Research On Some Theories And Related Technologies About Fault Diagnosis Of Internal Combustion Engine

Internal combustion engine(ICE) is one of the main power machinery in industrial and agricultural production, which is widely applied in oil drilling, marine, automotive, railway, agriculture, engineering and construction, etal. Its operation has a driect impact on working status of the entire unit. Therefore, its condition monitoring and fault diagnosis, to ensure the system operating normally, in the best conditions and improve the quality and efficiency of the maintenance of equipment is necessary, and also have important economic significance.Fault diagnosis technology of ICE is an integrated technology based on a multidisciplinary basis, and the real-time and future technical status can be identified and forecasted quantitatively by analyzing and processing status information and historical conditions of ICE. The research regards ICE as the research object from the engineering application perspective, and vibation signal de-noising, faults feature extraction, pattern recognition, condition prediction, dynamic characteristics and reliability evaluation are systematically studied based on integrating some advanced theories and techniques such as the empirical mode decomposition, ant colony algorithm, support vector machine, chaos, fuzzy clustering, nonlinear vibration, random vibration and dynamic reliability. The main research works are as follows:(1) For the envelope fitting problem existing in empirical mode decomposition(EMD), a modified cubic spline interpolation algorithm was proposed. Simulation experiments showed that the method can solve the cubic spline interpolation algorithm easily resulting in the phenomena of overshoot and undershoot, and which can improve reasonableness and accuracy of EMD in the signal feature extraction by combined with extremum envelope extension method which can effectively inhibit end effect.(2) Parameters of support vector machine(SVM) will directly affect the classification and prediction accuracy, in order to avoid the traditional grid search parameters bringing the time consuming and determining research scope difficultly, an ant colony optimization was used to select parameters of SVM; and a novel algorithm "ant colony optimization support vector machine"(ACO-SVM) was put forward, and compared with grid search parameter optimization. Simulation experiments showed that ant colony optimization algorithm can search the main parameters of SVM faster and better than the grid search algorithm.(3) For the non-linear and non-stationary characteristics of fault vibration signals of ICE and the difficulty to obtain a large number of fault samples in practice, a fault diagnosis scheme for ICE was proposed by using SVM and chaos characteristics based on intrinsic mode function(IMF). Experimental results showed that compared with extracting the chaos characteristics directly from the original signal, the chaos characteristics of IMF are more able to effectively describe the non-linear and non-stationary characteristics of the original signals, and the practical difficulty to obtain a large typical number of fault samples is solved by combined with SVM.(4) For the non-stationary characteristics of fault vibration signals of ICE, while considering the fault status changing was often a gradual process for the same fault type, it was difficult to visually distinguish the changing degree of fault by the features. A new method was presented for the fault diagnosis of ICE based on the energy ratios of IMF and fuzzy clustering. Experimental results showed that taking the energy ratios of IMF as the characteristics for the fault diagnosis of ICE, which can reflect the underlying fault features in different bands, and identification problem of fuzzy fault type is solved by combined with improved fuzzy C-means clustering algorithm at the same time.(5) In order to solve the neural network prediction method easily falling into local minimum and the contradictions between the precision and generalization, SVM was applied into the ICE condition prediction; the problem that the required input matrix for building SVM prediction model was unreasonable can be solved based on phase-space reconstruction method; The EMD using the original signal de-noising can not only overcome the dependence on the wavelet basis in wavelet transform, and which can lay the foundation for accurately calculating embedding dimension and time-delay of vibration signals in the process of phase-space reconstruction. Experimental results showed that the method combines the advantages of the existing methods, and is able to accurately predict the trends of ICE vibration signals, the performance is superior to the traditional analysis methods, it has a certain reference significance for status early-warning of ICE. (6) A coupled bending-torsional nonlinear dynamic model for crankshaft system of ICE was established by considering mass eccentricity, varied inertia and nonlinear dry friction as well as nonlinear elasticity between piston and cylinder. The nonlinear dynamic characteristics of the shafting were simulated by numerical integration method and analytical method respectively, The results showed that the mass eccentricity varied inertia and nonlinear dry friction between piston and cylinder have some effects on the nonlinear vibration of the shafting. The conclusions provide a theoretical basis for improving the dynamic characteristics of the system and ensuring safe and stable operation of ICE.(7) For the complicated and random characteristics of the exciting torque on the shafting of ICE, the dynamic characteristics of the shafting subjected to the non-stationary random excitation were studied by the accurate and efficient pseudo-excitation method, and the dynamic reliability of the shafting subjected to random loads was analyzed based on first excursion failure criterion model. The simulation results showed that fire angle, intensity and transfer paths of excitations have effects on the dynamic reliability of the shafting, and the conclusions provide a qualitative reference for reducing shafting vibration and prolonging service life.