基于声强测量技术的噪声自动分析系统研究
|
摘要
本文针对车辆及其他机电产品降噪和故障诊断的技术需求，对基于声强测量技术的噪声自动分析系统进行了深入的研究。在噪声自动分析系统的总体方案设计、硬件设计、软件设计、声强信号处理与算法研究及系统实际工程应用等方面本文做了全面、细致的工作，所研制的噪声自动分析系统已获国家实用新型专利，为汽车等机电产品的噪声控制、质量监测及故障诊断等提供了有效的分析工具和手段。
噪声自动分析系统是用于自动分析与监测车辆及其他机电产品运转噪声的机电一体化设备，涉及声强测量技术、虚拟仪器技术、计算机控制与分析技术、信号处理技术及人工智能技术等的应用与集成。它针对现有噪声分析设备自动化、智能化程度不高等不足之处，建立了以计算机为控制中枢的测控平台。通过计算机控制下的二维自动导轨的运动带动声强传感器在竖直二维平面内快速移动、准确定位，从而对测量面上若干测点进行自动扫描，并将扫描过程中所拾取的噪声信号经动态信号采集卡的调理和数模转换后输入计算机。在计算机中通过软件算法进行声强的计算与分析、自动绘制声强分布云图、噪声源的自动辨识、异常声响的自动判定以及故障的自动识别等工作。整个测量过程具有测量速度快、定位精度高及声强计算准确等特点，在噪声源辨识与分析和基于噪声的故障诊断等方面自动化、智能化程度较高。
本文首先对噪声自动分析系统的总体方案进行了设计，满足其自动化、智能化要求；基于总体方案的设计要求，分别对系统的硬件、软件进行了详细的设计工作，并对系统进行了标定与调试；在此基础上，成功地实现了整个噪声自动分析系统，为进一步的研究工作奠定了坚实的基础。
运用基于互谱分析的声强算法，对噪声自动分析系统在噪声监测和主噪声源识别方面的性能进行了研究。研究结果表明：在噪声监测方面，系统具有快速自动扫描的能力，满足了噪声监测对快速化测量的要求；在主噪声源识别方面，通过运用复合型优化算法，系统可以在计算机的控制下对主噪声源进行自动优化搜索，提高了主噪声源的识别定位精度。
基于互谱分析的声强算法是分析平稳噪声信号的有效工具，但对于短时非平稳噪声信号其分析能力有所不足。本文针对此问题研究了基于STFT谱的声强联合时—频分析方法，该方法可以将非平稳的一维噪声信号以二维的时间频率函数形式表示出来，有效地提取噪声信号随时间和频率的变化特征，从而分析噪声信号非平稳性的成因。利用上述特性，本文对某型号发动机加速噪声的非平稳性进行了分析。
针对汽车等机电产品所辐射的噪声普遍存在非平稳的瞬时冲击噪声的特点，在分析小波和小波包变换原理的基础上，研究了一种单小波包重构算法，并利用该算法对瞬时冲击信号的检测和特征提取进行了仿真分析。在此基础上，研究了一种基于小波包分析的声强算法，并对其声强测量精度进行了验证。基于小波包分析的声强计算方法融合了小波包分析和声强两种技术，可有效地分析故障发生时瞬时冲击噪声的反常现象，为基于噪声信号的故障诊断提供了一种有效的方法。本文利用该方法对某型号发动机单缸失火故障诊断指标的选取问题进行了分析。
针对传统声强测量中容易受到强大背景噪声干扰的问题，本章从软件和硬件两个方面对提高声强测量抗干扰性的方法进行了探索性研究。在软件方面，将选择性声强技术应用到噪声源辨识当中，可以将目标噪声源从强大的背景噪声中有效地识别出来。在硬件方面，通过一种抗干扰屏蔽罩的设计，可以在一定程度上屏蔽某些方向上非目标噪声源辐射声波的干扰，提高了对目标噪声源的辨析能力。
本文的研究着重于噪声自动分析系统解决实际应用问题的能力，所进行的研究工作都进行了相应的实验分析，验证了其可行性。本文最后针对某型号微型车车外辐射噪声较大的问题，利用噪声自动分析系统对其车身外表面进行了声强测试，识别出了其主要噪声源，解决了此工程实际问题。
Aiming at noise reduction and fault diagnosis of vehicle and other mechatronicsproducts an automatic noise analysis system is studied based on sound intensity technologyin this paper. The research of the paper includes scheme design, hardware design, softwaredesign, sound intensity processing algorithm, engineering application and so on. Theautomatic noise analysis system provides a tool for noise control, quality monitoring andfault diagnosis of vehicle and other mechatronics products.
The automatic noise analysis system is a mechatronics equipment used to analyzeand monitor noise of vehicle and other mechatronics products in working. It comes downto sound intensity, virtual instrument, computer control and analysis, signal processing andartifical intelligent technologies. Existing noise analysis equipments have insufficiencieson automatization and intelligentization. So the automatic noise analysis system usescomputer as the central workbench. By a computer controlled planar robot sound intensityprobe can be drived to scan measurement plane automatically with high moving speed.Then the sound intensity probe can scan the measurement plane automatically and measurenoise signals simultaneously. The measured noise signals are transmited into computerafter conditioned and acquired by the dynamic signal acquisition card. In the computer themeasured noise signals are analyzed and processed by the software algorithm such assound intensity computing and analyzing, plotting equal intensity contour plans, noisesource identification, distinguishing abnormal sound, fault diagnosis and so on. Hige speed,exact location precision and accurate sound computing are characteristic of the system. Thesystem can be used for noise source identification and fault diagnosis with automatizationand intelligentization.
The goal of the scheme design of the automatic noise analysis system is to realizeautomatization and intelligentization. Based on above scheme the hardware and thesoftware of the system are designed detailedly. Calibratation and debugging is alsocompleted. Consequently the whole system is realized. The realization of the automaticnoise analysis system lay the foundation for the further research.
