Diesel Engine Fault Diagnosis Using Wavelet Transforms Metho
发布时间：2021-04-17 15:28
Experiment presented in this research, used vibration data obtained from a four-stroke, a295diesel engine. Fault of the internal-combustion engine was detected by using the vibration signals of the cylinder head. The fault diagnosis system was designed and constructed for inspecting the status and fault diagnosis of a diesel engine based on wavelet analysis and LabVIEEW software.The cylinder-head vibration signals were captured through a piezoelectric acceleration sensor that was attached to a s...
【文章来源】：华中农业大学湖北省 211工程院校 教育部直属院校
【文章页数】：107 页
【学位级别】：博士
【文章目录】：
LIST OF CONTENT
LIST OF FIGURES
LIST OF TABLES
ABSTRACT
CHAPTER Ⅰ:INTRODUCTION
1.1 objectives and problems statement
1.1.1 Problems statement
1.1.2 Objectives of the study
CHAPTER Ⅱ:LITERATURE REVIEW
2.1 Diesel engine defect detection and monitoring methods
2.1.1 Vibration signal method
2.2 The vibration excitation sources of the diesel engine
2.2.1 Vibration response
2.2.2 Main sources of diesel engine noise
2.3 Time domain analysis
2.3.1 Feature extraction and selection from vibration signal
2.3.2 Time or statistical analysis
2.3.3 Standard deviation （SD）
2.3.4 Root mean square （RMS）
2.3.5 Peak level
2.3.6 Crest factor
2.3.7 Shape factor （SF）
2.3.8 Kurtosis
2.3.9 Skewness
CHAPTER Ⅲ:MATERIALS AND METHODS
3.1 Materials and hardware design of fault diagnosis system
3.1.1 Location of experiment
3.1.2 The test diesel engine of experimental study
3.1.3 The CW40 electric dynamometer
3.1.4 Charge amplifier YE5853A
3.1.5 NI-Data acquisition card PCI 6040 E
3.1.6 Shielded connection box （SCB-68）
3.1.7 Piezoelectric acceleration sensor-type CA-YD-106
3.1.8 Personal computer
3.1.9 Lab VIEW software and engine accelerated vibration signal acquisition system
3.1.10 The virtual instrument construction and operation
3.2 Selection method for signal processing
3.2.1 Introduction
3.2.2 Wavelet transform method
3.2.3 Continuous wavelet transforms
3.2.4 Multi-resolution analysis
3.3 Signal denoising
3.3.1 The threshold denoising method
3.3.2 Types of thresholding
3.4 Experimental settings and parameters selection
3.4.1 Setup of the experiment
3.4.2 Sensors installation on the diesel engine head
3.4.3 The selected sampling frequency and sampling points
3.4.4 Selection method for signal denoising
3.4.5 Selection of the optimum threshold level and mother wavelet #45 decomposition for the denoising process
3.4.6 Selection of mother wavelet and wavelet decomposition level for signal #50 analysis
3.5 Results and discussion
3.5.1 Wavelet analysis on cylinder head vibration signal
3.5.2 Characteristics of the signal energy and fault detection
3.5.3 Results of the analysis of time domain features extracted
CHAPTER Ⅳ:BACK PROPAGATION NEURAL NETWORK AND SUPPORT VECTOR MACHINE
4.1 Back propagation neural network and support vector machine
4.1.1 Back propagation neural network （BPNN）
4.1.2 Architecture of backward propagation neural network
4.2 Support vector machine and signal pattern recognition
4.2.1 Construction of SVM algorithm
4.3 Results and discussions
4.3.1 Design of the back-propagation （BP） network
4.3.2 Design of the support vector machine training
4.3.3 Features extracted using SVM and BPNN
CHAPTER Ⅴ:CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
5.2 Recommendations and future studies
ACKNOWLEDGMENTS
BIBLIOGRAPHY
APPENDIX A:Ⅵ, FRONT PANEL AND BLOCK DIAGRAM
【参考文献】：
期刊论文
