinear function suitable to blind deconvolution is selected by the similarity factor to maximize the output signal generalized energy. The delay factor should be chosen according to the separating time and the convergence performance. When the delay factor increases, the value of convergence error function reduces and the convergence performance would be strengthened. When the delay factor increases to the certain extent, the convergence performance is no longer enhanced remarkably and the separating time extends.
The mixing situation of two breakdown sources and the multi-breakdown source are simulated through the test separately using the ICA algorithm, the Bussgang algorithm and improved blind deconvolution algorithm to extract the typical fault signal characteristics. It is indicated that the mixing model and the iterative algorithm will influence the separating results of vibration signals. After the comparison, it is discovered that the improved blind separating algorithm enhances the separation precision to be highest, and the convolution mixing model is suitable to the vibration diagnosis of the large-scale air conditioning units. This is because the different disseminating ways leads to the result that the observation signals on the identical time become the superimposition of the source signals in the different time. Finally, the vibration signals of the JZKA31.5 screw unit are collected and the breakdown characteristics of attrition, air current hit and meshing are extracted. The blind extraction processing including whitening pretreatment and diagonalization will lead to some change of the amplitude value of the vibration signal. But if the waveform is available to express the fault feature, the amplitude value proportion does not affect the diagnosis result.
The acoustic diagnosis experiments of the blower are designed on the analysis vibration characteristic, the frequency characteristic and the acoustic radiation characteristic, and the acoustics diagnostic model is established based on the nonlinear mixing model. It is discovered in the tests that the rotational speed is bigger, more obvious difference of amplitude value in the low frequency part is. This article takes the cooling tower acoustics diagnosis as an example. Through testing the noise characteristic the priori knowledge is achieved initially. The independent acoustic source signals are extracted from the observation signals based on the nonlinear RBFN separation network to distinguish the major failure types from the cooling tower when the interference noise is taken an acoustic source, and the test results are compared with the separation results based on the blind deconvolution and BP model. In the simulation experiment, it is discovered that the error of estimation using the fourth-order cumulant method is bigger than the second-order cumulant method. When the number of sources increases, higher order cumulant will depress the algorithm performance and improve the computation load. Therefore, the algorithm based on the second-order statistics is better for the nonlinear separation effect of acoustic signals.
The blind source separation is studied in the complex big space background environment when the signals of the equipment group are mixed and disturbed mutually. Firstly, the algorithm stability is tested from the trace concept. And then the expected signals are attained by eliminating the unwanted random signals from the interference noise. Finally the most remarkable breakdown characteristics are extracted in turn according to the independent measure relations. The computational process will be simplified greatly by this method. The improved natural gradient algorithm still is satisfied with the orthogonal restraint and did not rely on the studying rate. In this paper, vibration and acoustics diagnosis has been completed in an air conditioning room. It is determined that main fault source was from the compressor through separating the noise spectrum in the air conditioning room based on nonlinear blind separating model when several heating pumps and water pumps are installed intently. The misalignment and rubbing faults has been diagnosed by vibration signal characteristic based on the blind deconvolution algorithm, which realized the vibration and sound diagnosis in the big space for the machine group.
本文比较了改进的二阶统计量方法和传统的JADE算法,具有时序结构的源信号因其有不同的自相关函数或有非零时序相关数降低了对统计独立性的限制条件,能够较快地实现收敛,同时也能在噪声能量较大的情况下实现分离。在信噪比低于20dB的情况下改进算法性能指标小得多,而在信噪比高于20dB以后,两种算法趋于一致。改进了盲解卷算法以消除噪声影响,无监督的盲分离处理过程和有监督的噪声消除同时进行,通过相似系数来选取适合含噪信号盲解卷的非线性函数来最大化输出信号的广义能量。选择延迟系数综合考虑分离时间和收敛性能,随着延迟系数的增大,收敛误差函数值减小,收敛性能增强,当延迟系数增大到一定程度后,收敛性能的提高就不再显著,同时分离时间会相应的增长。
通过实验模拟了两个故障源和多故障源振动信号混合的情况,分别应用ICA算法、Bussgang算法和改进盲解卷算法提取出典型故障信号特征,验证了不同混合模型和迭代算法对振动信号分离结果会产生很大影响。比较后发现改进盲解卷算法的分离精度最高,说明卷积混合模型适合大型空调机组的振动诊断,这是由于大型空调机组的信号传播路径的不同,因而导致同一时刻的观测信号是源信号在不同时刻数据的叠加。最后,应用改进盲解卷算法提取了JZKA31.5型螺杆机组的磨损、气流撞击和啮合不良的振动故障特征,盲提取的白化预处理和对角化造成振动信号的幅值有所变化,但如果用信号波形表征故障特征,可不考虑幅值比例对诊断结果的影响。
在分析振动特性、频率特性和声辐射特性三者联系的基础上进行风机声学诊断实验,建立基于非线性混合的声学诊断模型。实验发现,风机转速越大,负载越大,低频部分的幅值差异就越明显。本文以冷却塔声学诊断为实例,初步测试噪声特性作为先验知识,将干扰噪声本身作为一个声源,基于非线性RBFN分离网络从观测信号中提取独立的声源信号特征来识别冷却塔的主要故障类型,并与基于卷积混合和BP模型的分离结果比较。仿真实验中发现采用四阶累积量估计方法比二阶累积量估计误差大,随着信号源个数增加,需要估计四阶以上的高阶累积量,导致算法性能变差,计算量也大幅度增加。因此基于二阶统计量的改进算法适用于声信号的非线性盲分离。
研究复杂的大空间背景环境中设备群振动噪声信号混合交叉的盲分离,从迹的概念出发验证算法的稳定性,将无用信号认为是干扰噪声分离出来,只提取期望的随机信号,再根据独立性测度关系依次提取最显著的故障特征,大大简化了计算过程。经过改进后的自然梯度算法仍满足正交约束,而且不依赖学习速率。本文对某会所地下空调机房实施振声诊断,在多台热泵机组和水泵设备集合的情况下通过空调机房噪声频谱的非线性盲分离确定了主要的故障源为螺杆压缩机,应用盲解卷算法提取不对中和碰磨故障振动信号特征,实现了大空间设备群的振声诊断。
It is very difficult to extract accurate signals when the air conditioning units are usually installed together because all kinds of complicate signals disturb mutually. Through the blind signal separation technology the useful information can be acquired from the complex data because it does not need the massive samples and priori knowledge of producing and dissemination of signals. In this paper, the mixing models and algorithm are emphatically discussed which are suitable for the fault feature extraction of air-conditioning unit on the condition that the ambient noise, the fault source and the prior knowledge are unknown. The multiplex diagnosis parameters and the richer fault information are supplied through the vibration source and the noise source for the study on the refrigeration unit and the cooling tower. We can solve the he difficult problem of the signal characteristics extraction of the air conditioning units using the method which can both enhance the diagnosis accuracy and provide the solution of failure diagnosis for all kinds of equipment group in spacious situation.
In this paper, the improved second-order statistics algorithm is compared by the traditional JADE algorithm because the different autocorrelation function or the non-zero time sequence correlation reduces the limiting condition of statistical independence which can realize quick convergence and also the separation in the noise energy big situation. When the signal noise ratio is lower than 20dB, the improved second-order statistics algorithm is better. But after the signal-to-noise ratio is higher than 20dB, two algorithms tended to be consistent. Blind deconvolution algorithm is improved to eliminate noise effect, and the non-surveillance's blind separation process and surveillance noise elimination are carried on simultaneously. The nonlinear function suitable to blind deconvolution is selected by the similarity factor to maximize the output signal generalized energy. The delay factor should be chosen according to the separating time and the convergence performance. When the delay factor increases, the value of convergence error function reduces and the convergence performance would be strengthened. When the delay factor increases to the certain extent, the convergence performance is no longer enhanced remarkably and the separating time extends.
