Title :
Fault diagnosis of marine main engine based on BP neural network
Author :
Jiang, Huiqing ; Jia, Suling ; Lai, Guanjun
Author_Institution :
Sch. of Econ. & Manage., Beihang Univ., Beijing, China
Abstract :
The fault diagnosis of a marine´s main engine is a significant but complicated problem, and artificial intelligence has been considered in this field for decades. This paper describes a fault diagnosis method for main engine based on BP neural network with the system fault classified into hierarchies according to fault tree analysis. Sample data of supercharger´s fault is collected and used to train and test the BP network. The simulation results illustrate the effectiveness of the proposed approach.
Keywords :
backpropagation; engines; fault diagnosis; fault trees; marine systems; mechanical engineering computing; neural nets; artificial intelligence; backpropagation; fault diagnosis; fault tree analysis; marine main engine; neural network; Artificial neural networks; Eigenvalues and eigenfunctions; Electronic mail; Engineering management; Engines; Fault diagnosis; Manufacturing automation; Neural networks; Newton method; Transfer functions; BP neural network; fault diagnosis; marine main engine;
Conference_Titel :
Reliability, Maintainability and Safety, 2009. ICRMS 2009. 8th International Conference on
Conference_Location :
Chengdu
Print_ISBN :
978-1-4244-4903-3
Electronic_ISBN :
978-1-4244-4905-7
DOI :
10.1109/ICRMS.2009.5270075