[1]多小波在振动信号降噪中的应用[J]. 马建仓,孟凡路. 计算机仿真. 2010(08)
[2]快速小波变换在非平稳振动信号分析及实现[J]. 屈建社,陈勇,古康,黄鹏. 兵工自动化. 2010(07)
[3]小波分析在振动信号去噪中的应用[J]. 胡俊文,周国荣. 机械工程与自动化. 2010(01)
[4]醇类添加剂改善HCCI发动机高负荷爆震试验[J]. 何超,许金花,纪常伟,何洪. 农业机械学报. 2008(03)
[5]基于小波分析的发动机气缸失火故障诊断[J]. 蒋爱华,李小昱,王为,张军. 农业工程学报. 2007(04)
本文编号：3143691
【文章来源】：华中农业大学湖北省 211工程院校 教育部直属院校
【文章页数】：107 页
【学位级别】：博士
【文章目录】：
LIST OF CONTENT
LIST OF FIGURES
LIST OF TABLES
ABSTRACT
CHAPTER Ⅰ:INTRODUCTION
1.1 objectives and problems statement
1.1.1 Problems statement
1.1.2 Objectives of the study
CHAPTER Ⅱ:LITERATURE REVIEW
2.1 Diesel engine defect detection and monitoring methods
2.1.1 Vibration signal method
2.2 The vibration excitation sources of the diesel engine
2.2.1 Vibration response
2.2.2 Main sources of diesel engine noise
2.3 Time domain analysis
2.3.1 Feature extraction and selection from vibration signal
2.3.2 Time or statistical analysis
2.3.3 Standard deviation （SD）
2.3.4 Root mean square （RMS）
2.3.5 Peak level
2.3.6 Crest factor
2.3.7 Shape factor （SF）
2.3.8 Kurtosis
2.3.9 Skewness
CHAPTER Ⅲ:MATERIALS AND METHODS
3.1 Materials and hardware design of fault diagnosis system
3.1.1 Location of experiment
3.1.2 The test diesel engine of experimental study
3.1.3 The CW40 electric dynamometer
3.1.4 Charge amplifier YE5853A
3.1.5 NI-Data acquisition card PCI 6040 E
3.1.6 Shielded connection box （SCB-68）
3.1.7 Piezoelectric acceleration sensor-type CA-YD-106
3.1.8 Personal computer
3.1.9 Lab VIEW software and engine accelerated vibration signal acquisition system
3.1.10 The virtual instrument construction and operation
3.2 Selection method for signal processing
3.2.1 Introduction
3.2.2 Wavelet transform method
3.2.3 Continuous wavelet transforms
3.2.4 Multi-resolution analysis
3.3 Signal denoising
3.3.1 The threshold denoising method
3.3.2 Types of thresholding
3.4 Experimental settings and parameters selection
3.4.1 Setup of the experiment
3.4.2 Sensors installation on the diesel engine head
3.4.3 The selected sampling frequency and sampling points
3.4.4 Selection method for signal denoising
3.4.5 Selection of the optimum threshold level and mother wavelet #45 decomposition for the denoising process
3.4.6 Selection of mother wavelet and wavelet decomposition level for signal #50 analysis
3.5 Results and discussion
3.5.1 Wavelet analysis on cylinder head vibration signal
3.5.2 Characteristics of the signal energy and fault detection
3.5.3 Results of the analysis of time domain features extracted
CHAPTER Ⅳ:BACK PROPAGATION NEURAL NETWORK AND SUPPORT VECTOR MACHINE
4.1 Back propagation neural network and support vector machine
4.1.1 Back propagation neural network （BPNN）
4.1.2 Architecture of backward propagation neural network
4.2 Support vector machine and signal pattern recognition
4.2.1 Construction of SVM algorithm
4.3 Results and discussions
4.3.1 Design of the back-propagation （BP） network
4.3.2 Design of the support vector machine training
4.3.3 Features extracted using SVM and BPNN
CHAPTER Ⅴ:CONCLUSIONS AND RECOMMENDATIONS
5.1 Conclusions
5.2 Recommendations and future studies
ACKNOWLEDGMENTS
BIBLIOGRAPHY
APPENDIX A:Ⅵ, FRONT PANEL AND BLOCK DIAGRAM
【参考文献】：
期刊论文
[1]多小波在振动信号降噪中的应用[J]. 马建仓,孟凡路. 计算机仿真. 2010(08)
[2]快速小波变换在非平稳振动信号分析及实现[J]. 屈建社,陈勇,古康,黄鹏. 兵工自动化. 2010(07)
[3]小波分析在振动信号去噪中的应用[J]. 胡俊文,周国荣. 机械工程与自动化. 2010(01)
[4]醇类添加剂改善HCCI发动机高负荷爆震试验[J]. 何超,许金花,纪常伟,何洪. 农业机械学报. 2008(03)
[5]基于小波分析的发动机气缸失火故障诊断[J]. 蒋爱华,李小昱,王为,张军. 农业工程学报. 2007(04)
本文编号：3143691
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