基于小波分析与粗糙集理论的发动机智能故障诊断研究
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摘要
发动机失火故障是一种常见的发动机故障,对失火故障的监测和诊断是发动机故障诊断系统的必要组成部分。本文主要研究基于小波分析理论、粗糙集理论的发动机失火故障智能诊断方法。
论文主要内容由六部分组成:(1)、发动机故障的振动诊断机理分析。介绍发动机振动的激振力和发动机振动的类型,建立每种振动类型的动力学模型,总结发动机缸盖振动的信息模型;(2)、发动机缸盖振动信号的滤波算法研究。首先介绍数学形态滤波器的理论基础,然后建立仿真信号,对经典形态滤波器进行仿真研究,在此基础上,提出基于数学形态滤波器的组合滤波方案,最后利用该滤波方案对发动机缸盖振动信号进行处理;(3)、发动机缸盖振动信号的特征提取算法研究。首先简要介绍小波分析理论,然后研究三种特征提取算法:小波域近似熵特征提取算法、基于图像处理的小波时频分析特征提取算法、小波包能量谱特征提取算法,最后将三种算法应用于发动机缸盖振动信号特征提取,得到可以用于故障模式识别的特征向量;(4)、基于粗糙集理论的故障特征约简算法研究。首先简要介绍粗糙集理论,然后给出粗糙集理论中的连续属性离散化方法和属性约简算法,最后提出发动机缸盖振动信号故障特征的约简算法,并计算得到根据小波包能量谱特征提取算法提取出的16个故障特征量的一个属性约简P = { E0 , E1 , E3};(5)、基于支持向量机的故障模式识别算法研究。首先简要介绍支持向量机的基本概念,然后提出基于支持向量机的发动机失火故障智能诊断算法,并通过实验数据验证该算法的正确性和有效性;(6)、介绍发动机状态监测与故障诊断系统的软硬件系统设计过程。在简要介绍虚拟仪器技术和NI LabVIEW语言的基础上,详细阐述大众2VQS电喷发动机失火故障智能诊断系统的软硬件系统设计过程,以及发动机实验台架状态监测与故障诊断系统的软硬件系统设计过程。
Engine misfire event is a main internal combustion engine fault. Monitoring and diagnosing engine misfires are indispensable functions of engine fault detection systems. The main research of this paper is to develop intelligent engine misfire detection methods based on wavelet analysis and rough set theory.
The main contents of this thesis consist of six parts as follows. (1) The mechanism of engine fault vibration diagnosis methods is analyzed. The excitation forces of the engine vibration are presented. The classification of the engine vibration is introduced and the dynamic models of every kinds of engine vibration are constructed. Finally, the information model of engine cylinder head vibration is presented. (2) The filtering algorithm of engine cylinder head vibration signal is studied. Firstly, the theoretical basis of mathematical morphological filters is introduced. Then, the classical morphological filters are studied using a simulation signal and an integrated filtering algorithm based on morphological filters is presented. Finally, the cylinder head vibration signals are processed based on this filtering solution. (3) Feature extraction algorithms of cylinder head vibration signal are investigated. Firstly, wavelet analysis theory is introduced. Then, three kinds of feature extraction algorithms are studied. The three algorithms are approximate entropy feature extraction algorithm in wavelet domain, wavelet time-frequency analysis feature extraction algorithm based on image processing and wavelet packet power spectrum feature extraction algorithm. Finally, apply the three algorithms to extract features of cylinder head vibration signal and feature vectors that can be used for fault pattern recognition are gained. (4) The fault vector reduction algorithm based on rough set theory is studied. Firstly, rough set theory is introduced. The discrete method of continuous attribute values and attributes reduction algorithms are investigated. Finally, attributes reduction algorithm of cylinder head vibration signal fault vectors is investigated and reductive attributes P = { E0 , E1 , E3} are gained from 16 feature values that are calculated according to the wavelet packet power spectrum feature extraction algorithm. (5) The fault pattern recognition algorithm based on support vector machines is studied. Firstly, the basic concepts of support vector machines are introduced. Then, intelligent engine misfire detection algorithm based on support vector machines is presented, and the experiment results show that this algorithm is correct and valid. (6) The design of hardware and software of the engine fault monitoring and diagnosing system is detailed. Firstly, the virtual instrument technology and NI LabVIEW programming language are introduced. Then, the design of hardware and software of the intelligent misfire detection system of VOLKSWAGEN 2VQS electronic controlled engines is presented. Finally, the design of hardware and software of monitoring and diagnosing system of engine test bench is presented.
