Association rules based on analysis of anomalies in a diesel engine assembly cold test
ZHAO Xuhui
XU Zhuo
WANG Hui
YAN Wei
LI Guoxiang
Abstract：To analyze the correlations among abnormal data in the cold test of a certain diesel engine assembly,an abnormal data analysis method for assembly cold test based on association rules is proposed.This method adopts the KIH-means clustering method to construct an association rule sample library for the diesel engine assembly data.Through the support-confidence test,the optimal combination of minimum support and minimum confidence thresholds is determined.The Apriori algorithm is used for association rule mining to reveal the correlation among abnormal data,and the mining results for the diesel engine assembly anomalies are obtained.The results show that the optimal threshold combination for the diesel engine is a minimum confidence of 0.75 and a minimum support of 0.225％,at which the change in the number of association rules is the most stables.The combination of KIH-means clustering method and Apriori algorithm can determine the association rules of abnormal parameters through data mining of the association rule sample library.On this basis,targeted optimization measures can be effectively proposed,thereby improving the engine assembly quality and the consistency of assembly performance.
Keywords：diesel enginecold testanomaly detectionassociation rule
Publication Date：2025-11-30
Online Publishing Date：2026-05-22(First online date of this platform, not the publication date of the document)
Pages：6( 65-70 )