The mixing situation of two breakdown sources and the multi-breakdown source are simulated through the test separately using the ICA algorithm, the Bussgang algorithm and improved blind deconvolution algorithm to extract the typical fault signal characteristics. It is indicated that the mixing model and the iterative algorithm will influence the separating results of vibration signals. After the comparison, it is discovered that the improved blind separating algorithm enhances the separation precision to be highest, and the convolution mixing model is suitable to the vibration diagnosis of the large-scale air conditioning units. This is because the different disseminating ways leads to the result that the observation signals on the identical time become the superimposition of the source signals in the different time. Finally, the vibration signals of the JZKA31.5 screw unit are collected and the breakdown characteristics of attrition, air current hit and meshing are extracted. The blind extraction processing including whitening pretreatment and diagonalization will lead to some change of the amplitude value of the vibration signal. But if the waveform is available to express the fault feature, the amplitude value proportion does not affect the diagnosis result.
The acoustic diagnosis experiments of the blower are designed on the analysis vibration characteristic, the frequency characteristic and the acoustic radiation characteristic, and the acoustics diagnostic model is established based on the nonlinear mixing model. It is discovered in the tests that the rotational speed is bigger, more obvious difference of amplitude value in the low frequency part is. This article takes the cooling tower acoustics diagnosis as an example. Through testing the noise characteristic the priori knowledge is achieved initially. The independent acoustic source signals are extracted from the observation signals based on the nonlinear RBFN separation network to distinguish the major failure types from the cooling tower when the interference noise is taken an acoustic source, and the test results are compared with the separation results based on the blind deconvolution and BP model. In the simulation experiment, it is discovered that the error of estimation using the fourth-order cumulant method is bigger than the second-order cumulant method. When the number of sources increases, higher order cumulant will depress the algorithm performance and improve the computation load. Therefore, the algorithm based on the second-order statistics is better for the nonlinear separation effect of acoustic signals.
The blind source separation is studied in the complex big space background environment when the signals of the equipment group are mixed and disturbed mutually. Firstly, the algorithm stability is tested from the trace concept. And then the expected signals are attained by eliminating the unwanted random signals from the interference noise. Finally the most remarkable breakdown characteristics are extracted in turn according to the independent measure relations. The computational process will be simplified greatly by this method. The improved natural gradient algorithm still is satisfied with the orthogonal restraint and did not rely on the studying rate. In this paper, vibration and acoustics diagnosis has been completed in an air conditioning room. It is determined that main fault source was from the compressor through separating the noise spectrum in the air conditioning room based on nonlinear blind separating model when several heating pumps and water pumps are installed intently. The misalignment and rubbing faults has been diagnosed by vibration signal characteristic based on the blind deconvolution algorithm, which realized the vibration and sound diagnosis in the big space for the machine group.
引文
[1]刘泽华.空调冷热源工程.北京:机械工业出版社,2005.
[2]项端祈.空调系统消声与隔振设计.北京:机械工业出版社,2005.
[3]Y.H.Song,Y.Akashi,K.Kuniyoshi.Study on fault detection and diagnosis of building air-conditioning system.Investigation of faulty status using calculation,Summaries of Technical Papers of Annual Meeting of Architectural Institute of Japan,2005:1431-1432.
[4]Y.H.Song,Y.Akashi,Jurng-Jae Yee.A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems.Energy and Buildings,2007:1-12(In press).
[5]马大猷.噪声与振动控制工程手册.北京:机械工业出版社,2002.
[6]赵玫,周海亭,陈光冶等.机械振动与噪声学.北京:科学出版社,2004.
[7]L.N.Grace,D.Datta,S.A.Tassou.Sensitivity of refrigeration system performance to charge levels and parameters for on-line leak detection.Applied Thermal Engineering,2005,25(4):557-566.
[8]J.Cui,S.W.Wang.A model-based online fault detection and diagnosis strategy for centrifugal chiller systems.International Journal of Therrnal Science,2005,44(10):986-999.
[9]陈长征,胡立新,周勃等.设备振动分析与故障诊断技术.北京:科学出版社,2006.
[10]应怀樵.现代振动与噪声技术.北京:航空工业出版社,2002.
[11]K.Kuniyoshi,T.Hayashi,D.Sumiyoshi.A study on energy-saving diagnosis of the air conditioning system in the building.Outline of the HVAC&R experimental analysis system,Kyushu Chapter Architectural Research Meeting(Environment) of Architectural Institute of Japan,2005:437-440.
[12]T.I.Salsbury,R.C.Diamond.Fault detection in HVAC systems using model-based feedforward control.Energy and Buildings,2001(33):403-415.
[13]王志毅,谷波,江国和.二级模糊综合评判方法在制冷机故障诊断中的应用.暖通空调,2004,34(1):85-88.
[14]陈友明.自动故障检测与诊断在暖通空调中的研究与应用.暖通空调,2004,34(3):29-33.
[15]Dexter A.L,Ngo D.Fault diagnosis in air-conditionin system:a multi-step fuzzy model-based approach.HVAC&RResearch,2001,7(1):83-102.
[16]Stylianou Meli,Nikanpour Darius.Performan monitoring,faultdetection,and diagnosis of reciprocation chillers.ASHRAETrans.,1996,102(1):615-627.
[17]HouseJohn M,Lee WonYong.Classification techniques for fault detection and diagnosis of an air-handling unit.ASHRAETrans.,1999,105(1):1085-1097.
[18]Hossein.A neural network prototype for fault detection and diagnosis of heating systems.ASHRAE Trans,2007,113(1):634-644.
[19]Mcintosh B.D,Ahn Byung,Mitchell John W.Model-based fault detection and diagnosis for cooling towers.ASHRAETrans.,2001,107(1):839-846.
[20]M.Roger,S.Moreau.Broadband self-noise from loaded fan blades.AIAA Journal,2004,42(3):536-544.
[21]H.Sun,S.Lee.Numerical prediction of centrifugal compressor noise.Journal of Sound and Vibration,2004(269):421-430.
[22]金琰,袁新.应用流固耦合数值方法研究机翼的射流减振技术.空气动力学报,2002,(3):267-273.
[23]R.Ganguli.Optimum design of a helicopter rotor for low vibration using aero elastic analysis and response surface methods.Journal of Sound and Vibration,2002,258(2):327-344.
[24]屈梁生.机械故障诊断学.上海:科技出版社,1986.
[25]盛兆顺,尹畸岭.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.
[26]关惠玲,韩捷.设备故障诊断专家系统原理及实践.北京:机械工业出版社,2000.
[27]House JohnM,Vaezi Nejad,J Michael.An expert rule set for fault detection in air handling units.ASHRAETrans,2001,107(1):858-871.
[28]S.W.Wang,J.B.Wang.Law-based sensor fault diagnosis and validation for building air-conditioning systems.HVAC&R Research,1999,5(4):353-380.
[29]陈长征,周永.小波神经网络法在柴油机故障诊断中的应用.内燃机学报,2002,20(1):89-91.
[30]谷荻隆嗣,荻原将文著,马炫泽.人工神经网络和模糊信号处理.北京:科学出版社,2003.
