Dissertation > Industrial Technology > Energy and Power Engineering > Internal combustion engine > Diesel engine > Repair and maintenance

Fault Diagnosis of Diesel Engine Based on EMD Decomposition

Author WangChunTao
Tutor LuJinMing
School Jiangsu University of Science and Technology
Course Marine Engineering
Keywords Diesel engine Fault Diagnosis EMD Correlation dimension AR model Neural Networks Support Vector Machine
Type Master's thesis
Year 2010
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Diesel engine as a complex power machinery , widely used in ships, locomotives , cars and generator sets running state directly affects the security and reliability of the power system as a whole . The diesel engine will definitely vibrate reflect its performance of the work of the internal parts of the state information through certain pathways to the surface of the vibration signal , it can be used in diesel engine vibration signal disintegration fault diagnosis . Diesel engine fault diagnosis technology , made ??a more in-depth discussion of the cylinder head vibration signal based fault feature extraction and its diagnosis . The main content of this paper includes the following aspects : first , both from the theoretical and experimental analysis of the characteristics of the internal combustion engine cylinder head vibration signal reveals the characteristics of the vibration source , the diesel engine failure mechanism of the route of transmission , and to discuss the time domain and frequency domain characteristics of the diesel engine cylinder head vibration signal . Second, EMD decomposition of the cylinder head vibration signal with EMD , explore the decomposition of the physical meaning of each IMF component , try the IMF component HHT marginal spectrum analysis to find fault feature . Third , the use of time series analysis methods and fractal theory analysis of the vibration signal feature strike order IMF component of the AR model parameters and associated dimension , to verify the different conditions the intrinsic relationship between the amount of the state with the characteristics , which separating the independent features to reflect the failure of the feature information and fault information . Fourth , the substance of the conditions state recognition status classification problems , the AR model parameters and correlation dimension as the feature vectors imported into the neural network and support vector machine training , to identify the state of the diesel engine fault condition to verify the neural network the feasibility of the application in diesel engine fault diagnosis and support vector machine , in contrast to higher recognition rate of the support vector machine is more suitable for small sample analysis . By theoretical calculation and analysis , the diesel engine valve gap abnormal as well as the failure of the oil off vibration diagnostic mechanisms and diagnostic methods , certain conclusions and methods of engineering value , this important reference significance for diesel engine fault diagnosis .

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