Data analysis of cold test for diesel engine assembly based on large models
ZHAO Xuhui
XU Zhuo
WANG Xiangcheng
YAN Wei
LI Guoxiang
Abstract：To improve the assembly quality and cold test performance of diesel engines,based on the basic dataset of diesel engine assembly and cold testing,three standard datasets,namely Seeds,Wine,and Wdbc,from the University of California Irvine(UCI)machine learning repository are selected.The analysis effects of the support vector machines(SVM)model,the SVM model improved by combined intelligent algorithm,and the Transformer model on abnormal cold test data are compared.The results show that the classification accuracies of the SVM,improved SVM,and Transformer models for normal and abnormal data are 85.20％,92.54％,and 97.94％.Compared with the SVM and improved SVM models,the Transformer model has a significantly higher classification accuracy and can be used to analyze parameter anomalies.Exhaust pressure is closely related to torque,higher exhaust pressure leads to increased torque;an excessively long exhaust valve opening time results in abnormal intake vacuum.The effectiveness of the Transformer model in identifying engine assembly anomalies is verified.
Keywords：diesel engine assemblycold testanomaly detectionSVMTransformer model architecture
Publication Date：2026-02-28
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：8( 62-69 )