Using cross spectrum based sound intensity processing algorithm noise monitoringand main noise source identification is studied in the paper. On the aspect of noisemonitoring the automatic noise analysis system has the ability to scan noise source fleetlyand automatically. Using multiple model optimization algorithm the system can search for main noise source controlled by computer and the location precision is high.
Cross spectrum based sound intensity processing algorithm is effective on stationarynoise signals. But it can't deal with nonstationary noise signals. Aiming at this problem ajoint time—frequency analysis method is studied in the paper based on STFT spectrum.This method can express noise signals as time—frequency form. The variety characteristicof noise signals along with time—frequency can be extracted by this method. Soundintensity joint time—frequency analysis method based on STFT spectrum provides a toolto analyze nonstationary characteristic of noise signals. The acceleration noise of an engineis analyzed with the method.
Nonstationary instantaneous shock noise signals is ubiquitous in vehicle and othermechatronics products. Aiming at this problem a single wavelet packet reconstructionalgorithm is studied in the paper based on theory of wavelet transform. Using thisalgorithm a simulated instantaneous shock signal is analyzed to extract its instantaneouscharacteristic. Upon above work a sound intensity computing algorithm is studied based onwavelet packets analysis and the precision of this method is validated. The sound intensityalgorithm based on wavelet packets analysis incorporates sound intensity technology withwavelet packets technology so it can analyze abnormal phenomena of instantaneous shocknoise signals when faults take place. Meanwhile a tool for fault diagnosis is provided basedon noise signals. Using this method this paper analyzes the chosen of fault diagnosis indexof single cylinder misfire.
In the sound intensity measurement the interference of background noise is a realmatter. From the aspects of software and harkware the anti-interference method is studiedfor sound intensity measurement. Using selective sound intensity technology the targetnoise source can be identified from strong background noise. An anti-interference shield isstudied, which can screen sound wave of background noise so the ability of sound intenstyto discriminate target noise source is improved.
The research of the paper emphasizes on the engineering application. The feasibilityof researches in the paper is confirmed through experiments. To reduce exterior noise of aminibus the automatic noise analysis system is used to measure sound intensity of itssurface noise. According to the test result the maim noise source is separated, whichprovides reference for noise control of the minibus.
噪声自动分析系统是用于自动分析与监测车辆及其他机电产品运转噪声的机电一体化设备，涉及声强测量技术、虚拟仪器技术、计算机控制与分析技术、信号处理技术及人工智能技术等的应用与集成。它针对现有噪声分析设备自动化、智能化程度不高等不足之处，建立了以计算机为控制中枢的测控平台。通过计算机控制下的二维自动导轨的运动带动声强传感器在竖直二维平面内快速移动、准确定位，从而对测量面上若干测点进行自动扫描，并将扫描过程中所拾取的噪声信号经动态信号采集卡的调理和数模转换后输入计算机。在计算机中通过软件算法进行声强的计算与分析、自动绘制声强分布云图、噪声源的自动辨识、异常声响的自动判定以及故障的自动识别等工作。整个测量过程具有测量速度快、定位精度高及声强计算准确等特点，在噪声源辨识与分析和基于噪声的故障诊断等方面自动化、智能化程度较高。
本文首先对噪声自动分析系统的总体方案进行了设计，满足其自动化、智能化要求；基于总体方案的设计要求，分别对系统的硬件、软件进行了详细的设计工作，并对系统进行了标定与调试；在此基础上，成功地实现了整个噪声自动分析系统，为进一步的研究工作奠定了坚实的基础。
运用基于互谱分析的声强算法，对噪声自动分析系统在噪声监测和主噪声源识别方面的性能进行了研究。研究结果表明：在噪声监测方面，系统具有快速自动扫描的能力，满足了噪声监测对快速化测量的要求；在主噪声源识别方面，通过运用复合型优化算法，系统可以在计算机的控制下对主噪声源进行自动优化搜索，提高了主噪声源的识别定位精度。
基于互谱分析的声强算法是分析平稳噪声信号的有效工具，但对于短时非平稳噪声信号其分析能力有所不足。本文针对此问题研究了基于STFT谱的声强联合时—频分析方法，该方法可以将非平稳的一维噪声信号以二维的时间频率函数形式表示出来，有效地提取噪声信号随时间和频率的变化特征，从而分析噪声信号非平稳性的成因。利用上述特性，本文对某型号发动机加速噪声的非平稳性进行了分析。
针对汽车等机电产品所辐射的噪声普遍存在非平稳的瞬时冲击噪声的特点，在分析小波和小波包变换原理的基础上，研究了一种单小波包重构算法，并利用该算法对瞬时冲击信号的检测和特征提取进行了仿真分析。在此基础上，研究了一种基于小波包分析的声强算法，并对其声强测量精度进行了验证。基于小波包分析的声强计算方法融合了小波包分析和声强两种技术，可有效地分析故障发生时瞬时冲击噪声的反常现象，为基于噪声信号的故障诊断提供了一种有效的方法。本文利用该方法对某型号发动机单缸失火故障诊断指标的选取问题进行了分析。
针对传统声强测量中容易受到强大背景噪声干扰的问题，本章从软件和硬件两个方面对提高声强测量抗干扰性的方法进行了探索性研究。在软件方面，将选择性声强技术应用到噪声源辨识当中，可以将目标噪声源从强大的背景噪声中有效地识别出来。在硬件方面，通过一种抗干扰屏蔽罩的设计，可以在一定程度上屏蔽某些方向上非目标噪声源辐射声波的干扰，提高了对目标噪声源的辨析能力。
本文的研究着重于噪声自动分析系统解决实际应用问题的能力，所进行的研究工作都进行了相应的实验分析，验证了其可行性。本文最后针对某型号微型车车外辐射噪声较大的问题，利用噪声自动分析系统对其车身外表面进行了声强测试，识别出了其主要噪声源，解决了此工程实际问题。
Aiming at noise reduction and fault diagnosis of vehicle and other mechatronicsproducts an automatic noise analysis system is studied based on sound intensity technologyin this paper. The research of the paper includes scheme design, hardware design, softwaredesign, sound intensity processing algorithm, engineering application and so on. Theautomatic noise analysis system provides a tool for noise control, quality monitoring andfault diagnosis of vehicle and other mechatronics products.
