Maintenance decision method of a turbofan engine based on fault detection

In the maintenance process traditional excessive maintenance of turbofan engines may result in performance deterioration, long maintenance cycle and high maintenance cost. In order to solve this problem effectively, based on turbofan engine overhaul manual and maintenance technology, the fault detection process and repair mode were studied in depth, and the fault diagnosis expert system and fault diagnosis model were established based on BP(back propagation) neural network, verifying the reliability of fault diagnosis model with several sets of real performance data, with the diagnostic accuracy rate up to 95%. Secondly, the two kinds of diagnostic information were combined to develop reliable maintenance program and optimize the maintenance process. Then a maintenance decision method was put forward. By a certain type of turbofan real validation, it shows that this method can effectively eliminate the fault of high exhaust temperature, helping to improve maintenance quality and reduce maintenance cost of the engine.