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

fYear

2009

fDate

20-24 July 2009

Firstpage

822

Lastpage

825

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/ICRMS.2009.5270075

Filename

5270075