In order to effectively identify the running state of the diesel engine, according to the characteristic information and identification characteristics of the diesel engine, a fault diagnosis method for diesel engine state identification based on kernel principal component analysis (KPCA) and support vector machine (SVM) is studied. First, the diesel engine is feature extracted to form a feature vector. Then, the kernel principal component analysis is performed on it to calculate the feature vector that can reflect the equipment state and effectively remove the redundancy of information. Finally, the obtained feature vector is trained and learned by the support vector machine to identify the state of the diesel engine. The feasibility and practicality of this method are verified through the identification analysis of the laboratory diesel engine combustion system under different operating states.
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