论文主要内容由六部分组成:(1)、发动机故障的振动诊断机理分析。介绍发动机振动的激振力和发动机振动的类型,建立每种振动类型的动力学模型,总结发动机缸盖振动的信息模型;(2)、发动机缸盖振动信号的滤波算法研究。首先介绍数学形态滤波器的理论基础,然后建立仿真信号,对经典形态滤波器进行仿真研究,在此基础上,提出基于数学形态滤波器的组合滤波方案,最后利用该滤波方案对发动机缸盖振动信号进行处理;(3)、发动机缸盖振动信号的特征提取算法研究。首先简要介绍小波分析理论,然后研究三种特征提取算法:小波域近似熵特征提取算法、基于图像处理的小波时频分析特征提取算法、小波包能量谱特征提取算法,最后将三种算法应用于发动机缸盖振动信号特征提取,得到可以用于故障模式识别的特征向量;(4)、基于粗糙集理论的故障特征约简算法研究。首先简要介绍粗糙集理论,然后给出粗糙集理论中的连续属性离散化方法和属性约简算法,最后提出发动机缸盖振动信号故障特征的约简算法,并计算得到根据小波包能量谱特征提取算法提取出的16个故障特征量的一个属性约简P = { E0 , E1 , E3};(5)、基于支持向量机的故障模式识别算法研究。首先简要介绍支持向量机的基本概念,然后提出基于支持向量机的发动机失火故障智能诊断算法,并通过实验数据验证该算法的正确性和有效性;(6)、介绍发动机状态监测与故障诊断系统的软硬件系统设计过程。在简要介绍虚拟仪器技术和NI LabVIEW语言的基础上,详细阐述大众2VQS电喷发动机失火故障智能诊断系统的软硬件系统设计过程,以及发动机实验台架状态监测与故障诊断系统的软硬件系统设计过程。
Engine misfire event is a main internal combustion engine fault. Monitoring and diagnosing engine misfires are indispensable functions of engine fault detection systems. The main research of this paper is to develop intelligent engine misfire detection methods based on wavelet analysis and rough set theory.
The main contents of this thesis consist of six parts as follows. (1) The mechanism of engine fault vibration diagnosis methods is analyzed. The excitation forces of the engine vibration are presented. The classification of the engine vibration is introduced and the dynamic models of every kinds of engine vibration are constructed. Finally, the information model of engine cylinder head vibration is presented. (2) The filtering algorithm of engine cylinder head vibration signal is studied. Firstly, the theoretical basis of mathematical morphological filters is introduced. Then, the classical morphological filters are studied using a simulation signal and an integrated filtering algorithm based on morphological filters is presented. Finally, the cylinder head vibration signals are processed based on this filtering solution. (3) Feature extraction algorithms of cylinder head vibration signal are investigated. Firstly, wavelet analysis theory is introduced. Then, three kinds of feature extraction algorithms are studied. The three algorithms are approximate entropy feature extraction algorithm in wavelet domain, wavelet time-frequency analysis feature extraction algorithm based on image processing and wavelet packet power spectrum feature extraction algorithm. Finally, apply the three algorithms to extract features of cylinder head vibration signal and feature vectors that can be used for fault pattern recognition are gained. (4) The fault vector reduction algorithm based on rough set theory is studied. Firstly, rough set theory is introduced. The discrete method of continuous attribute values and attributes reduction algorithms are investigated. Finally, attributes reduction algorithm of cylinder head vibration signal fault vectors is investigated and reductive attributes P = { E0 , E1 , E3} are gained from 16 feature values that are calculated according to the wavelet packet power spectrum feature extraction algorithm. (5) The fault pattern recognition algorithm based on support vector machines is studied. Firstly, the basic concepts of support vector machines are introduced. Then, intelligent engine misfire detection algorithm based on support vector machines is presented, and the experiment results show that this algorithm is correct and valid. (6) The design of hardware and software of the engine fault monitoring and diagnosing system is detailed. Firstly, the virtual instrument technology and NI LabVIEW programming language are introduced. Then, the design of hardware and software of the intelligent misfire detection system of VOLKSWAGEN 2VQS electronic controlled engines is presented. Finally, the design of hardware and software of monitoring and diagnosing system of engine test bench is presented.
引文
[1]成曙,张振仁.发动机现代诊断技术[M].西安:西安交通大学出版社, 2006.12: 2-22.
[2]王道平,张义忠.故障智能诊断系统的理论与方法[M].北京:冶金出版社, 2001.05: 1-15.
[3]余成波,何怀波,石晓辉.内燃机振动控制及应用[M].北京:国防工业出版社, 1997.04: 1-227.