The automatic noise analysis system is a mechatronics equipment used to analyzeand monitor noise of vehicle and other mechatronics products in working. It comes downto sound intensity, virtual instrument, computer control and analysis, signal processing andartifical intelligent technologies. Existing noise analysis equipments have insufficiencieson automatization and intelligentization. So the automatic noise analysis system usescomputer as the central workbench. By a computer controlled planar robot sound intensityprobe can be drived to scan measurement plane automatically with high moving speed.Then the sound intensity probe can scan the measurement plane automatically and measurenoise signals simultaneously. The measured noise signals are transmited into computerafter conditioned and acquired by the dynamic signal acquisition card. In the computer themeasured noise signals are analyzed and processed by the software algorithm such assound intensity computing and analyzing, plotting equal intensity contour plans, noisesource identification, distinguishing abnormal sound, fault diagnosis and so on. Hige speed,exact location precision and accurate sound computing are characteristic of the system. Thesystem can be used for noise source identification and fault diagnosis with automatizationand intelligentization.
The goal of the scheme design of the automatic noise analysis system is to realizeautomatization and intelligentization. Based on above scheme the hardware and thesoftware of the system are designed detailedly. Calibratation and debugging is alsocompleted. Consequently the whole system is realized. The realization of the automaticnoise analysis system lay the foundation for the further research.
Using cross spectrum based sound intensity processing algorithm noise monitoringand main noise source identification is studied in the paper. On the aspect of noisemonitoring the automatic noise analysis system has the ability to scan noise source fleetlyand automatically. Using multiple model optimization algorithm the system can search for main noise source controlled by computer and the location precision is high.
Cross spectrum based sound intensity processing algorithm is effective on stationarynoise signals. But it can't deal with nonstationary noise signals. Aiming at this problem ajoint time—frequency analysis method is studied in the paper based on STFT spectrum.This method can express noise signals as time—frequency form. The variety characteristicof noise signals along with time—frequency can be extracted by this method. Soundintensity joint time—frequency analysis method based on STFT spectrum provides a toolto analyze nonstationary characteristic of noise signals. The acceleration noise of an engineis analyzed with the method.
Nonstationary instantaneous shock noise signals is ubiquitous in vehicle and othermechatronics products. Aiming at this problem a single wavelet packet reconstructionalgorithm is studied in the paper based on theory of wavelet transform. Using thisalgorithm a simulated instantaneous shock signal is analyzed to extract its instantaneouscharacteristic. Upon above work a sound intensity computing algorithm is studied based onwavelet packets analysis and the precision of this method is validated. The sound intensityalgorithm based on wavelet packets analysis incorporates sound intensity technology withwavelet packets technology so it can analyze abnormal phenomena of instantaneous shocknoise signals when faults take place. Meanwhile a tool for fault diagnosis is provided basedon noise signals. Using this method this paper analyzes the chosen of fault diagnosis indexof single cylinder misfire.
In the sound intensity measurement the interference of background noise is a realmatter. From the aspects of software and harkware the anti-interference method is studiedfor sound intensity measurement. Using selective sound intensity technology the targetnoise source can be identified from strong background noise. An anti-interference shield isstudied, which can screen sound wave of background noise so the ability of sound intenstyto discriminate target noise source is improved.
