Wear monitoring and fault diagnosis are important technical methods to ensure the safe and reliable operation of marine diesel engines.With the increasing requirements for the operational reliability of marine diesel engines,the wear monitoring needs to be analyzed more comprehensively.High-dimension data,irrelevant data and increasing redundant data multiply the complexity of fault diagnosis.In recent year,the demand for intelligent fault diagnosis of marine diesel engines is increasing day by day.Aimed at the above problems and requirements,based on the information entropy theory,the unsupervised attribute reduction algorithm for wear monitoring and fault diagnosis features of diesel engine was designed by combining information entropy and measurement entropy.With this algorithm,the dimension of feature index for the diesel engine lubrication wear fault diagnosis can be reduced from 16 to 7.By applying the BP neural network and identification rules of wear fault patterns,44 wear fault diagnosis samples of the diesel engine were verified and analyzed.The results show that the model can effectively reduce the dimension of the data set on the basis of ensuring the classification characteristics of the data set,and the accuracy of wear fault identification by the constructed BP neural network is significantly improved by taking the reduced data set as the research object.