Diesel Engine Fault Diagnosis Method for Imbalanced Data

Due to the emphasis on the accuracy of overall classification,the machine learning method is ineffective in diesel engine fault diagnosis for imbalanced data. Therefore,in this study,a fault diagnosis method combining an improved synthetic minority oversampling technology(SMOTE)algorithm and machine learning technology was proposed. This method firstly improved the SMOTE algorithm. It employed the k-nearest neighbor algorithm to filter out noise samples from the majority class,thereby reducing overlap between various fault classes. Meanwhile,the k-means algorithm was used to determine the minority class sparsity and sampling weight,which reduced intraclass imbalance. Then,the improved SMOTE algorithm was used to balance the diesel engine fault data,and machine learning methods were used for the final fault diagnosis. Experimental results on a two-dimensional dataset indicate that the improved SMOTE algorithm can effectively alleviate the class overlap and intraclass imbalance problems in the original data. Diesel engine fault diagnosis experiments show that the fault samples generated by the improved SMOTE algorithm can optimally simulate the original fault samples,and the improved SMOTE algorithm can improve the accuracy of fault diagnosis methods. © 2024 Tianjin University. All rights reserved.