The research of the paper emphasizes on the engineering application. The feasibilityof researches in the paper is confirmed through experiments. To reduce exterior noise of aminibus the automatic noise analysis system is used to measure sound intensity of itssurface noise. According to the test result the maim noise source is separated, whichprovides reference for noise control of the minibus.
引文
[1] 孙林．国内外汽车噪声法规和标准的发展．汽车工程．2000(3)：154-158
[2] 常思勤．汽车动力装置．第1版．北京：机械工业出版社，2006，2
[3] D.Mba, Raj B.K.N.Rao. Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines: Bearings, Pumps, Gearboxes, Engines, and Rotating Structures. The Shock and Vibration Digest. 2006(1): 3-16
[4] Friis-Hausen A, Fog.T.L. Monitoring Exhaust Valve Leaks and Misfire in Marine Diesel Engines. In Proceedings of COMADEM 2001:641-648
[5] Morhain A, Mba D. Bearing Defect Diagnosis and Acoustic Emission. Journal of Engineering Tribology, I Mech E. 2003(4): 257-272
[6] Marc Marroquin. A Comparison of Seven Different Noise Identification Techniques. 2003 SAE Paper: 2141-2151
[7] 郭建新，左言言，宫镇，曾发林．厢式汽车车内噪声的试验研究．江苏理工大学学报(自然科学版)．1999(4)：28-31
[8] 梁兴雨，舒歌群．柴油机噪声源的识别研究．内燃机工程．2005(6)：67-71
[9] Hald J., Christensen J.J. A class of optimal broadband phased array geometries designed for easy construction. Proceeding of Inter-Noise 2002
[10] J.J. Christensen, J. Hald. Beamforming. B & K Technical Review. 2004(1): 1-54
[11] J. Hald. Combined NAH and Beamforming Using the Same Array. B & K Technical Review. 2005(1): 11-39
[12] 宋雷鸣，孙守光，邢优胜，张新华．基于相控阵列的噪声源识别及仿真研究．铁道学报．2005(6)：101-103
[13] 蒋孝煜，连小珉．声强技术及其在汽车工程中的应用．第1版．北京：清华大学出版社，2001．6
[14] M. J. Crocker, J. P. Arenas. Fundamentals of the Direct Measurement of Sound Intensity and Practical Applications. ACOUSTICAL PHYSICS. 2003(2): 163-175
[15] F.J. Fahy. Sound Intensity. Second Edition. London:E & FN Spon, 1995
[16] Finn Jacobsen. A comparison of two different sound intensity measurement principles. J. Acoust. Am, 2005(3): 1510-1517
[17] F. Jacobsen, H.-E. de Bree. Intensity-based sound power determination under adverse sound field conditions: p-p versus p-u probes. Proceedings of Twelfth International Congress on Sound and Vibration, 2005
[18] Chung J Y, Pope J. Practical measurement of acoustic intensity—the two microphone corss spectral method. Proceedings of inter—noise 78, 1978:893-900
[19] Fahy F J. Measurement of acoustic intensity using the cross spectral density of weo microphone signals. J. Acoust Soc.Amer., 1977,62(4): 1057-1059
[20] Alfredson R J. A new technique for noise source identification on a multieylinder automotive engine. Proceedings of Noise—Con77, 1977:307-318
[21] IEC Publication 61043, "Instruments for the measurement of sound intensity-Measurements with pairs of pressure sensing microphones," 1993
[22] American National Standard ANSI S1.9-1996, "Instruments for the measurement of sound intensity," 1996
[23] 陆加良，张卫东．用于测量声功率的声强频谱分析仪．仪器仪表学报．1994(3)：244-249
[24] 邓兆祥，李克强，何渝生．汽车噪声声强测量分析系统的开发及应用．汽车工程．1994(5)：283-289
[25] 梁杰，孙巍，程鹏．虚拟式声强分析系统在发动机噪声源识别中的应用．吉林大学学报(工学版)．2004(2)：237-240
[26] 郝志勇，韩军，毕凤荣．一种中型乘用汽车表面噪声的声强测试分析．汽车工程．2003(4)：392-394
[27] 韩军，郝志勇，刘月辉．柴油机表面噪声的声强测量与分析．汽车工程．2003(3)：272-274
[28] 邓兆祥，李景渊，褚志刚，许响林，胡成太．SC6360B车外加速噪声的控制．重庆大学学报(自然科学版)．2005(4)：5-9
[29] 吕兴才，黄震．柴油机噪声源的声强识别方法．农业机械学报．2004(5)：51-54
[30] 金岩，郝志勇．车用汽油机噪声源的声强测量识别．农业机械学报．2006(2)：14-17
[31] Marc Marroquin. New Innovation in Sound Intensity: Selective Intensity. SAE 2001
[32] 俞明，柳文斌．轿车车内振动噪声源的识别．机床与液压．2003(5)：117-119
[33] 罗玉涛，俞明，柳文斌，孙国斌．用选择性声强技术识别车内噪声及其成因．汽车技术．2002(9)：20-22
[34] Maynard J D, Williams E G, Lee Y. Nearfield acoustic holography Ⅰ: Theory of generalized holography and the development of NAH. J. A. S. A. 1985(4): 1395-1413
[35] Veronesi W A, Maynard J D. Nearfield acoustic holography(NAH) Ⅱ: Holographic reconstruction algorithms and computer implementation. J. A. S. A. 1987(5): 1307-1322
[36] J.J.Christensen, J.Hald, J.Morkholt, A.Schuhmacher, C.Blaabjerg. A Review of Array Techniques for Noise Source Location. 2003 SAE Paper: 2152-2160