[3l]G.Betta,C.Liguori,and A.Pietrosanto.A multi-application FFT-analyzer based on a DSP architecture.IEEE Trans.Instrum.Meas.,2001:825-832.
[32]Giovanni Betta,Consolatina Liguori,Alfredo Paolillo.A DSP-based FFT-Analyzer for the fault diagnosis of rotating machine based on vibration analysis.IEEE Transactions on Instrumentation and Measurement,2002,51(6):316-1322.
[33] H. Yoshida, T. Ivami, H. Yuzawa, et al.. Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter. ASHRAE Transactions, 1996, 102 (1):557-564.
[34] Conde, M. R... Estimation of thermophysical properties of lubricating oils and their solutions with refrigerants: An appraisal of existing methods. Appl. Therm. Eng. 1996(16): 51—61.
[35] D. Ngo. Fuzzy indentication approach in air-conditioning system. International Journal of HVAC and R Research ,2006,7 (1): 183—192.
[36] Wilson Q. Wanga, M. Farid Golnaraghi, Fathy Ismail. Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing,2004(18): 813—831.
[37] Jesus Manuel, Fernandez Salido, Shuta Murakami. A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery. Applied Soft computing ,2004(4): 413—422.
[38] 虞和济,陈长征,张省等．基于神经网络的智能诊断．北京：冶金工业出版社,2000．
[39] W.Y. Lee, J.M. House, N.H. Kyong. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy, 2004, 77 (2): 153—170.
[40] S.W. Wang, Y.M. Chen. Fault-tolerant control for outdoor ventilation air flow rate in building based on neural network. Building and Environment, 2002,37 (7): 691 —704.
[41] D.J. Swider, M.W. Browne, P.K. Bansal, et al.. Modelling of vapour compression liquid chillers with neural networks. Applied Thermal Engineering , 2001, 21 (3): 311—329.
[42] Lee WY, House JM, Park C. Kelly JE. Fault diagnosis of an air-handling unit using artificial neural network.ASHRAE Trans, 1996(102): 540—549.
[43] B. Samanta, K. R. Balushi. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mechanical System and Signal Processing, 2005, 19 (2):371—390.
[44] A.K.Nandi. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron, 1997,47:1060—1069.
[45] Y.C. Chang, F.A. Lin, C.H. Lin. Optimal chiller sequencing by branch and bound method for saving energy. Energy Conversion and Management, 2005,46 (12—14):2158—2172.
[46] J. Burnett. Online adaptive control for optimizing variable-speed pumps of indirect water-cooled chilling systems. Applied Thermal Engineering, 2001, 21(11): 1083— 1103.
[47] Kyusung Kim, Alexander G. Parlos. Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Trans on Mechatronics, 2002, 7(2): 201—219.
[48] Meyer Y. Wavelets algorithms and applications. J. SIAM, 1993.
[49]B.A Paya,I.Esat.Artificial neural network based fault diagnostics of rotating machinery using wavelet transform as a preprocessor.Mechanical Systems and Signal Processing,2004:640-654.
[50]T.Matsuura.An application of neural network for selecting feature parameters in machinery diagnosis.Journal of Materials Processing Technology,2004:1-5.
[51]T.Kaewkongka,Y.H.Joe Au,R.Rakowski,B.E.Jones.Continuous wavelet transform and neural network for condition monitoring of dynamic machinery.IEEE Instrumentation and Measurement Technology Conference Budapest,2001:21-23.
[52]王志毅,谷波,黎远光.小波变换应用于空调制冷机组故障先兆预测.暖通空调,2004,34(10):117-119.
[53]Zhimin Du,Xinqiao Jin.Detection and diagnosis for multiple faults in VAV systems.Energy and Buildings,2007(39):923-934.
[54]Achmad Widodo,Bo-Suk Yang.Support vector machine in machine condition monitoring and fault diagnosis.Mechanical Systems and Signal Processing,2007(21):2560-2574.
[55]R.Fang.Induction machine rotor diagnosis using support vector machines and rough set.Lecture Notes in Artificial Intelligence,2006(4114):631-636.
[56]A.Widodo,B.S.Yang,T.Han.Combination of independent component analysis and support vector machine for intelligent faults diagnosis of induction motors.Expert System with Application,2007(32):299-312.
[57]S.F.Yuan,F.L.Chu.Support vector machines-based fault diagnosis for turbo-pump rotor.Mechanical System and Signal Processing,2006,20(4):939-952.
[58]S.F.Yuan,F.L.Chu.Fault diagnosis based on support vector machine with parameter optimization by artificial immunization algorithm.Mechanical System and Signal Processing,2007,21(3):1318-1330.
[59]B.Samanta.Gear fault detection using artificial neural networks and support vector machines with genetic algorithms.Mechanical Systems and Signal Processing,2004,18(3):625-644.
[60]C.W.Hsu,C.J.Lin.A comparison of methods for multi-class support vector machines.IEEE Transactions on Neural Networks,2002(13):415-425.
[61]J.Liang,R.Du.Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method.Mechanical Systems and Signal Processing,2007(21):2560-2574.
[62]陈长征,王楠,刘强.遗传算法中交叉和变异概率选择的自适应方法及作用机理.控制理论与应用,2002,19(1):41-43.
[63]B.S.Yang,T.Han,W.W.Hwang.Fault diagnosis of rotating machinery based on multi-class support vector machines.Journal of Mechanical Science and Technology,2005,19(3):845-858.
[64]晋欣桥,杜志敏,孙勇.基于主成分分析法的变风量空调系统传感器故障诊断.上海交通大学学报,2005,39(8):1222-1225.
[65]D.Wang,J.A.Romagnoli.Robust multi-scale principal components analysis with applications to process monitoring.Journal of Process Control,2005,15(8):869-882.
[66]House J.M.,Lee W.Y.,Shin D.R..Classification techniques for fault detection and diagnosis of an air-handling unit.ASHRAE Trans,1999,105:1087-1097.
[67]Katipamula S,Pratt RG,Shassin DP,et.al.Automated fault detection and diagnosis for outdoor-air ventilation systems and economizer:Testing,methodology and results from field.ASHRAE Trans,1999,105:555-567.
[68]M.Stylianou,Application of classification functions to chiller fault detection and diagnosis.ASHRAE Transactions 1997,103(1):640-656.
[69]K.Kim,A.G.Parlos.Model-based fault diagnosis of induction motors using non-stationary signal segmentation.Mechanical Systems and Signal processing,2002,16(2):223-253.
[70]I.Daubechies.The wavelet transform,time-frequency localization and signal analysis.IEEE Trans.Information Theory,1999,36(5):961-1005.
[71]Wan Lianghon.,Liu Yibing.,Feng Dongliang.Application of wavelet packets analysis to fault diagnose of rolling bearings.Modern Electric Power,2004,21(1):24-26.
[72]Isermann R.Supervision,fault detection and fault-diagnosis methods.Control Eng Practice,1997,5(5):639-652.
[73]R.N.Mahanty,P.B.Dutta Gupta.Application of RBF neural network to fault classification and location in transmission lines.IEE Proc-Gener.Transm.Distrib.,2004,151:201-212.
[74]M.Zacksenhouse,S.Braun,M.Feldman.Toward helicopter gearbox diagnostics from a small examples.Mechanical Systems and Signal Processing,2000,14(4):523-543.
[75]L.B.Jack,A.K.Nandi.Fault detection using support vector machines and artificial neural network,augmented by genetic algorithms.Mechanical System and Signal Processing,2002,16:373-390.
