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The diesel low temperature combustion (LTC) can keep high efficiency and produce low emission. Which has been widely studied at home and abroad in recent years. The combustion control parameters, such as injection pressure, injection timing, intake oxygen concentration, intake pressure, intake temperature and so on, have an important influence on the combustion and emission of diesel LTC. Therefore, to realize different combustion modes and combustion mode switch of diesel engine, it is necessary to accurately control the injection parameters and intake parameters of diesel engine. In this work, experimental study has been carried out to analyze the effect of intake oxygen concentration, intake pressure and intake temperature in combustion and emission characteristics of diesel LTC, such as in-cylinder pressure, temperature, heat release rate, NOx and soot emission.

Considering the randomness and instability of the oil pressure in the lubrication system, a new approach for fault detection and diagnosis of diesel engine lubrication system based on support vector machine optimized by particle swarm optimization (PSO-SVM) model and centroid location algorithm has been proposed. Firstly, PSO algorithm is chosen to determine the optimum parameters of SVM, to avoid the blindness of choosing parameters. It can improve the prediction accuracy of the model. The results show that the classify accuracy of PSO-SVM is improved compared with SVM in which parameters are set according to experience. Then, the support vector machine classification interface is fitted to a curve, and the boundary conditions of fault diagnosis are obtained. Finally, diagnose algorithm is achieved through analyzing the centroid movement of features. According to Performance degradation data, degenerate trajectory model is established based on centroid location.