[37] Kollek W., Kudzma Z., Osinski P., Rutanski J.. Location and Attempts at Reduction Noise in Hydraulic Feeder. 14th Scientific Conference on Earth-Moving Machinery Development Problems, Zakopane 2001
[38] Kollek W., Kudzma Z., Osinski P., Rutanski J.. Application of Acoustic Holography to Location of Noise Source in Hydraulic Power Transmission Systems. 15th Scientific Conference on Earth-Moving Machinery Development Problems, Zakopane 2002
[39] E.G.Williams, B.H.Houston. Interior near-field acoustical holography in flight. J. Acoust.Sco.Am., 2000(4): 1451-1463
[40] M. A. Rowell, D. J. Oldham. Determination of the directivity of a planar noise source by menas of near field acoustical holography 1: Theoretical background. Journal of Sound and Vibration. 1995(1): 99-118
[41] M. A. Rowell, D. J. Oldham. Determination of the directivity of a planar noise source by menas of near field acoustical holography 2: Numerical Simulation. Journal of Sound and Vibration. 1995(1): 119-142
[42] Kypung-Uk Nam, Yang-Hann Kim. Visualization of multiple incoherent source by the backward prediction of near-field acoustic holography. J. Acoust. Sco. Am. 2001 (5): 1808-1816
[43] Hiroshi Takata, Takuo Nisihi, Hyungseok Kook. Visualization techniques to identify and quantity source and paths of exterior noise radiated from stationary and nonstationary vehicles. Seoul 2000 FISITA world automotive Congress
[44] 陈晓东．近场平面声全息的测量和重构误差研究．[博士学位论文]．合肥：合肥工业大学．2004
[45] 罗禹贡，杨殿阁，郑四发，毛晓群，李克强，连小珉．基于近场声全息理论的运动声源动态识别方法．声学学报．2004(3)：226-230
[46] 于飞，陈剑，周广林，陈心昭．噪声源识别的近场声全息方法和数值仿真分析．振动工程学报．2003(3)：339-343
[47] Hyu-Sang kwon, Yang-Hann Kim. Moving frame technique for planar acoustic Holography. J. Acoust.Sco.Am., 1998(4): 1734-1741
[48] Soon-Hong Park, Yang-Hann Kim. An improved moving frame acoustic holography for coherent bandlimited noise. J. Acoust.Sco.Am., 1998(6): 3179-3189
[49] Waclaw Kollek, Piotr Osinski. Assessment of Energetistic Measuring Techniques and Their Application to Diagnosis of Acoustic Condition of Hydraulic Machinery and Equipment. 2002 SAE International and NFPA: 262-274
[50] Hald J.. Non-stationary STSF. B & K Technical Review No.1. 2000:1-36
[51] Hald J.. Time Domain Acoustical Holography and its Application. Sound and Vibration, 2001
[52] 孔令来，肖云魁，蒋国平，王保民，张玲玲，夏天．发动机稳态与非稳态振动信号分析比较．内燃机学报．2006(1)：72-77
[53] 杨伸记，廖明夫，赵旭民．旋转机械状态监测与故障诊断系统．测控技术．2000(1)：56-58
[54] 黄筱调，王宛山．二次FFT分析法与齿轮故障边频识别．东北大学学报(自然科学版)．1999(2)：188-191
[55] Al-Balushi, K. R., Samanta, B. Gear Faults Diagnosis Using Energy-based Features of Acoustic Emission. Journal of Systems and Control Engineering. 2002, Vol.216: 249-263
[56] Alfayez, L., Mba, D., Dyson, G. The Application of Acoustic Emission for Detecting Incipient Cavitation and the Best Efficiency Point of a 60KW Centrifugal Pump: A Case Study. NDT and E International. 2005, Vol.38, No.5:354-358
[57] 李剑秋，欧阳明高．汽车发动机瞬时转速信号的小波分析．中国机械工程．1999(5)：552-555
[58] 周东华，叶银忠．现代故障诊断与容错控制．第1版．北京：清华大学出版社，2000．6
[59] 肖云魁．汽车故障诊断学．第1版．北京：北京理工大学出版社，2001．7
[60] 康海英，栾军英，张志斌，郑海起．基于时频和频谱分析的齿轮箱故障诊断．军械工程学院学报．2004(3)：10-13
[61] 杜设亮，张云，李剑，陈子辰．频谱分析法在齿轮故障诊断中的应用研究．机电工程．1999(5)：211-213
[62] 贺银芝，沈松，刘正士，应怀樵．基于小波变换的奇异性检测在发动机连杆轴承故障诊断中的应用．北京工业大学学报．2006(4)：72-76
[63] 张辉，王淑娟，张青森，翟国富．基于小波包变换的滚动轴承故障诊断方法的研究．振动与冲击．2004(4)：127-130
[64] 张梅军，陈江海．基于小波包分解的整循环征兆提取与故障识别．解放军理工大学学报(自然科学版)．2005(5)：487-490
[65] 何勇，李增芳．智能化故障诊断技术的研究与应用．浙江大学学报(农业与生命科学版)．2003(2)：119-124
[66] 王致杰，王耀才，李冬．现代大型设备故障智能诊断技术的现状与展望．煤矿机械．2003(7)：102-104
[67] 马波，高金吉，江志农，杨剑峰．旋转机械故障诊断专家系统．机电工程技术．2005(12)：30-32
[68] 吴明强，史慧，朱晓华，肖开清．故障诊断专家系统研究的现状与展望．计算机测量与控制．2005(12)：1301-1304
[69] 徐刚，连小珉，蒋孝煜．汽车变速器齿轮制造缺陷的声频特征提取．汽车工程．2000(1)：55-58
[70] 饶泓，扶名福，金宇华．模糊故障诊断隶属函数的选择分析比较．南昌大学学报(理科版)．2006(4)：383-385
[71] 戎璐，张维竞．工程系统模糊故障诊断现状．机电设备．1999(2)：31-33
[72] 马修真，陈志显，李文辉，丁彦闯，朱友文，张栋波．基于模糊理论的增压系统故障诊断．内燃机学报．2001(5)：473-476
[73] 何佳洲，周志华，陈兆乾．系统故障诊断的一种神经网络方法．计算机工程与应用．1999(6)：49-52
[74] Luo Dehan, Lan Wenhui, Huang Sheng. Development of an On-line Condition Monitoring System Based on Neural Networks. Journal of UEST of China. 1999(5): 527-531
[75] 刘世元，杜润生，杨叔子．利用神经网络诊断内燃机失火故障的研究．内燃机学报．1999(1)：67-70
