Fault Diagnosis for the Oil System of Aviation Piston Engine Based on BP Neural Network

Abstract:

Introduced the composition and the principle of operation of the oil system of aviation piston engine. Analysed common faults of the oil system including high oil pressure indication,low oil pressure indication, high oil temperature indication and excessive oil consumption.Failure causes for above faults were analysed separately.Symbols were stood for failure modes and failure causes. Constructed the BP neural network.Symbols of failure modes were inputs of the BP neural network,and symbols of failure causes were outputs of the BP neural network.Builded a mapping relationship between failure modes and failure causes by training samples studying.Four training samples were selected based on common faults and fault effects.A given mode was as a input of the network,and by adjusting connection weights and the threshold of every neuron,an ideal result could be gotten.Then other mode was as a input of the network which carried on studying until the epochs was 369,and the mean squared error fast converged and the value of mean squared error was.The failure causes for the given failure mode can be confirmed by this BP neural network.By engineering verification, the BP neural network is applicable to fault diagnosis for oil system of aviation piston engine.

• Periodical: Advanced Materials Research (Volumes 1030-1032)
• Pages: 1185-1188
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1030-1032.1185
• Online since: September 2014
• Authors: Yan Feng*
• Keywords: Aviation Piston Engine, BP Neural Network (BPNN), Fault Diagnosis, Oil System

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