Intelligent Fault Diagnosis Using Entropy-Based Rough Decision Tree Method

The fault diagnosis on large complex system is a difficult problem due to the complex structure of the system and the presence of high dimensional fault datasets. To solve this problem, integrating minimize entropy principle approach (META), rough sets theory and C4.5 algorithm, an entropy-based rough decision tree method is proposed to extract fault diagnosis rules. The diagnosis example of a 4153 diesel demonstrated that the solution can reduce the cost and raise the efficiency of diagnosis method, and verified the feasibility of engineering application.