[76] F. Jacobsen. An Overview on Sources of error in Sound Power Determination using the Intensity Technique. Applied Acoustics, vol. 50, no.2: 155-166, 1997
[77] International Standard ISO9614-1. Acoustics-Determination of sound power levels of noise sources using sound intensity-Part 1: Measurement at discrete points. First edition, 1993.6.1
[78] 赵松龄．声学测量技术20年．声学技术．2002(1-2)：52-54
[79] 雷振山．LabVIEW 7 Express实用技术教程．第1版．北京：中国铁道出版社，2004
[80] 张贤达，保铮．非平稳信号分析与处理．第1版．北京：国防工业出版社，1998
[81] 何正嘉．机械设备非平稳信号的故障诊断原理及应用．第1版．北京：高等教育出版社，2001
[82] 杨汝青．现代机械设计—系统与结构．第1版．上海：上海科学技术文献出版社，2000
[83] 厚国屏．LabVIEW7.1编程与虚拟仪器设计．第1版．北京：清华大学出版社，2005
[84] 张凯．LabVIEW虚拟仪器工程设计与开发．第1版．北京：国防工业出版社，2004
[85] 刘伟，朱国玺，喜多山．最优化方法在声强测量探查噪声源中的应用．2000(3)：32-34
[86] 刘惟信．机械最优化设计．第1版．北京：清华大学出版社，1994
[87] 丁玉美．数字信号处理．第1版．西安：西安电子科技大学出版社，2002
[88] P.Loughlin(Ed). Proceedings of IEEE. 1996,84(9)
[89] S.Qian, D.Chen. Joint Time—Fequency Aanlysis. IEEE Signal Processing Magazine. 1999,6(2):52-67
[90] A.B.Fineberg, K.L.Yu. Time—frequency representation based cepstral processing for speech recognition. ICCASP'96. 1996,1:25-28
[91] R.A.Baxter. SAR Image Compression with the Gabor Transform. IEEE Transactions on Geoscience and Remote Sensing. 1999,37(1):574-588
[92] H.Rotstein, S.Raz. Gabor Transform of Time—varying System: Exact Representation and Approximation. IEEE Transactions on Automatic Control. 1999,44(4):729-741
[93] V.Chen, H.Lin. Joint time—frequency analysis for radar signal and image processing. IEEE Signal Processing Magazine. 1999,16(2):81-93
[94] L.Stankovic, V.Katkovnik. Wigner distribution of noisy signals with adaptive time—frequency varying window. IEEE Transactions on Signal Processing. 1999,47(4): 1099-1108
[95] 钱人一．汽车发动机噪声控制．第1版．上海：同济大学出版社，1997
[96] 陈南．汽车振动与噪声控制．第1版．北京：人民交通出版社，2005
[97] 吴炎庭，袁卫平．内燃机噪声振动与控制．第1版．北京：机械工业出版社，2005．3
[98] 韩军，郝志勇．车用发动机前端辐射声源的小波分析与识别．汽车工程．2003(6)：557-560
[99] 杨福生．小波变换的工程分析与应用．第1版．北京：科学出版社，1999
[100] 胡昌华，张军波，夏军，张伟．基于MATLAB的系统分析与设计—小波分析．第1版．西安：西安电子科技大学出版社，1999．12
[101] S.G.Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[102] S．G．Mallat．信号处理的小波导引．第2版．北京：机械工业出版社，2002
[103] 刘明才．小波分析及其应用．第1版．北京：清华大学出版社，2005．9
[104] 刘守道．大型车用柴油机智能故障诊断技术及寿命预测方法的研究．[博士学位论文]．北京：石油大学，2001
[105] Balant A C, Maling G C, Yeager D M. Measurement of blower and fan noise using sound intensity techniques. Noise Control Engng. 1989, 33(2): 77-88
[2] 常思勤．汽车动力装置．第1版．北京：机械工业出版社，2006，2
[3] D.Mba, Raj B.K.N.Rao. Development of Acoustic Emission Technology for Condition Monitoring and Diagnosis of Rotating Machines: Bearings, Pumps, Gearboxes, Engines, and Rotating Structures. The Shock and Vibration Digest. 2006(1): 3-16
[4] Friis-Hausen A, Fog.T.L. Monitoring Exhaust Valve Leaks and Misfire in Marine Diesel Engines. In Proceedings of COMADEM 2001:641-648
[5] Morhain A, Mba D. Bearing Defect Diagnosis and Acoustic Emission. Journal of Engineering Tribology, I Mech E. 2003(4): 257-272
[6] Marc Marroquin. A Comparison of Seven Different Noise Identification Techniques. 2003 SAE Paper: 2141-2151
[7] 郭建新，左言言，宫镇，曾发林．厢式汽车车内噪声的试验研究．江苏理工大学学报(自然科学版)．1999(4)：28-31
[8] 梁兴雨，舒歌群．柴油机噪声源的识别研究．内燃机工程．2005(6)：67-71
[9] Hald J., Christensen J.J. A class of optimal broadband phased array geometries designed for easy construction. Proceeding of Inter-Noise 2002
[10] J.J. Christensen, J. Hald. Beamforming. B & K Technical Review. 2004(1): 1-54
[11] J. Hald. Combined NAH and Beamforming Using the Same Array. B & K Technical Review. 2005(1): 11-39
[12] 宋雷鸣，孙守光，邢优胜，张新华．基于相控阵列的噪声源识别及仿真研究．铁道学报．2005(6)：101-103
[13] 蒋孝煜，连小珉．声强技术及其在汽车工程中的应用．第1版．北京：清华大学出版社，2001．6
[14] M. J. Crocker, J. P. Arenas. Fundamentals of the Direct Measurement of Sound Intensity and Practical Applications. ACOUSTICAL PHYSICS. 2003(2): 163-175
[15] F.J. Fahy. Sound Intensity. Second Edition. London:E & FN Spon, 1995
[16] Finn Jacobsen. A comparison of two different sound intensity measurement principles. J. Acoust. Am, 2005(3): 1510-1517
[17] F. Jacobsen, H.-E. de Bree. Intensity-based sound power determination under adverse sound field conditions: p-p versus p-u probes. Proceedings of Twelfth International Congress on Sound and Vibration, 2005
