Abstract:

Proposed in this paper is a fuzzy recognition method of engine fault modes based on the initial classifica-tion strategy with principal component entropy and oriented to energy data. In this method,first, the principal com-ponent data extraction method is used to reduce the highly- relevant multi- dimension data dimension.Next,the firstprincipal component data that maintain the maximum energy are clustered according to the number of possible classi-fications,and the optimal category number as well as the initial cluster centers is determined based on the kerneldensity estimation and the maximum entropy principle. Then,the best cluster center is produced via the fuzzy clus-tering of the principal component data only.Finally,the fault mode is recognized by calculating the maximum near-ness. Test results show that the proposed method effectively avoids the random selection of initial data due to theadoption of an independent initial classification algorithm,and that it is superior to the traditional algorithm due toits high classification accuracy,low computational overhead and excellent recognition performance.

Key words: fault recognition, principal component, dimension reduction, energy data, maximum entropy, fuzzyk- means