Abstract
In this paper, an improved BP neural network algorithm is applied to the fault diagnosis of aircraft fuel system. The simulation results show that the algorithm has the characteristics of fast diagnosis speed and low misdiagnosis rate, and lays a foundation for the development of aircraft fuel fault diagnosis expert system based on neural network.
Highlights
As a popular fault diagnosis method, expert system diagnosis can effectively deal with many kinds of fault diagnosis problems that have appeared in the past
In the fault diagnosis of B737 aircraft fuel system, the following two important improvements are made in the training steps of BP neural network, and the network structure is optimized [7]: The input training data is x
According to the above fault diagnosis results, for the information samples that have appeared before, the expected results are in good agreement with the values output by the improved BP neural network, which fully verifies that the BP neural network can complete the fault diagnosis of B737 aircraft fuel system
Summary
As a popular fault diagnosis method, expert system diagnosis can effectively deal with many kinds of fault diagnosis problems that have appeared in the past. BP neural network originated in 1986 and was first proposed by McCelland and Rumelhart et al It belongs to a special multi-layer feedforward network trained by error back propagation algorithm. It can solve some complex problems and develop a series of links, such as speculation, Association and memory, so that the fault diagnosis rate can be improved. This method is very suitable for the fault diagnosis of modern large aircraft [2]. By improving and optimizing the typical BP neural network, the fault diagnosis rate of B737 aircraft fuel system can be improved and perfected
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