Title :
Fault Prediction of Ship Machinery Based on Gray Neural Network Model
Author :
Guang Yang ; Xiaoping Wu
Author_Institution :
Zhenjiang Watercraft Coll., Zhenjiang
fDate :
May 30 2007-June 1 2007
Abstract :
Aimed at the limitation of feedforward and feedback ANN, and the shortcoming that the diagnostic characteristic parameters are considered separately in conventional fault forecast method for machinery equipment, the multivariable gray model, MGM(l,n), and RBF network are introduced into fault prediction, which allows characteristic parameters to be described from the viewpoint of systems. It predicts future characteristic parameters considering the past and current machinery information, then RBF network is used to predict online. The fault prediction example indicates that the model has good prediction precision. It offers an effective method for reliable real-time fault diagnosis.
Keywords :
fault diagnosis; machinery; marine engineering; radial basis function networks; ships; RBF network; diagnostic characteristic parameters; fault forecast method; fault prediction; gray neural network model; machinery equipment; multivariable gray model; real-time fault diagnosis; ship machinery; Artificial neural networks; Employee welfare; Fault diagnosis; Feedforward neural networks; Machinery; Marine vehicles; Neural networks; Neurofeedback; Predictive models; Radial basis function networks; MGM(1,n) model; RBF neural network; fault prediction; machinery faults;
Conference_Titel :
Control and Automation, 2007. ICCA 2007. IEEE International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4244-0817-7
Electronic_ISBN :
978-1-4244-0818-4
DOI :
10.1109/ICCA.2007.4376521
