Title :
Application of multi-step time series prediction for industrial equipment prognostic
Author :
Asmai, Siti Azirah ; Abdullah, Rosmiza Wahida ; Soh, Mohd Norhisham Che ; Basari, Abd Samad Hasan ; Hussin, Burairah
Author_Institution :
Fac. of Inf. & Commun. Technol., Univ. Teknikal Malaysia Melaka, Durian Tunggal, Malaysia
Abstract :
The use of prognostics is critically to be implemented in industrial. This paper presents an application of multi-step time series prediction to support industrial equipment prognostic. An artificial neural network technique with sliding window is considered for the multi-step prediction which is able to predict the series of future equipment condition. The structure of prognostic application is presented. The feasibility of this prediction application was demonstrated by applying real condition monitoring data of industrial equipment.
Keywords :
condition monitoring; maintenance engineering; mechanical engineering computing; neural nets; time series; artificial neural network technique; condition monitoring data; industrial equipment prognostics; multistep time series prediction; Autoregressive processes; Biological neural networks; Degradation; Mathematical model; Neurons; Predictive models; Time series analysis; failure propability; multi-step prediction; neural network; prognostic; time series prediction;
Conference_Titel :
Open Systems (ICOS), 2011 IEEE Conference on
Conference_Location :
Langkawi
Print_ISBN :
978-1-61284-931-7
DOI :
10.1109/ICOS.2011.6079285