[76]B.Samanta,K.R.Al-Balushi,S.A.Al-Araimi.Artificial neural network and support vector machine with genetic algorithm for bearing fault detection.Engineering Application of Artificial Intelligence,2003,16:657-665.
[77]Wang,S.and Cui,J.Sensor-fault detection,diagnosis and estimation for centrifugal chiller systems using principal-component analysis method.Applied Energy,2005,8(23):197-213.
[78]S.Yoon,J.F.MacGregor.Fault diagnosis with multivariate statistic models.Journal of Process Control,2001,11(1):387-400.
[79] Sung-Hwan Cho, Hoon-Cheol Yang, M. Zaheer-uddin. Transient pattern analysis for fault detection and diagnosis of HVAC systems. Energy Conversion and Management, 2005( 46): 3103-3116.
[80] 黄其柏．工程噪声控制学．武汉:华中理工大学出版社,1999．
[81] U. Ayr,E.Cirillo, F. Martellotta. An experimental study on noise indices in air-conditioned offices. Applied Acoustics, 2001, 62:633—643.
[82] Tim Toutountzakis, David Mba. Observations of acoustic emission activity during gear defect diagnosis. NDT&E International, 2003(36): 471—477.
[83] F.Pellicano, F.Vestroni. Complex dynamics of high-speed axially moving systems. Journal of Sound and Vibration, 2002, 258(1): 31—44.
[84] A. J. Bell, T. J. Seinowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 1995 (7): 1129—1159.
[85] 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, 2007, 16:719—740.
[86] Guillaume Gelle, Maxime Cloas, Christine Serviere. Blind source separation: a new pre-processing tool for rotating machines monitoring. IEEE. Transactions on Instrumentation and Measurement,2003,52:790—795.
[87] C. Serviere., P. Fabry. Blind source separation of noisy harmonic signals for rotating machine diagnosis. Journal of Sound and Vibration, 2004, 272:317—339.
[88] Gwo-Ching Liao, Ta-Peng Tsao: Application of fuzzy neural networks and artificial intelligence for load forecasting. Electric Power Systems Research, 2004, 70:237—244.
[89] Xinhua Xu, Fu Xiao, Shengwei Wang. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods. Applied Thermal Engineering, 2007:1 — 12.
[90] P. Haves, T.I. Salsbury, J.A. Wright. Condition monitoring in HVAC subsystem using first principles models. ASHRAE Transactions, 1996, 102 (1):519—527.
[91] J.E. Jackson. A User's Guide to Principal Components. John Wiley &Sons, 1991.
[92] X.Q. Jin, Z.M. Du. Fault tolerant control of outdoor air and AHU supply air temperature in VAV air conditioning systems using PCA method. Applied Thermal Engineering, 2006,26 (11-12): 1226—1237.
[93] R. Peled, S. Braun, M. Zacksenhouse. A blind deconvolution separation of multiple sources, with application to bearing diagnostics. Mechanical Systems and Signal Processing, 2005, 19:1181 — 1195.
[94]G.Gelle,M.Colas,G.Delaunay,Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis.Mechanical Systems and Signal Processing,2000,14(3):427-442.
[95]T.J.Sejnowski.A non-linear information maximization approach that performs blind deconvolution approach.Neural Computation,1998(12):571-583.
[96]J.K.Tugnait.Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria.IEEE Transactions on Signal Processing,1997,45(3):658-672.
[97]Massoud Babaie-Zadeha,Christian Jutten A general approach for mutual information minimization and its application to blind source separation.Signal Processing,2005,85:975-995.
[98]宋友,重堪,李其汉.基于三阶累积量的转子振动信号降噪方法研究.航空动力学报,2002,17(3):363-366.
[99]李舜酩.转子振动故障信号的盲分离.航空动力学报,2005,20(5):751-756.
[100]钟振茂,陈进,钟平.盲源分离技术用于机械故障诊断的研究初探.机械科学与技术,2002,21(2):282-284.
[101]Roal M.J.,Erling J.G.A new non-linear adaptive blind separation approach to gear tooth failure detection and analysis.Mechanical Systems and Signal Processing,2002,16:719-740.
[102]Roger Boustany.A subspace method for the blind extraction of a cyclostationary source:Application to rolling element bearing diagnostics.Mechanical Systems and Signal Processing,2005,19:1245-1259.
[103]J.Antoni,F.Bonnardot,A.Raad,M.El Badaoui[J].Cyclostationary modelling of rotating machine vibration signals.Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[104]J.Antoni,F.Guillet,M.El Badaoui.Blind separation of convolved cyclostationary processes[J].Signal Processing,2005,85:1-66.
[105]A.Ferre' ol,P.Chevalier[J].On the behavior of current second and higher order blind source separation methods for cyclostationary sources.IEEE Transactions on Signal Processing,2000,48(6):1712-1725.
[106]卢文祥,杜润生.工程测试与信息处理.华中科技大学出版社,2004.
[107]U.M.Bae,T.W.Lee,S.Y.Lee.Blind signal separation in teleconferencing using the ICA mixture model.Electronic Letters,2000,37(7):680-692.
[108]A.Belouchrani,A.Cichocki.Robust whitening procedure in blind source separation context.Electronic Letters,2000,36(24):2050-2053.
[109]X.R.Cao,R.W.Liu.General approach to blind source separation.IEEE Trans.Signal Processing,1996,44(3):562-571.
[110]张云涛,龚玲.数据挖掘原理与技术.北京:电子工业出版社,2004.
[111]罗抟翼,程桂芬.控制工程与信号处理.北京:化学工业出版社,2004.
[112]夏天昌.系统辨识.北京:国防工业出版社,1984.
[113]黄长艺,卢文祥,熊诗波.机械工程测量与试验技术.北京:机械工业出版社,2003.
[114]L.C.Ludeman著,邱天爽等译.随机过程—滤波、估计与检测.北京:电子工业出版社,2005.
[115]邓自立.最优估计理论及其应用.哈尔滨工业大学出版社,2005.
[116]A.Cichocki,W.Kasprzak.Nonlinear learning algorithms for blind separation of natural images.Neural Network World,1996,6(4):515-523.
[117]J.Antoni.Blind separation of vibration components:Principles and demonstrations.Mechanical Systems and Signal Processing,2005(19):1166-1180.
[118]J.Antoni,L.Garibaldi,S.Marchesiello.New separation techniques for output-only modal analysis.Shock and Vibration,2004,11(3-4):227-242.
[119]Christian Jutten,Jeanny Heraul.Blind separation of sources,Part Ⅰ:An adaptive algorithm based on neuromimetic architecture.Signal Process,1991,24(1):1-10.
[120]S.Amari,A.Cichocki.Adaptive blind signal processing neural network approaches[J].Proceedings IEEE,1998,86:1186-1187.
[121]S.Matsuoka.Blind separation of nonstationary sources in noisy mixtures.Electronic Letters,2000,36(4):848-859.
[122]A.Belouchrani,K.Abed Meraim,J.F.Cardoso.A blind source separation techhique using second-order statistics.IEEE Trans.Processing,1997,45(2):434-444.
[123]N.Bouguerrioul,M.Haritopoulos,C.Capdessus.Novel cyclostationarity-based blind source separation algorithm using second-order statistical properties:Theory and application to the bearing defect diagnosis.Mechanical Systems and Signal Processing,2005,19:1260-1281.
[124]Hoang-Lan Nguyen Thi,C.Jutten.Blind source separation for convolutive mixtures.Signal Processing,1995,45(2):209-229.
