Title :
Engine Condition Monitoring Based on Grey AR Combination Model
Author :
Wang, Qiang ; Dai, Hui Sheng
Abstract :
Aiming at the problems of the wear condition monitoring, grey theory and auto-regressive combination forecasting model was put forward, and the combination model was build. The rough trend of the wear particle content change can be reflected through grey theory, and the detail of the change can be reflected through auto-regressive model. By testing and comparing a set of Ferro graphic data, the result shows that the combination model has a better forecasting result.
Keywords :
Abrasives; Condition monitoring; Engines; Graphics; Least squares approximation; Linear regression; Predictive models; Technology forecasting; Testing; Time series analysis; auto-regressive; condition monitoring; grey theory;
Conference_Titel :
Challenges in Environmental Science and Computer Engineering (CESCE), 2010 International Conference on
Conference_Location :
Wuhan, China
Print_ISBN :
978-0-7695-3972-0
Electronic_ISBN :
978-1-4244-5924-7
DOI :
10.1109/CESCE.2010.19