[4] Jinseo Park, Jaehyung Lee, Kwonhee Han, et al. A Study on Reducing the Computing Burden of Misfire Detection using a Conditional Monitoring Method [J]. SAE Paper, 2004-01-0722.
[5] Helge Nareid, Neil Lightowler. Detection of Engine Misfire Events Using an Artificial Neural Network [J]. SAE Paper, 2004-01-1363.
[6] Piotr Bogus, Jerzy Merkisz, Rafal Grzeszczyk, et al. Nonlinear Analysis of Combustion Engine Vibroacoustic Signals for Misfire Detection [J]. SAE Paper, 2003-01-0354.
[7] Martina Rohalova Ilkivova, Boris Rohal Ilkiv, Tomas Neuschl. Comparison of a Linear and Nonlinear Approach to Engine Misfires [J]. Control Engineering Practice, 2002, 10: 1141-1146.
[8] Piero Mario Azzoni, Davide Moro and Carlo Maria Porceddu, et al. Misfire detection in a high-performance engine by the principal component analysis approach[J]. SAE Paper, 960622.
[9] U. Kiencke. Engine misfire detection[J]. Control Engineering Practice, 1999(7): 203-208.
[10]刘世元,杜润生,杨叔子.利用转速波动信号诊断内燃机失火故障的研究(1)——诊断模型方法[J].内燃机学报, 2000, 18(3): 315-319.
[11] Nicolo Cavina, Enrico Corti, Giorgio Minelli. Misfire Detection Based on Engine Speed Time-Frequency Analysis [J]. SAE Paper, 2002-01-0480.
[12] Matteo Montani, Nicolo Speciale. Multiple Misfire Identification by a Wavelet-Based Analysis of Crankshaft Speed Fluctuation [C]. 2006 IEEE International Symposium on Signal Processing and Information Technology, 2006, 144-148.
[13] Fabrizio Ponti. Instantaneous Engine Speed Time-Frequency Analysis for Onboard Misfire Detection and Cylinder Isolation in a V12 High-Performance Engine [J]. Journal of Engineering for Gas Turbines and Power, 2008, 130: 0128051-0128059.
[14] J. Ben-Ari, G. deBotton, R. Itzhaki, et al. Fault Detection in Internal Combustion Engines by the Vibrations Analysis Method [J]. SAE Paper, 1999-01-1223.
[15] Jinseok Chang, Manshik Kim, Kyoungdoug Min. Detection of Misfire and Knock in Spark Ignition Engine by Wavelet Transform of Engine Block Vibration Signals [J]. Measurement
[47] Jing Wang, Guanghua Xu, Qing Zhang, et al. Application of Improved Morphological Filter to the Extraction of Impulsive Attenuation Signals [J]. Mechanical Systems and Signal Processing, 2008, 03(12).
[48]孙圣和,赵春晖.一种噪声污染的心电信号形态滤波方法[J].仪器仪表学报, 1998, 19(01): 76-79.
[49]王润秋,郑桂娟,付洪洲,等.地震资料处理中的形态滤波去噪方法[J].石油地球物理勘探, 2005, 40(3): 277-282.
[50]章立军,杨德斌,徐金梧,等.基于数学形态滤波的齿轮故障特征提取方法[J].机械工程学报, 2007, 43(2): 71-75.
[51]陈平,李庆民.基于数学形态学的数字滤波器设计与分析[J].中国电机工程学报, 2005, 25(11): 60-65.
[52]胡广书.现代信号处理教程[M].北京:清华大学出版社, 2004.11: 4-9, 239-370.
[53] Stephane Mallat. A Wavelet Tour of Signal Processing (Second Edition) [M]. Beijing: China Machine Press, 2003.09: 79-91, 220-313, 322-338.
[54] C. Sidney Burrus, Ramesh A. Gopinath and Haitao Guo. Introduction to Wavelets and Wavelet Transforms: A Primer [M]. Beijing: China Machine Press, 2005.04: 1-9, 50-65, 110-114.
[55] Hans-Georg Stark. Wavelets and Signal Processing [M]. Springer, 2005, 21-94.
[56]飞思科技产品研发中心.小波分析理论与MATLAB7实现[M].北京:电子工业出版社, 2005.03: 29-80, 109-116.
[57]冯光烁,杨海青,魏民祥.发动机转速时间序列分形特征分析[J].机械科学与技术, 2008.27(11): 1283-1286.
[58] Steve Pincus. Approximate Entropy (ApEn) as A Complexity Measure [J]. Chaos, 1995, 5(1): 110-117.
