Research on Diesel Engine Fault Diagnosis Based on Imbalanced Data and Ensemble Learning
Combining the ensemble learning theory with the deep confidence network,Adaboost-M2 al-gorithm was adopted as the ensemble learning generation method,and sparrow search algorithm was used to determine the deep confidence network models with different initial hyperparametric distribu-tions under different termination fitness.The above-mentioned different deep confidence network models were used as base classifiers for ensemble learning,and the integrated Ada-DBN model was obtained.The influence of the number of classifiers on the model diagnosis performance after ensem-ble learning was analyzed through experiments.The results show that the integrated Ada-DBN model can not only ensure the diagnosis ability under balanced data,but also improve the generalization abili-ty under unbalanced data,and it is an effective method for practical diesel engine fault diagnosis.
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