Open AccessAfault Diagnosis Model of Marine Diesel Engine Lubrication System Based on Improvedextreme Learning Machine
Author(s) -
Gang Zhao,
Zhikun Liu,
Long Chen
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/4/042092
Subject(s) - lubrication , diesel engine , fault (geology) , particle swarm optimization , diesel fuel , computer science , automotive engineering , extreme learning machine , chaotic , artificial intelligence , engineering , mechanical engineering , algorithm , artificial neural network , geology , seismology
The lubrication system provides lubrication oil to various moving parts in the marine diesel engine. Once faults occurred in lubrication system, it can result in dramatically damage to the diesel engine. Development of fast and accurate fault diagnosis method of lubrication system is therefore highly urged. In this paper, we present a novel intelligent fault diagnosis methodbased on improved extreme learning machine (ELM). Firstly, we use chaotic mapping to enhance capability of the particle swarm optimization (PSO) algorithm; Then, an enhanced PSO algorithm is used to determine initial input weights (connecting input layer nodes and hidden layer nodes) and thresholds of ELM. Finally, we carry out fault diagnosis experiment on the marine diesel engine lubrication system. The experiments demonstrated that the proposed model could achieve more ideal performance.