[125]M.Zacksenhouse,S.Braun.A application to bearing diagnostics based on time-delayed second order correlations.Mechanical Systems and Signal Processing,2002(9):925-934.
[126]S.Amari,S.C.Douglas.Novel on-line adaptive learning algorithms for blind deconvolution using natural gradient approach.Proceeding 11~(th) IFAC Symposium on System Identification,1997(3):1057-1062.
[127]K.Tugnait.Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria.IEEE Transactions on Signal Processing,1997,45(3):658-672.
[128]D.Erdogmus,et al.Blind source separation using Renyis alpha-marginal entropies.Neurocomputing,2002,49(1):25-38.
[129]S.Amari,S.C.Douglas.Multichannel blind deconvolution and source separation using the natural gradient.Proceedings IEEE,1997,85:873-879.
[130]D.Erdogmus,J.C.Principe.Generalized information potential criterion for adaptive system training.IEEE Trans.Neural Networks,2002,13(5):1035-1044.
[131]A.J.Bell,T.J.Sejnowski.A non-linear information maximization approach that performs blind separation.Advances in Neural Information Processing Systems,1995,7:467-474.
[132]K.Hild II,et al.Blind source separation of time-varying,instantaneous mixtures using an on-line algorithm.International Conference on Acoustics,Speech,and Signal Processing,Orlando,2002(13-17):993-996.
[133]Bin-Chul Ihm,Dong-Jo Park.Blind separation of sources using higher-order cumulants Signal Processing,1999,73:267-276.
[134]P.Comon.Higher-order separation,application to detection and localization.Signal Processing,1999,71:277-280.
[135]D.Pham.Fast algorithm for estimating mutual information,entropies,and score functions.IEEE Signal Processing Letters,2003(1-4):17-22.
[136]J.F.Cardoso.Infomax and maximum likelihood for blind source separation.IEEE Signal Processing Letters,1997,4(4):112-124.
[137]D.Erdogmus,J.C.Principe.Generalized information potential criterion for adaptive system training.IEEE Trans.Neural Networks,2002,13(5):1035-1044.
[138]D.Pham.Contrast functions for blind separation and deconvolution of sources.International Workshop on Independent Component Analysis and Signal Separation,San Diego,CA,2001(9-12):37-52.
[139]J.F.Cardoso,B.H.Laheld.Equivariant adaptive source separation.IEEE Trans.Signal Processing,1996,44(12):3017-3030.
[140]赵荣义,范存养,薛殿华.空气调节(第三版).北京:中国建筑工业出版社,1994.
[141]虞和济.设备故障诊断工程.北京:冶金工业出版社,2001.
[142]豐天利夫.设备现场诊断的开展方法.北京:机械工业出版社,1985.
[143]徐敏.设备故障诊断手册.西安:西安交通大学出版社,1998.
[144]周勃,费朝阳,张宇等.基于振声分析的氨制冷螺杆压缩机组的故障诊断与噪声治理研究.环境工程,2007,25(1):49-51.
[145]柯拉科特.机械故障的诊断与情况监测.北京:机械工业出版社,1984.
[146]M.E.Badaoui,J.Daniere,F.Guillet.Separation of combustion noise and piston-slap in diesel engine.Mechanical Systems and Signal Processing,2005,19:1209-1217.
[147]S.N.Y.Gerges,J.C.Luca,N.Labor.A literature review of diesel engine noise with emphasis on piston slap.International Journal of Acoustic and Vibration,2000,(5):37-45.
[148]Margaret B.Bailey,Jan F.Kreiderb.Creating an automated chiller fault detection and diagnostics tool using a data fault library.ISA Transactions,2003,42:485-495.
[149]孙晓峰,周盛.气动声学.北京:国防工业出版社,1994.
[150]莫尔斯,英格特.理论声学.北京:科学出版社,1984.
[151]M.J.Fisher,R.H.Self.Aeroacoustics Research in Europe:the Ceas-asc Report on 2001 Highlights.Journal of Sound and Vibration,2002,258(1):1-30.
[152]黎胜,赵德有用边界元计算机构振动辐射声场.大连理工大学学报,2000,40(4):391-394.
[153]李林凌,黄其柏.风机叶片噪声模型研究.机械工程学报,2004,40(7):114-118.
[154]赵键,谢壮宁,黄幼玲.自由场结构体声辐射研究.声学学报,1994,19(1):22-31.
[155]周勃,陈长征等.冷却塔的噪声控制研究.暖通空调,2007,37(3):75-78.
[156]盛美萍,王敏庆.噪声与振动控制技术基础.北京:科学出版社,2001.
[157]Chee Keong Tan,David Mba.Identification of the acoustic emission source during a comparative study on diagnosis of a spur gearbox.Tribology International,2005(38):469-480.
[158]Jae-Seob Kwak.Neural network approach for diagnosis of grinding operation by acoustic emission and power signals.Journal of Materials Processing Technology,2004(147):65-71.
[2]项端祈.空调系统消声与隔振设计.北京:机械工业出版社,2005.
[3]Y.H.Song,Y.Akashi,K.Kuniyoshi.Study on fault detection and diagnosis of building air-conditioning system.Investigation of faulty status using calculation,Summaries of Technical Papers of Annual Meeting of Architectural Institute of Japan,2005:1431-1432.
[4]Y.H.Song,Y.Akashi,Jurng-Jae Yee.A development of easy-to-use tool for fault detection and diagnosis in building air-conditioning systems.Energy and Buildings,2007:1-12(In press).
[5]马大猷.噪声与振动控制工程手册.北京:机械工业出版社,2002.
[6]赵玫,周海亭,陈光冶等.机械振动与噪声学.北京:科学出版社,2004.
[7]L.N.Grace,D.Datta,S.A.Tassou.Sensitivity of refrigeration system performance to charge levels and parameters for on-line leak detection.Applied Thermal Engineering,2005,25(4):557-566.
[8]J.Cui,S.W.Wang.A model-based online fault detection and diagnosis strategy for centrifugal chiller systems.International Journal of Therrnal Science,2005,44(10):986-999.
[9]陈长征,胡立新,周勃等.设备振动分析与故障诊断技术.北京:科学出版社,2006.
[10]应怀樵.现代振动与噪声技术.北京:航空工业出版社,2002.
[11]K.Kuniyoshi,T.Hayashi,D.Sumiyoshi.A study on energy-saving diagnosis of the air conditioning system in the building.Outline of the HVAC&R experimental analysis system,Kyushu Chapter Architectural Research Meeting(Environment) of Architectural Institute of Japan,2005:437-440.
[12]T.I.Salsbury,R.C.Diamond.Fault detection in HVAC systems using model-based feedforward control.Energy and Buildings,2001(33):403-415.
[13]王志毅,谷波,江国和.二级模糊综合评判方法在制冷机故障诊断中的应用.暖通空调,2004,34(1):85-88.
[14]陈友明.自动故障检测与诊断在暖通空调中的研究与应用.暖通空调,2004,34(3):29-33.
[15]Dexter A.L,Ngo D.Fault diagnosis in air-conditionin system:a multi-step fuzzy model-based approach.HVAC&RResearch,2001,7(1):83-102.
[16]Stylianou Meli,Nikanpour Darius.Performan monitoring,faultdetection,and diagnosis of reciprocation chillers.ASHRAETrans.,1996,102(1):615-627.
[17]HouseJohn M,Lee WonYong.Classification techniques for fault detection and diagnosis of an air-handling unit.ASHRAETrans.,1999,105(1):1085-1097.
