Development of Bayesian network model for engine cold test based on K2 algorithm
Engine cold test can reduce fuel consumption and emissions compared to traditional hot test methods.In order to improve the accuracy of fault diagnosis in cold test,a Bayesian network fault diagnosis model using K2 algorithm was proposed.Firstly,relevant cold test data from multiple diesel engines were selected to construct Bayesian network fault diagnosis models based on expert knowledge and K2 algorithm for comparison.The results showed that using K2 algorithm to construct Bayesian network was better than using expert knowledge to construct Bayesian network,and the proposed solution could optimize the current fault diagnosis model.
troubleshootingBayesian networksK2 algorithmcold test technologyengine
NETL
NSTL
万方数据
维普
