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　　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... 

【文章來源】：華中農(nóng)業(yè)大學湖北省 211工程院校 教育部直屬院校

【文章頁數(shù)】：107 頁

【學位級別】：博士

【文章目錄】：
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


【參考文獻】：
期刊論文
[1]多小波在振動信號降噪中的應(yīng)用[J]. 馬建倉,孟凡路.  計算機仿真. 2010(08)
[2]快速小波變換在非平穩(wěn)振動信號分析及實現(xiàn)[J]. 屈建社,陳勇,古康,黃鵬.  兵工自動化. 2010(07)
[3]小波分析在振動信號去噪中的應(yīng)用[J]. 胡俊文,周國榮.  機械工程與自動化. 2010(01)
[4]醇類添加劑改善HCCI發(fā)動機高負荷爆震試驗[J]. 何超,許金花,紀常偉,何洪.  農(nóng)業(yè)機械學報. 2008(03)
[5]基于小波分析的發(fā)動機氣缸失火故障診斷[J]. 蔣愛華,李小昱,王為,張軍.  農(nóng)業(yè)工程學報. 2007(04)



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