[18]Hossein.A neural network prototype for fault detection and diagnosis of heating systems.ASHRAE Trans,2007,113(1):634-644.
[19]Mcintosh B.D,Ahn Byung,Mitchell John W.Model-based fault detection and diagnosis for cooling towers.ASHRAETrans.,2001,107(1):839-846.
[20]M.Roger,S.Moreau.Broadband self-noise from loaded fan blades.AIAA Journal,2004,42(3):536-544.
[21]H.Sun,S.Lee.Numerical prediction of centrifugal compressor noise.Journal of Sound and Vibration,2004(269):421-430.
[22]金琰,袁新.应用流固耦合数值方法研究机翼的射流减振技术.空气动力学报,2002,(3):267-273.
[23]R.Ganguli.Optimum design of a helicopter rotor for low vibration using aero elastic analysis and response surface methods.Journal of Sound and Vibration,2002,258(2):327-344.
[24]屈梁生.机械故障诊断学.上海:科技出版社,1986.
[25]盛兆顺,尹畸岭.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.
[26]关惠玲,韩捷.设备故障诊断专家系统原理及实践.北京:机械工业出版社,2000.
[27]House JohnM,Vaezi Nejad,J Michael.An expert rule set for fault detection in air handling units.ASHRAETrans,2001,107(1):858-871.
[28]S.W.Wang,J.B.Wang.Law-based sensor fault diagnosis and validation for building air-conditioning systems.HVAC&R Research,1999,5(4):353-380.
[29]陈长征,周永.小波神经网络法在柴油机故障诊断中的应用.内燃机学报,2002,20(1):89-91.
[30]谷荻隆嗣,荻原将文著,马炫泽.人工神经网络和模糊信号处理.北京:科学出版社,2003.
[3l]G.Betta,C.Liguori,and A.Pietrosanto.A multi-application FFT-analyzer based on a DSP architecture.IEEE Trans.Instrum.Meas.,2001:825-832.
[32]Giovanni Betta,Consolatina Liguori,Alfredo Paolillo.A DSP-based FFT-Analyzer for the fault diagnosis of rotating machine based on vibration analysis.IEEE Transactions on Instrumentation and Measurement,2002,51(6):316-1322.
[33] H. Yoshida, T. Ivami, H. Yuzawa, et al.. Typical faults of air conditioning systems and fault detection by ARX model and extended Kalman filter. ASHRAE Transactions, 1996, 102 (1):557-564.
[34] Conde, M. R... Estimation of thermophysical properties of lubricating oils and their solutions with refrigerants: An appraisal of existing methods. Appl. Therm. Eng. 1996(16): 51—61.
[35] D. Ngo. Fuzzy indentication approach in air-conditioning system. International Journal of HVAC and R Research ,2006,7 (1): 183—192.
[36] Wilson Q. Wanga, M. Farid Golnaraghi, Fathy Ismail. Prognosis of machine health condition using neuro-fuzzy systems. Mechanical Systems and Signal Processing,2004(18): 813—831.
[37] Jesus Manuel, Fernandez Salido, Shuta Murakami. A comparison of two learning mechanisms for the automatic design of fuzzy diagnosis systems for rotating machinery. Applied Soft computing ,2004(4): 413—422.
[38] 虞和济,陈长征,张省等．基于神经网络的智能诊断．北京：冶金工业出版社,2000．
[39] W.Y. Lee, J.M. House, N.H. Kyong. Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks. Applied Energy, 2004, 77 (2): 153—170.
[40] S.W. Wang, Y.M. Chen. Fault-tolerant control for outdoor ventilation air flow rate in building based on neural network. Building and Environment, 2002,37 (7): 691 —704.
[41] D.J. Swider, M.W. Browne, P.K. Bansal, et al.. Modelling of vapour compression liquid chillers with neural networks. Applied Thermal Engineering , 2001, 21 (3): 311—329.
[42] Lee WY, House JM, Park C. Kelly JE. Fault diagnosis of an air-handling unit using artificial neural network.ASHRAE Trans, 1996(102): 540—549.
[43] B. Samanta, K. R. Balushi. Condition classification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines. Mechanical System and Signal Processing, 2005, 19 (2):371—390.
[44] A.K.Nandi. Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Ind. Electron, 1997,47:1060—1069.
[45] Y.C. Chang, F.A. Lin, C.H. Lin. Optimal chiller sequencing by branch and bound method for saving energy. Energy Conversion and Management, 2005,46 (12—14):2158—2172.
[46] J. Burnett. Online adaptive control for optimizing variable-speed pumps of indirect water-cooled chilling systems. Applied Thermal Engineering, 2001, 21(11): 1083— 1103.
[47] Kyusung Kim, Alexander G. Parlos. Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Trans on Mechatronics, 2002, 7(2): 201—219.
[48] Meyer Y. Wavelets algorithms and applications. J. SIAM, 1993.
[49]B.A Paya,I.Esat.Artificial neural network based fault diagnostics of rotating machinery using wavelet transform as a preprocessor.Mechanical Systems and Signal Processing,2004:640-654.
[50]T.Matsuura.An application of neural network for selecting feature parameters in machinery diagnosis.Journal of Materials Processing Technology,2004:1-5.
[51]T.Kaewkongka,Y.H.Joe Au,R.Rakowski,B.E.Jones.Continuous wavelet transform and neural network for condition monitoring of dynamic machinery.IEEE Instrumentation and Measurement Technology Conference Budapest,2001:21-23.
[52]王志毅,谷波,黎远光.小波变换应用于空调制冷机组故障先兆预测.暖通空调,2004,34(10):117-119.
[53]Zhimin Du,Xinqiao Jin.Detection and diagnosis for multiple faults in VAV systems.Energy and Buildings,2007(39):923-934.
[54]Achmad Widodo,Bo-Suk Yang.Support vector machine in machine condition monitoring and fault diagnosis.Mechanical Systems and Signal Processing,2007(21):2560-2574.
[55]R.Fang.Induction machine rotor diagnosis using support vector machines and rough set.Lecture Notes in Artificial Intelligence,2006(4114):631-636.
[56]A.Widodo,B.S.Yang,T.Han.Combination of independent component analysis and support vector machine for intelligent faults diagnosis of induction motors.Expert System with Application,2007(32):299-312.
[57]S.F.Yuan,F.L.Chu.Support vector machines-based fault diagnosis for turbo-pump rotor.Mechanical System and Signal Processing,2006,20(4):939-952.
[58]S.F.Yuan,F.L.Chu.Fault diagnosis based on support vector machine with parameter optimization by artificial immunization algorithm.Mechanical System and Signal Processing,2007,21(3):1318-1330.
[59]B.Samanta.Gear fault detection using artificial neural networks and support vector machines with genetic algorithms.Mechanical Systems and Signal Processing,2004,18(3):625-644.
[60]C.W.Hsu,C.J.Lin.A comparison of methods for multi-class support vector machines.IEEE Transactions on Neural Networks,2002(13):415-425.
[61]J.Liang,R.Du.Model-based Fault Detection and Diagnosis of HVAC systems using Support Vector Machine method.Mechanical Systems and Signal Processing,2007(21):2560-2574.
[62]陈长征,王楠,刘强.遗传算法中交叉和变异概率选择的自适应方法及作用机理.控制理论与应用,2002,19(1):41-43.
[63]B.S.Yang,T.Han,W.W.Hwang.Fault diagnosis of rotating machinery based on multi-class support vector machines.Journal of Mechanical Science and Technology,2005,19(3):845-858.