[18] Chung J Y, Pope J. Practical measurement of acoustic intensity—the two microphone corss spectral method. Proceedings of inter—noise 78, 1978:893-900
[19] Fahy F J. Measurement of acoustic intensity using the cross spectral density of weo microphone signals. J. Acoust Soc.Amer., 1977,62(4): 1057-1059
[20] Alfredson R J. A new technique for noise source identification on a multieylinder automotive engine. Proceedings of Noise—Con77, 1977:307-318
[21] IEC Publication 61043, "Instruments for the measurement of sound intensity-Measurements with pairs of pressure sensing microphones," 1993
[22] American National Standard ANSI S1.9-1996, "Instruments for the measurement of sound intensity," 1996
[23] 陆加良，张卫东．用于测量声功率的声强频谱分析仪．仪器仪表学报．1994(3)：244-249
[24] 邓兆祥，李克强，何渝生．汽车噪声声强测量分析系统的开发及应用．汽车工程．1994(5)：283-289
[25] 梁杰，孙巍，程鹏．虚拟式声强分析系统在发动机噪声源识别中的应用．吉林大学学报(工学版)．2004(2)：237-240
[26] 郝志勇，韩军，毕凤荣．一种中型乘用汽车表面噪声的声强测试分析．汽车工程．2003(4)：392-394
[27] 韩军，郝志勇，刘月辉．柴油机表面噪声的声强测量与分析．汽车工程．2003(3)：272-274
[28] 邓兆祥，李景渊，褚志刚，许响林，胡成太．SC6360B车外加速噪声的控制．重庆大学学报(自然科学版)．2005(4)：5-9
[29] 吕兴才，黄震．柴油机噪声源的声强识别方法．农业机械学报．2004(5)：51-54
[30] 金岩，郝志勇．车用汽油机噪声源的声强测量识别．农业机械学报．2006(2)：14-17
[31] Marc Marroquin. New Innovation in Sound Intensity: Selective Intensity. SAE 2001
[32] 俞明，柳文斌．轿车车内振动噪声源的识别．机床与液压．2003(5)：117-119
[33] 罗玉涛，俞明，柳文斌，孙国斌．用选择性声强技术识别车内噪声及其成因．汽车技术．2002(9)：20-22
[34] Maynard J D, Williams E G, Lee Y. Nearfield acoustic holography Ⅰ: Theory of generalized holography and the development of NAH. J. A. S. A. 1985(4): 1395-1413
[35] Veronesi W A, Maynard J D. Nearfield acoustic holography(NAH) Ⅱ: Holographic reconstruction algorithms and computer implementation. J. A. S. A. 1987(5): 1307-1322
[36] J.J.Christensen, J.Hald, J.Morkholt, A.Schuhmacher, C.Blaabjerg. A Review of Array Techniques for Noise Source Location. 2003 SAE Paper: 2152-2160
[37] Kollek W., Kudzma Z., Osinski P., Rutanski J.. Location and Attempts at Reduction Noise in Hydraulic Feeder. 14th Scientific Conference on Earth-Moving Machinery Development Problems, Zakopane 2001
[38] Kollek W., Kudzma Z., Osinski P., Rutanski J.. Application of Acoustic Holography to Location of Noise Source in Hydraulic Power Transmission Systems. 15th Scientific Conference on Earth-Moving Machinery Development Problems, Zakopane 2002
[39] E.G.Williams, B.H.Houston. Interior near-field acoustical holography in flight. J. Acoust.Sco.Am., 2000(4): 1451-1463
[40] M. A. Rowell, D. J. Oldham. Determination of the directivity of a planar noise source by menas of near field acoustical holography 1: Theoretical background. Journal of Sound and Vibration. 1995(1): 99-118
[41] M. A. Rowell, D. J. Oldham. Determination of the directivity of a planar noise source by menas of near field acoustical holography 2: Numerical Simulation. Journal of Sound and Vibration. 1995(1): 119-142
[42] Kypung-Uk Nam, Yang-Hann Kim. Visualization of multiple incoherent source by the backward prediction of near-field acoustic holography. J. Acoust. Sco. Am. 2001 (5): 1808-1816
[43] Hiroshi Takata, Takuo Nisihi, Hyungseok Kook. Visualization techniques to identify and quantity source and paths of exterior noise radiated from stationary and nonstationary vehicles. Seoul 2000 FISITA world automotive Congress
[44] 陈晓东．近场平面声全息的测量和重构误差研究．[博士学位论文]．合肥：合肥工业大学．2004
[45] 罗禹贡，杨殿阁，郑四发，毛晓群，李克强，连小珉．基于近场声全息理论的运动声源动态识别方法．声学学报．2004(3)：226-230
[46] 于飞，陈剑，周广林，陈心昭．噪声源识别的近场声全息方法和数值仿真分析．振动工程学报．2003(3)：339-343
[47] Hyu-Sang kwon, Yang-Hann Kim. Moving frame technique for planar acoustic Holography. J. Acoust.Sco.Am., 1998(4): 1734-1741
[48] Soon-Hong Park, Yang-Hann Kim. An improved moving frame acoustic holography for coherent bandlimited noise. J. Acoust.Sco.Am., 1998(6): 3179-3189
[49] Waclaw Kollek, Piotr Osinski. Assessment of Energetistic Measuring Techniques and Their Application to Diagnosis of Acoustic Condition of Hydraulic Machinery and Equipment. 2002 SAE International and NFPA: 262-274
[50] Hald J.. Non-stationary STSF. B & K Technical Review No.1. 2000:1-36
[51] Hald J.. Time Domain Acoustical Holography and its Application. Sound and Vibration, 2001
[52] 孔令来，肖云魁，蒋国平，王保民，张玲玲，夏天．发动机稳态与非稳态振动信号分析比较．内燃机学报．2006(1)：72-77