[59] Zhong Zhang, Eiji Tomota. A New Diagnostic Method of Knocking in a Spark-Ignition Engine Using the Wavelet Transform [J]. SAE Paper, 2000-01-1801.
[60] Cary Smith, Cajetan M. Akujuobi, Phil Hamory, et al. An Approach to Vibration Analysis Using Wavelets in an Application of Aircraft Health Monitoring [J]. Mechanical Systems and Signal Processing, 2007, 21: 1255-1272.
[61] B. P. Marchant. Time-frequency Analysis for Biosystems Engineering [J]. Biosystems Engineering, 2003, 85(3): 261-281.
[62] Chinmaya Kar, A. R. Mohanty. Vibration and Current Transient Monitoring for Gearbox Fault Detection Using Multiresolution Fourier Transform [J]. Journal of Sound and Vibration, 2008,311: 109-132.
[63]夏勇,赵红.小波分解及图像处理在内燃机振动诊断中的应用研究[J].振动与冲击, 2004, 23(02): 64-67.
[64]李智,陈祥初,张振仁,等.图像处理方法在柴油机振动故障诊断中的应用[J].振动、测试与诊断, 2002, 22(04): 300-305.
[65] Qiang Huang, Yongchang Liu, Huimeng Liu, et al. A New Vibration Diagnosis Method Based on the Neural Network and Wavelet Analysis [J]. SAE Paper, 2003-01-0363.
[66] Zdzislaw Pawlak. Rough Sets [J]. Communications of the ACM, 1995, 38(11): 90-95.
[67]胡寿松,何亚群.粗糙决策理论与应用[M].北京:北京航空航天大学出版社, 2006.04: 1-49.
[68]张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2001.07: 1-25.
[69]边肇祺,张学工.模式识别(第二版) [M].北京:清华大学出版社, 2000.01: 296-304.
[70] Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M]. Beijing: China Machine Press, 2005, 93-121.
[71]美国国家仪器(NI)有限公司. http://www.ni.com/
[72] National Instruments. Getting Started with LabVIEW. 2005.08.
[73] National Instruments. LabVIEW Fundamentals. 2006.08.
[2]王道平,张义忠.故障智能诊断系统的理论与方法[M].北京:冶金出版社, 2001.05: 1-15.
[3]余成波,何怀波,石晓辉.内燃机振动控制及应用[M].北京:国防工业出版社, 1997.04: 1-227.
[4] Jinseo Park, Jaehyung Lee, Kwonhee Han, et al. A Study on Reducing the Computing Burden of Misfire Detection using a Conditional Monitoring Method [J]. SAE Paper, 2004-01-0722.
[5] Helge Nareid, Neil Lightowler. Detection of Engine Misfire Events Using an Artificial Neural Network [J]. SAE Paper, 2004-01-1363.
[6] Piotr Bogus, Jerzy Merkisz, Rafal Grzeszczyk, et al. Nonlinear Analysis of Combustion Engine Vibroacoustic Signals for Misfire Detection [J]. SAE Paper, 2003-01-0354.
[7] Martina Rohalova Ilkivova, Boris Rohal Ilkiv, Tomas Neuschl. Comparison of a Linear and Nonlinear Approach to Engine Misfires [J]. Control Engineering Practice, 2002, 10: 1141-1146.
[8] Piero Mario Azzoni, Davide Moro and Carlo Maria Porceddu, et al. Misfire detection in a high-performance engine by the principal component analysis approach[J]. SAE Paper, 960622.
[9] U. Kiencke. Engine misfire detection[J]. Control Engineering Practice, 1999(7): 203-208.
[10]刘世元,杜润生,杨叔子.利用转速波动信号诊断内燃机失火故障的研究(1)——诊断模型方法[J].内燃机学报, 2000, 18(3): 315-319.
[11] Nicolo Cavina, Enrico Corti, Giorgio Minelli. Misfire Detection Based on Engine Speed Time-Frequency Analysis [J]. SAE Paper, 2002-01-0480.
[12] Matteo Montani, Nicolo Speciale. Multiple Misfire Identification by a Wavelet-Based Analysis of Crankshaft Speed Fluctuation [C]. 2006 IEEE International Symposium on Signal Processing and Information Technology, 2006, 144-148.
[13] Fabrizio Ponti. Instantaneous Engine Speed Time-Frequency Analysis for Onboard Misfire Detection and Cylinder Isolation in a V12 High-Performance Engine [J]. Journal of Engineering for Gas Turbines and Power, 2008, 130: 0128051-0128059.