[64]晋欣桥,杜志敏,孙勇.基于主成分分析法的变风量空调系统传感器故障诊断.上海交通大学学报,2005,39(8):1222-1225.
[65]D.Wang,J.A.Romagnoli.Robust multi-scale principal components analysis with applications to process monitoring.Journal of Process Control,2005,15(8):869-882.
[66]House J.M.,Lee W.Y.,Shin D.R..Classification techniques for fault detection and diagnosis of an air-handling unit.ASHRAE Trans,1999,105:1087-1097.
[67]Katipamula S,Pratt RG,Shassin DP,et.al.Automated fault detection and diagnosis for outdoor-air ventilation systems and economizer:Testing,methodology and results from field.ASHRAE Trans,1999,105:555-567.
[68]M.Stylianou,Application of classification functions to chiller fault detection and diagnosis.ASHRAE Transactions 1997,103(1):640-656.
[69]K.Kim,A.G.Parlos.Model-based fault diagnosis of induction motors using non-stationary signal segmentation.Mechanical Systems and Signal processing,2002,16(2):223-253.
[70]I.Daubechies.The wavelet transform,time-frequency localization and signal analysis.IEEE Trans.Information Theory,1999,36(5):961-1005.
[71]Wan Lianghon.,Liu Yibing.,Feng Dongliang.Application of wavelet packets analysis to fault diagnose of rolling bearings.Modern Electric Power,2004,21(1):24-26.
[72]Isermann R.Supervision,fault detection and fault-diagnosis methods.Control Eng Practice,1997,5(5):639-652.
[73]R.N.Mahanty,P.B.Dutta Gupta.Application of RBF neural network to fault classification and location in transmission lines.IEE Proc-Gener.Transm.Distrib.,2004,151:201-212.
[74]M.Zacksenhouse,S.Braun,M.Feldman.Toward helicopter gearbox diagnostics from a small examples.Mechanical Systems and Signal Processing,2000,14(4):523-543.
[75]L.B.Jack,A.K.Nandi.Fault detection using support vector machines and artificial neural network,augmented by genetic algorithms.Mechanical System and Signal Processing,2002,16:373-390.
[76]B.Samanta,K.R.Al-Balushi,S.A.Al-Araimi.Artificial neural network and support vector machine with genetic algorithm for bearing fault detection.Engineering Application of Artificial Intelligence,2003,16:657-665.
[77]Wang,S.and Cui,J.Sensor-fault detection,diagnosis and estimation for centrifugal chiller systems using principal-component analysis method.Applied Energy,2005,8(23):197-213.
[78]S.Yoon,J.F.MacGregor.Fault diagnosis with multivariate statistic models.Journal of Process Control,2001,11(1):387-400.
[79] Sung-Hwan Cho, Hoon-Cheol Yang, M. Zaheer-uddin. Transient pattern analysis for fault detection and diagnosis of HVAC systems. Energy Conversion and Management, 2005( 46): 3103-3116.
[80] 黄其柏．工程噪声控制学．武汉:华中理工大学出版社,1999．
[81] U. Ayr,E.Cirillo, F. Martellotta. An experimental study on noise indices in air-conditioned offices. Applied Acoustics, 2001, 62:633—643.
[82] Tim Toutountzakis, David Mba. Observations of acoustic emission activity during gear defect diagnosis. NDT&E International, 2003(36): 471—477.
[83] F.Pellicano, F.Vestroni. Complex dynamics of high-speed axially moving systems. Journal of Sound and Vibration, 2002, 258(1): 31—44.
[84] A. J. Bell, T. J. Seinowski. An information maximization approach to blind separation and blind deconvolution. Neural Computation, 1995 (7): 1129—1159.
[85] 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, 2007, 16:719—740.
[86] Guillaume Gelle, Maxime Cloas, Christine Serviere. Blind source separation: a new pre-processing tool for rotating machines monitoring. IEEE. Transactions on Instrumentation and Measurement,2003,52:790—795.
[87] C. Serviere., P. Fabry. Blind source separation of noisy harmonic signals for rotating machine diagnosis. Journal of Sound and Vibration, 2004, 272:317—339.
[88] Gwo-Ching Liao, Ta-Peng Tsao: Application of fuzzy neural networks and artificial intelligence for load forecasting. Electric Power Systems Research, 2004, 70:237—244.
[89] Xinhua Xu, Fu Xiao, Shengwei Wang. Enhanced chiller sensor fault detection, diagnosis and estimation using wavelet analysis and principal component analysis methods. Applied Thermal Engineering, 2007:1 — 12.
[90] P. Haves, T.I. Salsbury, J.A. Wright. Condition monitoring in HVAC subsystem using first principles models. ASHRAE Transactions, 1996, 102 (1):519—527.
[91] J.E. Jackson. A User's Guide to Principal Components. John Wiley &Sons, 1991.
[92] X.Q. Jin, Z.M. Du. Fault tolerant control of outdoor air and AHU supply air temperature in VAV air conditioning systems using PCA method. Applied Thermal Engineering, 2006,26 (11-12): 1226—1237.
[93] R. Peled, S. Braun, M. Zacksenhouse. A blind deconvolution separation of multiple sources, with application to bearing diagnostics. Mechanical Systems and Signal Processing, 2005, 19:1181 — 1195.
[94]G.Gelle,M.Colas,G.Delaunay,Blind sources separation applied to rotating machines monitoring by acoustical and vibrations analysis.Mechanical Systems and Signal Processing,2000,14(3):427-442.
[95]T.J.Sejnowski.A non-linear information maximization approach that performs blind deconvolution approach.Neural Computation,1998(12):571-583.
[96]J.K.Tugnait.Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria.IEEE Transactions on Signal Processing,1997,45(3):658-672.
[97]Massoud Babaie-Zadeha,Christian Jutten A general approach for mutual information minimization and its application to blind source separation.Signal Processing,2005,85:975-995.
[98]宋友,重堪,李其汉.基于三阶累积量的转子振动信号降噪方法研究.航空动力学报,2002,17(3):363-366.
[99]李舜酩.转子振动故障信号的盲分离.航空动力学报,2005,20(5):751-756.
[100]钟振茂,陈进,钟平.盲源分离技术用于机械故障诊断的研究初探.机械科学与技术,2002,21(2):282-284.
[101]Roal M.J.,Erling J.G.A new non-linear adaptive blind separation approach to gear tooth failure detection and analysis.Mechanical Systems and Signal Processing,2002,16:719-740.
[102]Roger Boustany.A subspace method for the blind extraction of a cyclostationary source:Application to rolling element bearing diagnostics.Mechanical Systems and Signal Processing,2005,19:1245-1259.
[103]J.Antoni,F.Bonnardot,A.Raad,M.El Badaoui[J].Cyclostationary modelling of rotating machine vibration signals.Mechanical Systems and Signal Processing,2004,18(6):1285-1314.
[104]J.Antoni,F.Guillet,M.El Badaoui.Blind separation of convolved cyclostationary processes[J].Signal Processing,2005,85:1-66.
[105]A.Ferre' ol,P.Chevalier[J].On the behavior of current second and higher order blind source separation methods for cyclostationary sources.IEEE Transactions on Signal Processing,2000,48(6):1712-1725.
[106]卢文祥,杜润生.工程测试与信息处理.华中科技大学出版社,2004.
[107]U.M.Bae,T.W.Lee,S.Y.Lee.Blind signal separation in teleconferencing using the ICA mixture model.Electronic Letters,2000,37(7):680-692.