[53] 杨伸记，廖明夫，赵旭民．旋转机械状态监测与故障诊断系统．测控技术．2000(1)：56-58
[54] 黄筱调，王宛山．二次FFT分析法与齿轮故障边频识别．东北大学学报(自然科学版)．1999(2)：188-191
[55] Al-Balushi, K. R., Samanta, B. Gear Faults Diagnosis Using Energy-based Features of Acoustic Emission. Journal of Systems and Control Engineering. 2002, Vol.216: 249-263
[56] Alfayez, L., Mba, D., Dyson, G. The Application of Acoustic Emission for Detecting Incipient Cavitation and the Best Efficiency Point of a 60KW Centrifugal Pump: A Case Study. NDT and E International. 2005, Vol.38, No.5:354-358
[57] 李剑秋，欧阳明高．汽车发动机瞬时转速信号的小波分析．中国机械工程．1999(5)：552-555
[58] 周东华，叶银忠．现代故障诊断与容错控制．第1版．北京：清华大学出版社，2000．6
[59] 肖云魁．汽车故障诊断学．第1版．北京：北京理工大学出版社，2001．7
[60] 康海英，栾军英，张志斌，郑海起．基于时频和频谱分析的齿轮箱故障诊断．军械工程学院学报．2004(3)：10-13
[61] 杜设亮，张云，李剑，陈子辰．频谱分析法在齿轮故障诊断中的应用研究．机电工程．1999(5)：211-213
[62] 贺银芝，沈松，刘正士，应怀樵．基于小波变换的奇异性检测在发动机连杆轴承故障诊断中的应用．北京工业大学学报．2006(4)：72-76
[63] 张辉，王淑娟，张青森，翟国富．基于小波包变换的滚动轴承故障诊断方法的研究．振动与冲击．2004(4)：127-130
[64] 张梅军，陈江海．基于小波包分解的整循环征兆提取与故障识别．解放军理工大学学报(自然科学版)．2005(5)：487-490
[65] 何勇，李增芳．智能化故障诊断技术的研究与应用．浙江大学学报(农业与生命科学版)．2003(2)：119-124
[66] 王致杰，王耀才，李冬．现代大型设备故障智能诊断技术的现状与展望．煤矿机械．2003(7)：102-104
[67] 马波，高金吉，江志农，杨剑峰．旋转机械故障诊断专家系统．机电工程技术．2005(12)：30-32
[68] 吴明强，史慧，朱晓华，肖开清．故障诊断专家系统研究的现状与展望．计算机测量与控制．2005(12)：1301-1304
[69] 徐刚，连小珉，蒋孝煜．汽车变速器齿轮制造缺陷的声频特征提取．汽车工程．2000(1)：55-58
[70] 饶泓，扶名福，金宇华．模糊故障诊断隶属函数的选择分析比较．南昌大学学报(理科版)．2006(4)：383-385
[71] 戎璐，张维竞．工程系统模糊故障诊断现状．机电设备．1999(2)：31-33
[72] 马修真，陈志显，李文辉，丁彦闯，朱友文，张栋波．基于模糊理论的增压系统故障诊断．内燃机学报．2001(5)：473-476
[73] 何佳洲，周志华，陈兆乾．系统故障诊断的一种神经网络方法．计算机工程与应用．1999(6)：49-52
[74] Luo Dehan, Lan Wenhui, Huang Sheng. Development of an On-line Condition Monitoring System Based on Neural Networks. Journal of UEST of China. 1999(5): 527-531
[75] 刘世元，杜润生，杨叔子．利用神经网络诊断内燃机失火故障的研究．内燃机学报．1999(1)：67-70
[76] F. Jacobsen. An Overview on Sources of error in Sound Power Determination using the Intensity Technique. Applied Acoustics, vol. 50, no.2: 155-166, 1997
[77] International Standard ISO9614-1. Acoustics-Determination of sound power levels of noise sources using sound intensity-Part 1: Measurement at discrete points. First edition, 1993.6.1
[78] 赵松龄．声学测量技术20年．声学技术．2002(1-2)：52-54
[79] 雷振山．LabVIEW 7 Express实用技术教程．第1版．北京：中国铁道出版社，2004
[80] 张贤达，保铮．非平稳信号分析与处理．第1版．北京：国防工业出版社，1998
[81] 何正嘉．机械设备非平稳信号的故障诊断原理及应用．第1版．北京：高等教育出版社，2001
[82] 杨汝青．现代机械设计—系统与结构．第1版．上海：上海科学技术文献出版社，2000
[83] 厚国屏．LabVIEW7.1编程与虚拟仪器设计．第1版．北京：清华大学出版社，2005
[84] 张凯．LabVIEW虚拟仪器工程设计与开发．第1版．北京：国防工业出版社，2004
[85] 刘伟，朱国玺，喜多山．最优化方法在声强测量探查噪声源中的应用．2000(3)：32-34
[86] 刘惟信．机械最优化设计．第1版．北京：清华大学出版社，1994
[87] 丁玉美．数字信号处理．第1版．西安：西安电子科技大学出版社，2002
[88] P.Loughlin(Ed). Proceedings of IEEE. 1996,84(9)
[89] S.Qian, D.Chen. Joint Time—Fequency Aanlysis. IEEE Signal Processing Magazine. 1999,6(2):52-67
[90] A.B.Fineberg, K.L.Yu. Time—frequency representation based cepstral processing for speech recognition. ICCASP'96. 1996,1:25-28
[91] R.A.Baxter. SAR Image Compression with the Gabor Transform. IEEE Transactions on Geoscience and Remote Sensing. 1999,37(1):574-588
[92] H.Rotstein, S.Raz. Gabor Transform of Time—varying System: Exact Representation and Approximation. IEEE Transactions on Automatic Control. 1999,44(4):729-741
[93] V.Chen, H.Lin. Joint time—frequency analysis for radar signal and image processing. IEEE Signal Processing Magazine. 1999,16(2):81-93
[94] L.Stankovic, V.Katkovnik. Wigner distribution of noisy signals with adaptive time—frequency varying window. IEEE Transactions on Signal Processing. 1999,47(4): 1099-1108
[95] 钱人一．汽车发动机噪声控制．第1版．上海：同济大学出版社，1997
[96] 陈南．汽车振动与噪声控制．第1版．北京：人民交通出版社，2005
[97] 吴炎庭，袁卫平．内燃机噪声振动与控制．第1版．北京：机械工业出版社，2005．3
[98] 韩军，郝志勇．车用发动机前端辐射声源的小波分析与识别．汽车工程．2003(6)：557-560
[99] 杨福生．小波变换的工程分析与应用．第1版．北京：科学出版社，1999
[100] 胡昌华，张军波，夏军，张伟．基于MATLAB的系统分析与设计—小波分析．第1版．西安：西安电子科技大学出版社，1999．12
[101] S.G.Mallat. A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674-693
[102] S．G．Mallat．信号处理的小波导引．第2版．北京：机械工业出版社，2002
[103] 刘明才．小波分析及其应用．第1版．北京：清华大学出版社，2005．9
[104] 刘守道．大型车用柴油机智能故障诊断技术及寿命预测方法的研究．[博士学位论文]．北京：石油大学，2001
[105] Balant A C, Maling G C, Yeager D M. Measurement of blower and fan noise using sound intensity techniques. Noise Control Engng. 1989, 33(2): 77-88