[14] J. Ben-Ari, G. deBotton, R. Itzhaki, et al. Fault Detection in Internal Combustion Engines by the Vibrations Analysis Method [J]. SAE Paper, 1999-01-1223.
[15] Jinseok Chang, Manshik Kim, Kyoungdoug Min. Detection of Misfire and Knock in Spark Ignition Engine by Wavelet Transform of Engine Block Vibration Signals [J]. Measurement
[47] Jing Wang, Guanghua Xu, Qing Zhang, et al. Application of Improved Morphological Filter to the Extraction of Impulsive Attenuation Signals [J]. Mechanical Systems and Signal Processing, 2008, 03(12).
[48]孙圣和,赵春晖.一种噪声污染的心电信号形态滤波方法[J].仪器仪表学报, 1998, 19(01): 76-79.
[49]王润秋,郑桂娟,付洪洲,等.地震资料处理中的形态滤波去噪方法[J].石油地球物理勘探, 2005, 40(3): 277-282.
[50]章立军,杨德斌,徐金梧,等.基于数学形态滤波的齿轮故障特征提取方法[J].机械工程学报, 2007, 43(2): 71-75.
[51]陈平,李庆民.基于数学形态学的数字滤波器设计与分析[J].中国电机工程学报, 2005, 25(11): 60-65.
[52]胡广书.现代信号处理教程[M].北京:清华大学出版社, 2004.11: 4-9, 239-370.
[53] Stephane Mallat. A Wavelet Tour of Signal Processing (Second Edition) [M]. Beijing: China Machine Press, 2003.09: 79-91, 220-313, 322-338.
[54] C. Sidney Burrus, Ramesh A. Gopinath and Haitao Guo. Introduction to Wavelets and Wavelet Transforms: A Primer [M]. Beijing: China Machine Press, 2005.04: 1-9, 50-65, 110-114.
[55] Hans-Georg Stark. Wavelets and Signal Processing [M]. Springer, 2005, 21-94.
[56]飞思科技产品研发中心.小波分析理论与MATLAB7实现[M].北京:电子工业出版社, 2005.03: 29-80, 109-116.
[57]冯光烁,杨海青,魏民祥.发动机转速时间序列分形特征分析[J].机械科学与技术, 2008.27(11): 1283-1286.
[58] Steve Pincus. Approximate Entropy (ApEn) as A Complexity Measure [J]. Chaos, 1995, 5(1): 110-117.
[59] Zhong Zhang, Eiji Tomota. A New Diagnostic Method of Knocking in a Spark-Ignition Engine Using the Wavelet Transform [J]. SAE Paper, 2000-01-1801.
[60] Cary Smith, Cajetan M. Akujuobi, Phil Hamory, et al. An Approach to Vibration Analysis Using Wavelets in an Application of Aircraft Health Monitoring [J]. Mechanical Systems and Signal Processing, 2007, 21: 1255-1272.
[61] B. P. Marchant. Time-frequency Analysis for Biosystems Engineering [J]. Biosystems Engineering, 2003, 85(3): 261-281.
[62] Chinmaya Kar, A. R. Mohanty. Vibration and Current Transient Monitoring for Gearbox Fault Detection Using Multiresolution Fourier Transform [J]. Journal of Sound and Vibration, 2008,311: 109-132.
[63]夏勇,赵红.小波分解及图像处理在内燃机振动诊断中的应用研究[J].振动与冲击, 2004, 23(02): 64-67.
[64]李智,陈祥初,张振仁,等.图像处理方法在柴油机振动故障诊断中的应用[J].振动、测试与诊断, 2002, 22(04): 300-305.
[65] Qiang Huang, Yongchang Liu, Huimeng Liu, et al. A New Vibration Diagnosis Method Based on the Neural Network and Wavelet Analysis [J]. SAE Paper, 2003-01-0363.
[66] Zdzislaw Pawlak. Rough Sets [J]. Communications of the ACM, 1995, 38(11): 90-95.
[67]胡寿松,何亚群.粗糙决策理论与应用[M].北京:北京航空航天大学出版社, 2006.04: 1-49.
[68]张文修,吴伟志,梁吉业,等.粗糙集理论与方法[M].北京:科学出版社, 2001.07: 1-25.
[69]边肇祺,张学工.模式识别(第二版) [M].北京:清华大学出版社, 2000.01: 296-304.
[70] Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods[M]. Beijing: China Machine Press, 2005, 93-121.
[71]美国国家仪器(NI)有限公司. http://www.ni.com/
[72] National Instruments. Getting Started with LabVIEW. 2005.08.
[73] National Instruments. LabVIEW Fundamentals. 2006.08.
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