[108]A.Belouchrani,A.Cichocki.Robust whitening procedure in blind source separation context.Electronic Letters,2000,36(24):2050-2053.
[109]X.R.Cao,R.W.Liu.General approach to blind source separation.IEEE Trans.Signal Processing,1996,44(3):562-571.
[110]张云涛,龚玲.数据挖掘原理与技术.北京:电子工业出版社,2004.
[111]罗抟翼,程桂芬.控制工程与信号处理.北京:化学工业出版社,2004.
[112]夏天昌.系统辨识.北京:国防工业出版社,1984.
[113]黄长艺,卢文祥,熊诗波.机械工程测量与试验技术.北京:机械工业出版社,2003.
[114]L.C.Ludeman著,邱天爽等译.随机过程—滤波、估计与检测.北京:电子工业出版社,2005.
[115]邓自立.最优估计理论及其应用.哈尔滨工业大学出版社,2005.
[116]A.Cichocki,W.Kasprzak.Nonlinear learning algorithms for blind separation of natural images.Neural Network World,1996,6(4):515-523.
[117]J.Antoni.Blind separation of vibration components:Principles and demonstrations.Mechanical Systems and Signal Processing,2005(19):1166-1180.
[118]J.Antoni,L.Garibaldi,S.Marchesiello.New separation techniques for output-only modal analysis.Shock and Vibration,2004,11(3-4):227-242.
[119]Christian Jutten,Jeanny Heraul.Blind separation of sources,Part Ⅰ:An adaptive algorithm based on neuromimetic architecture.Signal Process,1991,24(1):1-10.
[120]S.Amari,A.Cichocki.Adaptive blind signal processing neural network approaches[J].Proceedings IEEE,1998,86:1186-1187.
[121]S.Matsuoka.Blind separation of nonstationary sources in noisy mixtures.Electronic Letters,2000,36(4):848-859.
[122]A.Belouchrani,K.Abed Meraim,J.F.Cardoso.A blind source separation techhique using second-order statistics.IEEE Trans.Processing,1997,45(2):434-444.
[123]N.Bouguerrioul,M.Haritopoulos,C.Capdessus.Novel cyclostationarity-based blind source separation algorithm using second-order statistical properties:Theory and application to the bearing defect diagnosis.Mechanical Systems and Signal Processing,2005,19:1260-1281.
[124]Hoang-Lan Nguyen Thi,C.Jutten.Blind source separation for convolutive mixtures.Signal Processing,1995,45(2):209-229.
[125]M.Zacksenhouse,S.Braun.A application to bearing diagnostics based on time-delayed second order correlations.Mechanical Systems and Signal Processing,2002(9):925-934.
[126]S.Amari,S.C.Douglas.Novel on-line adaptive learning algorithms for blind deconvolution using natural gradient approach.Proceeding 11~(th) IFAC Symposium on System Identification,1997(3):1057-1062.
[127]K.Tugnait.Identification and deconvolution of multichannel linear non-Gaussian processes using higher order statistics and inverse filter criteria.IEEE Transactions on Signal Processing,1997,45(3):658-672.
[128]D.Erdogmus,et al.Blind source separation using Renyis alpha-marginal entropies.Neurocomputing,2002,49(1):25-38.
[129]S.Amari,S.C.Douglas.Multichannel blind deconvolution and source separation using the natural gradient.Proceedings IEEE,1997,85:873-879.
[130]D.Erdogmus,J.C.Principe.Generalized information potential criterion for adaptive system training.IEEE Trans.Neural Networks,2002,13(5):1035-1044.
[131]A.J.Bell,T.J.Sejnowski.A non-linear information maximization approach that performs blind separation.Advances in Neural Information Processing Systems,1995,7:467-474.
[132]K.Hild II,et al.Blind source separation of time-varying,instantaneous mixtures using an on-line algorithm.International Conference on Acoustics,Speech,and Signal Processing,Orlando,2002(13-17):993-996.
[133]Bin-Chul Ihm,Dong-Jo Park.Blind separation of sources using higher-order cumulants Signal Processing,1999,73:267-276.
[134]P.Comon.Higher-order separation,application to detection and localization.Signal Processing,1999,71:277-280.
[135]D.Pham.Fast algorithm for estimating mutual information,entropies,and score functions.IEEE Signal Processing Letters,2003(1-4):17-22.
[136]J.F.Cardoso.Infomax and maximum likelihood for blind source separation.IEEE Signal Processing Letters,1997,4(4):112-124.
[137]D.Erdogmus,J.C.Principe.Generalized information potential criterion for adaptive system training.IEEE Trans.Neural Networks,2002,13(5):1035-1044.
[138]D.Pham.Contrast functions for blind separation and deconvolution of sources.International Workshop on Independent Component Analysis and Signal Separation,San Diego,CA,2001(9-12):37-52.
[139]J.F.Cardoso,B.H.Laheld.Equivariant adaptive source separation.IEEE Trans.Signal Processing,1996,44(12):3017-3030.
[140]赵荣义,范存养,薛殿华.空气调节(第三版).北京:中国建筑工业出版社,1994.
[141]虞和济.设备故障诊断工程.北京:冶金工业出版社,2001.
[142]豐天利夫.设备现场诊断的开展方法.北京:机械工业出版社,1985.
[143]徐敏.设备故障诊断手册.西安:西安交通大学出版社,1998.
[144]周勃,费朝阳,张宇等.基于振声分析的氨制冷螺杆压缩机组的故障诊断与噪声治理研究.环境工程,2007,25(1):49-51.
[145]柯拉科特.机械故障的诊断与情况监测.北京:机械工业出版社,1984.
[146]M.E.Badaoui,J.Daniere,F.Guillet.Separation of combustion noise and piston-slap in diesel engine.Mechanical Systems and Signal Processing,2005,19:1209-1217.
[147]S.N.Y.Gerges,J.C.Luca,N.Labor.A literature review of diesel engine noise with emphasis on piston slap.International Journal of Acoustic and Vibration,2000,(5):37-45.
[148]Margaret B.Bailey,Jan F.Kreiderb.Creating an automated chiller fault detection and diagnostics tool using a data fault library.ISA Transactions,2003,42:485-495.
[149]孙晓峰,周盛.气动声学.北京:国防工业出版社,1994.
[150]莫尔斯,英格特.理论声学.北京:科学出版社,1984.
[151]M.J.Fisher,R.H.Self.Aeroacoustics Research in Europe:the Ceas-asc Report on 2001 Highlights.Journal of Sound and Vibration,2002,258(1):1-30.
[152]黎胜,赵德有用边界元计算机构振动辐射声场.大连理工大学学报,2000,40(4):391-394.
[153]李林凌,黄其柏.风机叶片噪声模型研究.机械工程学报,2004,40(7):114-118.
[154]赵键,谢壮宁,黄幼玲.自由场结构体声辐射研究.声学学报,1994,19(1):22-31.
[155]周勃,陈长征等.冷却塔的噪声控制研究.暖通空调,2007,37(3):75-78.
[156]盛美萍,王敏庆.噪声与振动控制技术基础.北京:科学出版社,2001.
[157]Chee Keong Tan,David Mba.Identification of the acoustic emission source during a comparative study on diagnosis of a spur gearbox.Tribology International,2005(38):469-480.
[158]Jae-Seob Kwak.Neural network approach for diagnosis of grinding operation by acoustic emission and power signals.Journal of Materials Processing Technology,2004(147):65-71.
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